1
|
Alharbi KK, Alvi SH, Ali B, Mirza J, Javed MA, Alharbi HA. Reliable relay assisted communications for IoT based fall detection. Sci Rep 2024; 14:6249. [PMID: 38491039 PMCID: PMC10942986 DOI: 10.1038/s41598-024-56124-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 02/29/2024] [Indexed: 03/18/2024] Open
Abstract
Robust wireless communication using relaying system and Non-Orthogonal Multiple Access (NOMA) will be extensively used for future IoT applications. In this paper, we consider a fall detection IoT application in which elderly patients are equipped with wearable motion sensors. Patient motion data is sent to fog data servers via a NOMA-based relaying system, thereby improving the communication reliability. We analyze the average signal-to-interference-plus-noise (SINR) performance of the NOMA-based relaying system, where the source node transmits two different symbols to the relay and destination node by employing superposition coding over Rayleigh fading channels. In the amplify-and-forward (AF) based relaying, the relay re-transmits the received signal after amplification, whereas, in the decode-and-forward (DF) based relaying, the relay only re-transmits the symbol having lower NOMA power coefficient. We derive closed-form average SINR expressions for AF and DF relaying systems using NOMA. The average SINR expressions for AF and DF relaying systems are derived in terms of computationally efficient functions, namely Tricomi confluent hypergeometric and Meijer's G functions. Through simulations, it is shown that the average SINR values computed using the derived analytical expressions are in excellent agreement with the simulation-based average SINR results.
Collapse
Affiliation(s)
- Khulud K Alharbi
- Department of Health Administration and Hospitals, College of Public Health and Health Informatics, Umm Al-Qura University, Makkah, Saudi Arabia.
| | - Sajid H Alvi
- Department of Physics, COMSATS University Islamabad, Islamabad, 45550, Pakistan
| | - Bakhtiar Ali
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, 45550, Pakistan
| | - Jawad Mirza
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, 45550, Pakistan
| | - Muhammad Awais Javed
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, 45550, Pakistan
| | - Hatem A Alharbi
- Department of Computer Engineering, College of Computer Science and Engineering, Taibah University, 42353, Madinah, Saudi Arabia
| |
Collapse
|
2
|
Dzeng RJ, Watanabe K, Hsueh HH, Fu CK. A GRU-Based Model for Detecting Common Accidents of Construction Workers. Sensors (Basel) 2024; 24:672. [PMID: 38276363 PMCID: PMC10818701 DOI: 10.3390/s24020672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/16/2024] [Accepted: 01/16/2024] [Indexed: 01/27/2024]
Abstract
Fall accidents in the construction industry have been studied over several decades and identified as a common hazard and the leading cause of fatalities. Inertial sensors have recently been used to detect accidents of workers in construction sites, such as falls or trips. IMU-based systems for detecting fall-related accidents have been developed and have yielded satisfactory accuracy in laboratory settings. Nevertheless, the existing systems fail to uphold consistent accuracy and produce a significant number of false alarms when deployed in real-world settings, primarily due to the intricate nature of the working environments and the behaviors of the workers. In this research, the authors redesign the aforementioned laboratory experiment to target situations that are prone to false alarms based on the feedback obtained from workers in real construction sites. In addition, a new algorithm based on recurrent neural networks was developed to reduce the frequencies of various types of false alarms. The proposed model outperforms the existing benchmark model (i.e., hierarchical threshold model) with higher sensitivities and fewer false alarms in detecting stumble (100% sensitivity vs. 40%) and fall (95% sensitivity vs. 65%) events. However, the model did not outperform the hierarchical model in detecting coma events in terms of sensitivity (70% vs. 100%), but it did generate fewer false alarms (5 false alarms vs. 13).
Collapse
Affiliation(s)
- Ren-Jye Dzeng
- Department of Civil Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan;
| | - Keisuke Watanabe
- Department of Marine Science and Ocean Engineering, School of Marine Science and Technology, Tokai University, Shizuoka 424–8610, Japan;
| | - Hsien-Hui Hsueh
- Department of Civil Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan;
| | - Chien-Kai Fu
- Department of Civil Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan;
| |
Collapse
|
3
|
Liang T, Liu R, Yang L, Lin Y, Shi CJR, Xu H. Fall Detection System Based on Point Cloud Enhancement Model for 24 GHz FMCW Radar. Sensors (Basel) 2024; 24:648. [PMID: 38276339 PMCID: PMC10820484 DOI: 10.3390/s24020648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/05/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024]
Abstract
Automatic fall detection plays a significant role in monitoring the health of senior citizens. In particular, millimeter-wave radar sensors are relevant for human pose recognition in an indoor environment due to their advantages of privacy protection, low hardware cost, and wide range of working conditions. However, low-quality point clouds from 4D radar diminish the reliability of fall detection. To improve the detection accuracy, conventional methods utilize more costly hardware. In this study, we propose a model that can provide high-quality three-dimensional point cloud images of the human body at a low cost. To improve the accuracy and effectiveness of fall detection, a system that extracts distribution features through small radar antenna arrays is developed. The proposed system achieved 99.1% and 98.9% accuracy on test datasets pertaining to new subjects and new environments, respectively.
Collapse
Affiliation(s)
- Tingxuan Liang
- State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 201203, China; (T.L.)
| | - Ruizhi Liu
- State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 201203, China; (T.L.)
| | - Lei Yang
- ICLegend Micro, Shanghai 201203, China
| | - Yue Lin
- ICLegend Micro, Shanghai 201203, China
| | - C.-J. Richard Shi
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195, USA;
| | - Hongtao Xu
- State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 201203, China; (T.L.)
| |
Collapse
|
4
|
Bouazizi M, Mora AL, Feghoul K, Ohtsuki T. Activity Detection in Indoor Environments Using Multiple 2D Lidars. Sensors (Basel) 2024; 24:626. [PMID: 38257717 DOI: 10.3390/s24020626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 01/15/2024] [Accepted: 01/15/2024] [Indexed: 01/24/2024]
Abstract
In health monitoring systems for the elderly, a crucial aspect is unobtrusively and continuously monitoring their activities to detect potentially hazardous incidents such as sudden falls as soon as they occur. However, the effectiveness of current non-contact sensor-based activity detection systems is limited by obstacles present in the environment. To overcome this limitation, a straightforward yet highly efficient approach involves utilizing multiple sensors that collaborate seamlessly. This paper proposes a method that leverages 2D Light Detection and Ranging (Lidar) technology for activity detection. Multiple 2D Lidars are positioned in an indoor environment with varying obstacles such as furniture, working cohesively to create a comprehensive representation of ongoing activities. The data from these Lidars is concatenated and transformed into a more interpretable format, resembling images. A convolutional Long Short-Term Memory (LSTM) Neural Network is then used to process these generated images to classify the activities. The proposed approach achieves high accuracy in three tasks: activity detection, fall detection, and unsteady gait detection. Specifically, it attains accuracies of 96.10%, 99.13%, and 93.13% for these tasks, respectively. This demonstrates the efficacy and promise of the method in effectively monitoring and identifying potentially hazardous events for the elderly through 2D Lidars, which are non-intrusive sensing technology.
Collapse
Affiliation(s)
- Mondher Bouazizi
- Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan
| | - Alejandro Lorite Mora
- Graduate School of Science and Technology, Keio University, Yokohama 223-8522, Japan
| | - Kevin Feghoul
- UMR-S1172-Lille Neuroscience and Cognition, Centre Hospitalier Universitaire Lille, Inserm, University of Lille, F-59000 Lille, France
| | - Tomoaki Ohtsuki
- Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan
| |
Collapse
|
5
|
Kan X, Zhu S, Zhang Y, Qian C. A Lightweight Human Fall Detection Network. Sensors (Basel) 2023; 23:9069. [PMID: 38005456 PMCID: PMC10674212 DOI: 10.3390/s23229069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 10/26/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023]
Abstract
The rising issue of an aging population has intensified the focus on the health concerns of the elderly. Among these concerns, falls have emerged as a predominant health threat for this demographic. The YOLOv5 family represents the forefront of techniques for human fall detection. However, this algorithm, although advanced, grapples with issues such as computational demands, challenges in hardware integration, and vulnerability to occlusions in the designated target group. To address these limitations, we introduce a pioneering lightweight approach named CGNS-YOLO for human fall detection. Our method incorporates both the GSConv module and the GDCN module to reconfigure the neck network of YOLOv5s. The objective behind this modification is to diminish the model size, curtail floating-point computations during feature channel fusion, and bolster feature extraction efficacy, thereby enhancing hardware adaptability. We also integrate a normalization-based attention module (NAM) into the framework, which concentrates on salient fall-related data and deemphasizes less pertinent information. This strategic refinement augments the algorithm's precision. By embedding the SCYLLA Intersection over Union (SIoU) loss function, our model benefits from faster convergence and heightened detection precision. We evaluated our model using the Multicam dataset and the Le2i Fall Detection dataset. Our findings indicate a 1.2% enhancement in detection accuracy compared with the conventional YOLOv5s framework. Notably, our model realized a 20.3% decrease in parameter tally and a 29.6% drop in floating-point operations. A comprehensive instance analysis and comparative assessments underscore the method's superiority and efficacy.
Collapse
Affiliation(s)
- Xi Kan
- School of the Internet of Things Engineering, Wuxi University, Wuxi 214105, China; (X.K.); (C.Q.)
| | - Shenghao Zhu
- School of Automation, Nanjing University of Information Science & Technology, Nanjing 211800, China;
| | - Yonghong Zhang
- School of the Internet of Things Engineering, Wuxi University, Wuxi 214105, China; (X.K.); (C.Q.)
- School of Automation, Nanjing University of Information Science & Technology, Nanjing 211800, China;
| | - Chengshan Qian
- School of the Internet of Things Engineering, Wuxi University, Wuxi 214105, China; (X.K.); (C.Q.)
- School of Automation, Nanjing University of Information Science & Technology, Nanjing 211800, China;
| |
Collapse
|
6
|
Abou L, Fliflet A, Presti P, Sosnoff JJ, Mahajan HP, Frechette ML, Rice LA. Fall detection from a manual wheelchair: preliminary findings based on accelerometers using machine learning techniques. Assist Technol 2023; 35:523-531. [PMID: 36749900 DOI: 10.1080/10400435.2023.2177775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/02/2023] [Indexed: 02/09/2023] Open
Abstract
Automated fall detection devices for individuals who use wheelchairs to minimize the consequences of falls are lacking. This study aimed to develop and train a fall detection algorithm to differentiate falls from wheelchair mobility activities using machine learning techniques. Thirty, healthy, ambulatory, young adults simulated falls from a wheelchair and performed other wheelchair-related mobility activities in a laboratory. Neural Network classifiers were used to train the algorithm developed based on data retrieved from accelerometers mounted at the participant's wrist, chest, and head. Results indicate excellent accuracy to differentiate between falls and wheelchair mobility activities. The sensors mounted at the wrist, chest, and head presented with an accuracy of 100%, 96.9%, and 94.8%, respectively, using data from 258 falls and 220 wheelchair mobility activities. This pilot study indicates that a fall detection algorithm developed in a laboratory setting based on fall accelerometer patterns can accurately differentiate wheelchair-related falls and wheelchair mobility activities. This algorithm should be integrated into a wrist-worn devices and tested among individuals who use a wheelchair in the community.
Collapse
Affiliation(s)
- Libak Abou
- Department of Physical Medicine & Rehabilitation, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Alexander Fliflet
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Peter Presti
- Interactive Media Technology Center, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Jacob J Sosnoff
- Department of Physical Therapy and Rehabilitation Science, School of Health Professions, University of Kansas Medical Center, Kansas City, Kansas USA
| | - Harshal P Mahajan
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Center for Health, Aging and Disability, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Mikaela L Frechette
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Laura A Rice
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Center for Health, Aging and Disability, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| |
Collapse
|
7
|
Brandstötter M, Lumetzberger J, Kampel M, Planinc R. Privacy by Design Solution for Robust Fall Detection. Stud Health Technol Inform 2023; 306:113-119. [PMID: 37638906 DOI: 10.3233/shti230604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
The majority of falls leading to death occur among the elderly population. The use of fall detection technology can help to ensure quick help for fall victims by automatically informing caretakers. Our fall detection method is based on depth data and has a high level of reliability in detecting falls while maintaining a low false alarm rate. The technology has been deployed in over 1,200 installations, indicating user acceptance and technological maturity. We follow a privacy by design approach by using range maps for the analysis instead of RGB images and process all the data in the sensor. The literature review shows that real-world fall detection evaluation is scarce, and if available, is conducted with a limited amount of participants. To our knowledge, our depth image based fall detection method has achieved the largest field evaluation up to date, with more than 100,000 events manually annotated and an evaluation on a dataset with 2.2 million events. We additionally present an 8-months study with more than 120,000 alarms analysed, provoked by 214 sensors located in 16 care facilities in Austria. We learned that on average 2.3 times more falls happen than are documented. Consequently, the system helps to detect falls that are otherwise overseen. The presented solution has the potential to make a significant impact in reducing the risk of accidental falls.
Collapse
Affiliation(s)
| | | | - Martin Kampel
- Vienna University of Technology, Computer Vision Lab, Austria
| | | |
Collapse
|
8
|
Wang S, Wu J. Patch-Transformer Network: A Wearable-Sensor-Based Fall Detection Method. Sensors (Basel) 2023; 23:6360. [PMID: 37514654 PMCID: PMC10384835 DOI: 10.3390/s23146360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 07/10/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023]
Abstract
Falls can easily cause major harm to the health of the elderly, and timely detection can avoid further injuries. To detect the occurrence of falls in time, we propose a new method called Patch-Transformer Network (PTN) wearable-sensor-based fall detection algorithm. The neural network includes a convolution layer, a Transformer encoding layer, and a linear classification layer. The convolution layer is used to extract local features and project them into feature matrices. After adding positional coding information, the global features of falls are learned through the multi-head self-attention mechanism in the Transformer encoding layer. Global average pooling (GAP) is used to strengthen the correlation between features and categories. The final classification results are provided by the linear layer. The accuracy of the model obtained on the public available datasets SisFall and UnMib SHAR is 99.86% and 99.14%, respectively. The network model has fewer parameters and lower complexity, with detection times of 0.004 s and 0.001 s on the two datasets. Therefore, our proposed method can timely and accurately detect the occurrence of falls, which is important for protecting the lives of the elderly.
Collapse
Affiliation(s)
- Shaobing Wang
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
| | - Jiang Wu
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
| |
Collapse
|
9
|
Inturi AR, Manikandan VM, Kumar MN, Wang S, Zhang Y. Synergistic Integration of Skeletal Kinematic Features for Vision-Based Fall Detection. Sensors (Basel) 2023; 23:6283. [PMID: 37514578 PMCID: PMC10385725 DOI: 10.3390/s23146283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 06/29/2023] [Accepted: 07/04/2023] [Indexed: 07/30/2023]
Abstract
According to the World Health Organisation, falling is a major health problem with potentially fatal implications. Each year, thousands of people die as a result of falls, with seniors making up 80% of these fatalities. The automatic detection of falls may reduce the severity of the consequences. Our study focuses on developing a vision-based fall detection system. Our work proposes a new feature descriptor that results in a new fall detection framework. The body geometry of the subject is analyzed and patterns that help to distinguish falls from non-fall activities are identified in our proposed method. An AlphaPose network is employed to identify 17 keypoints on the human skeleton. Thirteen keypoints are used in our study, and we compute two additional keypoints. These 15 keypoints are divided into five segments, each of which consists of a group of three non-collinear points. These five segments represent the left hand, right hand, left leg, right leg and craniocaudal section. A novel feature descriptor is generated by extracting the distances from the segmented parts, angles within the segmented parts and the angle of inclination for every segmented part. As a result, we may extract three features from each segment, giving us 15 features per frame that preserve spatial information. To capture temporal dynamics, the extracted spatial features are arranged in the temporal sequence. As a result, the feature descriptor in the proposed approach preserves the spatio-temporal dynamics. Thus, a feature descriptor of size [m×15] is formed where m is the number of frames. To recognize fall patterns, machine learning approaches such as decision trees, random forests, and gradient boost are applied to the feature descriptor. Our system was evaluated on the UPfall dataset, which is a benchmark dataset. It has shown very good performance compared to the state-of-the-art approaches.
Collapse
Affiliation(s)
- Anitha Rani Inturi
- Department of Computer Science and Engineering, SRM University—AP, Mangalagiri 522240, India; (A.R.I.); (M.N.K.)
| | - Vazhora Malayil Manikandan
- Department of Computer Science and Engineering, SRM University—AP, Mangalagiri 522240, India; (A.R.I.); (M.N.K.)
| | - Mahamkali Naveen Kumar
- Department of Computer Science and Engineering, SRM University—AP, Mangalagiri 522240, India; (A.R.I.); (M.N.K.)
| | - Shuihua Wang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK;
| | - Yudong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK;
| |
Collapse
|
10
|
Muaaz M, Waqar S, Pätzold M. Orientation-Independent Human Activity Recognition Using Complementary Radio Frequency Sensing. Sensors (Basel) 2023; 23:5810. [PMID: 37447660 DOI: 10.3390/s23135810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/12/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023]
Abstract
RF sensing offers an unobtrusive, user-friendly, and privacy-preserving method for detecting accidental falls and recognizing human activities. Contemporary RF-based HAR systems generally employ a single monostatic radar to recognize human activities. However, a single monostatic radar cannot detect the motion of a target, e.g., a moving person, orthogonal to the boresight axis of the radar. Owing to this inherent physical limitation, a single monostatic radar fails to efficiently recognize orientation-independent human activities. In this work, we present a complementary RF sensing approach that overcomes the limitation of existing single monostatic radar-based HAR systems to robustly recognize orientation-independent human activities and falls. Our approach used a distributed mmWave MIMO radar system that was set up as two separate monostatic radars placed orthogonal to each other in an indoor environment. These two radars illuminated the moving person from two different aspect angles and consequently produced two time-variant micro-Doppler signatures. We first computed the mean Doppler shifts (MDSs) from the micro-Doppler signatures and then extracted statistical and time- and frequency-domain features. We adopted feature-level fusion techniques to fuse the extracted features and a support vector machine to classify orientation-independent human activities. To evaluate our approach, we used an orientation-independent human activity dataset, which was collected from six volunteers. The dataset consisted of more than 1350 activity trials of five different activities that were performed in different orientations. The proposed complementary RF sensing approach achieved an overall classification accuracy ranging from 98.31 to 98.54%. It overcame the inherent limitations of a conventional single monostatic radar-based HAR and outperformed it by 6%.
Collapse
Affiliation(s)
- Muhammad Muaaz
- Faculty of Engineering and Science, University of Agder, 4898 Grimstad, Norway
| | - Sahil Waqar
- Faculty of Engineering and Science, University of Agder, 4898 Grimstad, Norway
| | - Matthias Pätzold
- Faculty of Engineering and Science, University of Agder, 4898 Grimstad, Norway
| |
Collapse
|
11
|
Jiang X, Zhang L, Li L. Multi-Task Learning Radar Transformer (MLRT): A Personal Identification and Fall Detection Network Based on IR-UWB Radar. Sensors (Basel) 2023; 23:5632. [PMID: 37420798 DOI: 10.3390/s23125632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/08/2023] [Accepted: 06/13/2023] [Indexed: 07/09/2023]
Abstract
Radar-based personal identification and fall detection have received considerable attention in smart healthcare scenarios. Deep learning algorithms have been introduced to improve the performance of non-contact radar sensing applications. However, the original Transformer network is not suitable for multi-task radar-based applications to effectively extract temporal features from time-series radar signals. This article proposes the Multi-task Learning Radar Transformer (MLRT): a personal Identification and fall detection network based on IR-UWB radar. The proposed MLRT utilizes the attention mechanism of Transformer as its core to automatically extract features for personal identification and fall detection from radar time-series signals. Multi-task learning is applied to exploit the correlation between the personal identification task and the fall detection task, enhancing the performance of discrimination for both tasks. In order to suppress the impact of noise and interference, a signal processing approach is employed including DC removal and bandpass filtering, followed by clutter suppression using a RA method and Kalman filter-based trajectory estimation. An indoor radar signal dataset is generated with 11 persons under one IR-UWB radar, and the performance of MLRT is evaluated using this dataset. The measurement results show that the accuracy of MLRT improves by 8.5% and 3.6% for personal identification and fall detection, respectively, compared to state-of-the-art algorithms. The indoor radar signal dataset and the proposed MLRT source code are publicly available.
Collapse
Affiliation(s)
- Xikang Jiang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Lin Zhang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Lei Li
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
| |
Collapse
|
12
|
Wang J, Sun Y, Chen Z, Jin Y, Xu Y. [Design and Research of Wearable Fall Protection Device for the Elderly]. Zhongguo Yi Liao Qi Xie Za Zhi 2023; 47:278-283. [PMID: 37288628 DOI: 10.3969/j.issn.1671-7104.2023.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
A protective device was designed that can be worn on the elderly, which consists of protective airbag, control box and protective mechanism. The combined acceleration, combined angular velocity and human posture angle are selected as the parameters to determine the fall, and the threshold algorithm and SVM algorithm are used to detect the fall. The protective mechanism is an inflatable device based on CO2 compressed air cylinder, and the equal-width cam structure is applied to its transmission part to improve the puncture efficiency of the compressed gas cylinder. A fall experiment was designed to obtain the combined acceleration and angular velocity eigenvalues of fall actions (forward fall, backward fall and lateral fall) and daily activities (sitting-standing, walking, jogging and walking up and down stairs), showing that the specificity and sensitivity of the protection module reached 92.1% and 84.4% respectively, which verified the feasibility of the fall protection device.
Collapse
Affiliation(s)
- Jie Wang
- Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030
| | - Yeke Sun
- Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030
| | - Zhenglong Chen
- Medical Instrumentation College, Shanghai University of Medicine & Health Sciences, Shanghai, 201318
| | - Yongchun Jin
- Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030
| | - Yunhua Xu
- Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030
| |
Collapse
|
13
|
Mohammad Z, Anwary AR, Mridha MF, Shovon MSH, Vassallo M. An Enhanced Ensemble Deep Neural Network Approach for Elderly Fall Detection System Based on Wearable Sensors. Sensors (Basel) 2023; 23:4774. [PMID: 37430686 DOI: 10.3390/s23104774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 04/27/2023] [Accepted: 05/12/2023] [Indexed: 07/12/2023]
Abstract
Fatal injuries and hospitalizations caused by accidental falls are significant problems among the elderly. Detecting falls in real-time is challenging, as many falls occur in a short period. Developing an automated monitoring system that can predict falls before they happen, provide safeguards during the fall, and issue remote notifications after the fall is essential to improving the level of care for the elderly. This study proposed a concept for a wearable monitoring framework that aims to anticipate falls during their beginning and descent, activating a safety mechanism to minimize fall-related injuries and issuing a remote notification after the body impacts the ground. However, the demonstration of this concept in the study involved the offline analysis of an ensemble deep neural network architecture based on a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) and existing data. It is important to note that this study did not involve the implementation of hardware or other elements beyond the developed algorithm. The proposed approach utilized CNN for robust feature extraction from accelerometer and gyroscope data and RNN to model the temporal dynamics of the falling process. A distinct class-based ensemble architecture was developed, where each ensemble model identified a specific class. The proposed approach was evaluated on the annotated SisFall dataset and achieved a mean accuracy of 95%, 96%, and 98% for Non-Fall, Pre-Fall, and Fall detection events, respectively, outperforming state-of-the-art fall detection methods. The overall evaluation demonstrated the effectiveness of the developed deep learning architecture. This wearable monitoring system will prevent injuries and improve the quality of life of elderly individuals.
Collapse
Affiliation(s)
- Zabir Mohammad
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh
| | - Arif Reza Anwary
- School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
| | - Muhammad Firoz Mridha
- Department of Computer Science, American International University-Bangladesh (AIUB), Dhaka 1229, Bangladesh
| | - Md Sakib Hossain Shovon
- Department of Computer Science, American International University-Bangladesh (AIUB), Dhaka 1229, Bangladesh
| | | |
Collapse
|
14
|
Costanzo A, Augello E, Battistini G, Benassi F, Masotti D, Paolini G. Microwave Devices for Wearable Sensors and IoT. Sensors (Basel) 2023; 23:s23094356. [PMID: 37177569 PMCID: PMC10181738 DOI: 10.3390/s23094356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 04/21/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023]
Abstract
The Internet of Things (IoT) paradigm is currently highly demanded in multiple scenarios and in particular plays an important role in solving medical-related challenges. RF and microwave technologies, coupled with wireless energy transfer, are interesting candidates because of their inherent contactless spectrometric capabilities and for the wireless transmission of sensing data. This article reviews some recent achievements in the field of wearable sensors, highlighting the benefits that these solutions introduce in operative contexts, such as indoor localization and microwave sensing. Wireless power transfer is an essential requirement to be fulfilled to allow these sensors to be not only wearable but also compact and lightweight while avoiding bulky batteries. Flexible materials and 3D printing polymers, as well as daily garments, are widely exploited within the presented solutions, allowing comfort and wearability without renouncing the robustness and reliability of the built-in wearable sensor.
Collapse
Affiliation(s)
- Alessandra Costanzo
- Department of Electrical, Electronic, and Information Engineering (DEI) "G. Marconi", Alma Mater Studiorum-University of Bologna, 40136 Bologna, Italy
| | - Elisa Augello
- Department of Electrical, Electronic, and Information Engineering (DEI) "G. Marconi", Alma Mater Studiorum-University of Bologna, 40136 Bologna, Italy
| | - Giulia Battistini
- Department of Electrical, Electronic, and Information Engineering (DEI) "G. Marconi", Alma Mater Studiorum-University of Bologna, 40136 Bologna, Italy
| | - Francesca Benassi
- Department of Electrical, Electronic, and Information Engineering (DEI) "G. Marconi", Alma Mater Studiorum-University of Bologna, 40136 Bologna, Italy
| | - Diego Masotti
- Department of Electrical, Electronic, and Information Engineering (DEI) "G. Marconi", Alma Mater Studiorum-University of Bologna, 40136 Bologna, Italy
| | - Giacomo Paolini
- Department of Electrical, Electronic, and Information Engineering (DEI) "G. Marconi", Alma Mater Studiorum-University of Bologna, 40136 Bologna, Italy
| |
Collapse
|
15
|
Othmen F, Baklouti M, Lazzaretti AE, Hamdi M. Energy-Aware IoT-Based Method for a Hybrid On-Wrist Fall Detection System Using a Supervised Dictionary Learning Technique. Sensors (Basel) 2023; 23:3567. [PMID: 37050627 PMCID: PMC10099041 DOI: 10.3390/s23073567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 01/31/2023] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
In recent decades, falls have posed multiple critical health issues, especially for the older population, with their emerging growth. Recent research has shown that a wrist-based fall detection system offers an accessory-like comfortable solution for Internet of Things (IoT)-based monitoring. Nevertheless, an autonomous device for anywhere-anytime may present an energy consumption concern. Hence, this paper proposes a novel energy-aware IoT-based architecture for Message Queuing Telemetry Transport (MQTT)-based gateway-less monitoring for wearable fall detection. Accordingly, a hybrid double prediction technique based on Supervised Dictionary Learning was implemented to reinforce the detection efficiency of our previous works. A controlled dataset was collected for training (offline), while a real set of measurements of the proposed system was used for validation (online). It achieved a noteworthy offline and online detection performance of 99.8% and 91%, respectively, overpassing most of the related works using only an accelerometer. In the worst case, the system showed a battery consumption optimization by a minimum of 27.32 working hours, significantly higher than other research prototypes. The approach presented here proves to be promising for real applications, which require a reliable and long-term anywhere-anytime solution.
Collapse
Affiliation(s)
- Farah Othmen
- Tunisia Polytechnic School, University of Carthage, La Marsa, Tunis 2078, Tunisia
- CES Lab, University of Sfax, Sfax 3029, Tunisia;
| | | | - André Eugenio Lazzaretti
- Graduate Program in Electrical and Computer Engineering, Federal University of Technology (UTFPR), Curitiba 80230-901, Paraná, Brazil;
| | - Monia Hamdi
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| |
Collapse
|
16
|
Guo R, Li H, Han D, Liu R. Feasibility Analysis of Using Channel State Information (CSI) Acquired from Wi-Fi Routers for Construction Worker Fall Detection. Int J Environ Res Public Health 2023; 20:4998. [PMID: 36981907 PMCID: PMC10049159 DOI: 10.3390/ijerph20064998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/08/2023] [Accepted: 03/10/2023] [Indexed: 06/18/2023]
Abstract
Accidental falls represent a major cause of fatal injuries for construction workers. Failure to seek medical attention after a fall can significantly increase the risk of death for construction workers. Wearable sensors, computer vision, and manual techniques are common modalities for detecting worker falls in the literature. However, they are severely constrained by issues such as cost, lighting, background, clutter, and privacy. To address the problems associated with the existing proposed methods, a new method has been conceived to identify construction worker falls by analyzing the CSI signals extracted from commercial Wi-Fi routers. In this research context, our study aimed to investigate the potential of using Channel State Information (CSI) to identify falls among construction workers. To achieve the aim of this study, CSI data corresponding to 360 sets of activities were collected from six construction workers on real construction sites. The results indicate that (1) the behavior of construction workers is highly correlated with the magnitude of CSI, even in real construction sites, and (2) the CSI-based method for identifying construction worker falls has an accuracy of 99% and can also accurately distinguish between falls and fall-like actions. The present study makes a significant contribution to the field by demonstrating the feasibility of utilizing low-cost Wi-Fi routers for the continuous monitoring of fall incidents among construction workers. To the best of our knowledge, this is the first investigation to address the issue of fall detection using commercial Wi-Fi devices in real-world construction environments. Considering the dynamic nature of construction sites, the new method developed in this study helps to detect falls at construction sites automatically and helps injured construction workers to seek medical attention on time.
Collapse
Affiliation(s)
- Runhao Guo
- Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China
| | - Heng Li
- Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China
| | - Dongliang Han
- Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China
| | - Runze Liu
- Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hong Kong, China
| |
Collapse
|
17
|
Rahemtulla Z, Turner A, Oliveira C, Kaner J, Dias T, Hughes-Riley T. The Design and Engineering of a Fall and Near- Fall Detection Electronic Textile. Materials (Basel) 2023; 16:1920. [PMID: 36903036 PMCID: PMC10004402 DOI: 10.3390/ma16051920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
Falls can be detrimental to the quality of life of older people, and therefore the ability to detect falls is beneficial, especially if the person is living alone and has injured themselves. In addition, detecting near falls (when a person is imbalanced or stumbles) has the potential to prevent a fall from occurring. This work focused on the design and engineering of a wearable electronic textile device to monitor falls and near-falls and used a machine learning algorithm to assist in the interpretation of the data. A key driver behind the study was to create a comfortable device that people would be willing to wear. A pair of over-socks incorporating a single motion sensing electronic yarn each were designed. The over-socks were used in a trial involving 13 participants. The participants performed three types of activities of daily living (ADLs), three types of falls onto a crash mat, and one type of near-fall. The trail data was visually analyzed for patterns, and a machine learning algorithm was used to classify the data. The developed over-socks combined with the use of a bidirectional long short-term memory (Bi-LSTM) network have been shown to be able to differentiate between three different ADLs and three different falls with an accuracy of 85.7%, ADLs and falls with an accuracy of 99.4%, and ADLs, falls, and stumbles (near-falls) with an accuracy of 94.2%. In addition, results showed that the motion sensing E-yarn only needs to be present in one over-sock.
Collapse
Affiliation(s)
- Zahra Rahemtulla
- Nottingham School of Art & Design, Nottingham Trent University, Bonington Building, Dryden Street, Nottingham NG1 4GG, UK
| | - Alexander Turner
- School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK
| | - Carlos Oliveira
- Nottingham School of Art & Design, Nottingham Trent University, Bonington Building, Dryden Street, Nottingham NG1 4GG, UK
| | - Jake Kaner
- Nottingham School of Art & Design, Nottingham Trent University, Bonington Building, Dryden Street, Nottingham NG1 4GG, UK
| | - Tilak Dias
- Nottingham School of Art & Design, Nottingham Trent University, Bonington Building, Dryden Street, Nottingham NG1 4GG, UK
| | - Theodore Hughes-Riley
- Nottingham School of Art & Design, Nottingham Trent University, Bonington Building, Dryden Street, Nottingham NG1 4GG, UK
| |
Collapse
|
18
|
Yan J, Wang X, Shi J, Hu S. Skeleton-Based Fall Detection with Multiple Inertial Sensors Using Spatial-Temporal Graph Convolutional Networks. Sensors (Basel) 2023; 23:2153. [PMID: 36850753 PMCID: PMC9962182 DOI: 10.3390/s23042153] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 02/02/2023] [Accepted: 02/08/2023] [Indexed: 06/18/2023]
Abstract
The application of wearable devices for fall detection has been the focus of much research over the past few years. One of the most common problems in established fall detection systems is the large number of false positives in the recognition schemes. In this paper, to make full use of the dependence between human joints and improve the accuracy and reliability of fall detection, a fall-recognition method based on the skeleton and spatial-temporal graph convolutional networks (ST-GCN) was proposed, using the human motion data of body joints acquired by inertial measurement units (IMUs). Firstly, the motion data of five inertial sensors were extracted from the UP-Fall dataset and a human skeleton model for fall detection was established through the natural connection relationship of body joints; after that, the ST-GCN-based fall-detection model was established to extract the motion features of human falls and the activities of daily living (ADLs) at the spatial and temporal scales for fall detection; then, the influence of two hyperparameters and window size on the algorithm performance was discussed; finally, the recognition results of ST-GCN were also compared with those of MLP, CNN, RNN, LSTM, TCN, TST, and MiniRocket. The experimental results showed that the ST-GCN fall-detection model outperformed the other seven algorithms in terms of accuracy, precision, recall, and F1-score. This study provides a new method for IMU-based fall detection, which has the reference significance for improving the accuracy and robustness of fall detection.
Collapse
Affiliation(s)
- Jianjun Yan
- Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, East China University of Science and Technology, Shanghai 200237, China
- School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Xueqiang Wang
- School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Jiangtao Shi
- School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Shuai Hu
- School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China
| |
Collapse
|
19
|
Marques J, Moreno P. Online Fall Detection Using Wrist Devices. Sensors (Basel) 2023; 23:1146. [PMID: 36772187 PMCID: PMC9920426 DOI: 10.3390/s23031146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/10/2023] [Accepted: 01/16/2023] [Indexed: 06/18/2023]
Abstract
More than 37 million falls that require medical attention occur every year, mainly affecting the elderly. Besides the natural consequences of falls, most aged adults with a history of falling are likely to develop a fear of falling, leading to a decrease in their mobility level and impacting their overall quality of life. Previous wrist-based datasets revealed limitations such as unrealistic recording set-ups, lack of proper documentation and, most importantly, the absence of elderly people's movements. Therefore, this work proposes a new wrist-based dataset to tackle this problem. With this dataset, exhaustive research is carried out with the low computational FS-1 feature set (maximum, minimum, mean and variance) with various machine learning methods. This work presents an accelerometer-only fall detector streaming data at 50 Hz, using the low computational FS-1 feature set to train a 3NN algorithm with Euclidean distance, with a window size of 9 s. This work had battery and memory limitations in mind. It also developed a learning version that boosts the fall detector's performance over time, achieving no single false positives or false negatives over four days.
Collapse
Affiliation(s)
- João Marques
- Instituto Superior Técnico, Unviersidade de Lisboa, 1049-001 Lisboa, Portugal
| | - Plinio Moreno
- Instituto Superior Técnico, Unviersidade de Lisboa, 1049-001 Lisboa, Portugal
- Institute for Systems and Robotics, 1049-001 Lisboa, Portugal
| |
Collapse
|
20
|
Maray N, Ngu AH, Ni J, Debnath M, Wang L. Transfer Learning on Small Datasets for Improved Fall Detection. Sensors (Basel) 2023; 23:1105. [PMID: 36772148 PMCID: PMC9919743 DOI: 10.3390/s23031105] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/06/2023] [Accepted: 01/15/2023] [Indexed: 06/18/2023]
Abstract
Falls in the elderly are associated with significant morbidity and mortality. While numerous fall detection devices incorporating AI and machine learning algorithms have been developed, no known smartwatch-based system has been used successfully in real-time to detect falls for elderly persons. We have developed and deployed a SmartFall system on a commodity-based smartwatch which has been trialled by nine elderly participants. The system, while being usable and welcomed by the participants in our trials, has two serious limitations. The first limitation is the inability to collect a large amount of personalized data for training. When the fall detection model, which is trained with insufficient data, is used in the real world, it generates a large amount of false positives. The second limitation is the model drift problem. This means an accurate model trained using data collected with a specific device performs sub-par when used in another device. Therefore, building one model for each type of device/watch is not a scalable approach for developing smartwatch-based fall detection system. To tackle those issues, we first collected three datasets including accelerometer data for fall detection problem from different devices: the Microsoft watch (MSBAND), the Huawei watch, and the meta-sensor device. After that, a transfer learning strategy was applied to first explore the use of transfer learning to overcome the small dataset training problem for fall detection. We also demonstrated the use of transfer learning to generalize the model across the heterogeneous devices. Our preliminary experiments demonstrate the effectiveness of transfer learning for improving fall detection, achieving an F1 score higher by over 10% on average, an AUC higher by over 0.15 on average, and a smaller false positive prediction rate than the non-transfer learning approach across various datasets collected using different devices with different hardware specifications.
Collapse
|
21
|
Cardenas JD, Gutierrez CA, Aguilar-Ponce R. Deep Learning Multi-Class Approach for Human Fall Detection Based on Doppler Signatures. Int J Environ Res Public Health 2023; 20:1123. [PMID: 36673883 PMCID: PMC9858740 DOI: 10.3390/ijerph20021123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/30/2022] [Accepted: 01/04/2023] [Indexed: 06/17/2023]
Abstract
Falling events are a global health concern with short- and long-term physical and psychological implications, especially for the elderly population. This work aims to monitor human activity in an indoor environment and recognize falling events without requiring users to carry a device or sensor on their bodies. A sensing platform based on the transmission of a continuous wave (CW) radio-frequency (RF) probe signal was developed using general-purpose equipment. The CW probe signal is similar to the pilot subcarriers transmitted by commercial off-the-shelf WiFi devices. As a result, our methodology can easily be integrated into a joint radio sensing and communication scheme. The sensing process is carried out by analyzing the changes in phase, amplitude, and frequency that the probe signal suffers when it is reflected or scattered by static and moving bodies. These features are commonly extracted from the channel state information (CSI) of WiFi signals. However, CSI relies on complex data acquisition and channel estimation processes. Doppler radars have also been used to monitor human activity. While effective, a radar-based fall detection system requires dedicated hardware. In this paper, we follow an alternative method to characterize falling events on the basis of the Doppler signatures imprinted on the CW probe signal by a falling person. A multi-class deep learning framework for classification was conceived to differentiate falling events from other activities that can be performed in indoor environments. Two neural network models were implemented. The first is based on a long-short-term memory network (LSTM) and the second on a convolutional neural network (CNN). A series of experiments comprising 11 subjects were conducted to collect empirical data and test the system's performance. Falls were detected with an accuracy of 92.1% for the LSTM case, while for the CNN, an accuracy rate of 92.1% was obtained. The results demonstrate the viability of human fall detection based on a radio sensing system such as the one described in this paper.
Collapse
|
22
|
He C, Liu S, Zhong G, Wu H, Cheng L, Lin J, Huang Q. A Non-Contact Fall Detection Method for Bathroom Application Based on MEMS Infrared Sensors. Micromachines (Basel) 2023; 14:130. [PMID: 36677192 PMCID: PMC9867492 DOI: 10.3390/mi14010130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 12/23/2022] [Accepted: 12/30/2022] [Indexed: 06/17/2023]
Abstract
The ratio of the elderly to the total population around the world is larger than 10%, and about 30% of the elderly are injured by falls each year. Accidental falls, especially bathroom falls, account for a large proportion. Therefore, fall events detection of the elderly is of great importance. In this article, a non-contact fall detector based on a Micro-electromechanical Systems Pyroelectric Infrared (MEMS PIR) sensor and a thermopile IR array sensor is designed to detect bathroom falls. Besides, image processing algorithms with a low pass filter and double boundary scans are put forward in detail. Then, the statistical features of the area, center, duration and temperature are extracted. Finally, a 3-layer BP neural network is adopted to identify the fall events. Taking into account the key factors of ambient temperature, objective, illumination, fall speed, fall state, fall area and fall scene, 640 tests were performed in total, and 5-fold cross validation is adopted. Experimental results demonstrate that the averages of the precision, recall, detection accuracy and F1-Score are measured to be 94.45%, 90.94%, 92.81% and 92.66%, respectively, which indicates that the novel detection method is feasible. Thereby, this IOT detector can be extensively used for household bathroom fall detection and is low-cost and privacy-security guaranteed.
Collapse
Affiliation(s)
- Chunhua He
- School of Computer, Guangdong University of Technology, Guangzhou 510006, China
| | - Shuibin Liu
- School of Computer, Guangdong University of Technology, Guangzhou 510006, China
| | - Guangxiong Zhong
- School of Computer, Guangdong University of Technology, Guangzhou 510006, China
| | - Heng Wu
- School of Computer, Guangdong University of Technology, Guangzhou 510006, China
| | - Lianglun Cheng
- School of Computer, Guangdong University of Technology, Guangzhou 510006, China
| | - Juze Lin
- Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangdong Institute of Gerontology, Guangzhou 510080, China
| | - Qinwen Huang
- No. 5 Electronics Research Institute of the Ministry of Industry and Information Technology, Guangzhou 510610, China
| |
Collapse
|
23
|
Zheng L, Zhao J, Dong F, Huang Z, Zhong D. Fall Detection Algorithm Based on Inertial Sensor and Hierarchical Decision. Sensors (Basel) 2022; 23:107. [PMID: 36616703 PMCID: PMC9824604 DOI: 10.3390/s23010107] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 12/12/2022] [Accepted: 12/17/2022] [Indexed: 06/17/2023]
Abstract
With the aging of the human body and the reduction in its physiological capacities, falls have become a huge threat to individuals' physical and mental health, leading to serious bodily damage to the elderly and financial pressure on their families. As a result, it is vital to design a fall detection algorithm that monitors the state of human activity. This work designs a human fall detection algorithm based on hierarchical decision making. First, this work proposes a dimensionality reduction approach based on feature importance analysis (FIA), which optimizes the feature space via feature importance. This procedure reduces the dimension of features greatly and reduces the time spent by the model in the training phase. Second, this work proposes a hierarchical decision-making algorithm with an XGBoost model. The algorithm is divided into three levels. The first level uses the threshold approach to make a preliminary assessment of the data and only transfers the fall type data to the next level. The second level is an XGBoost-based classification algorithm to analyze again the type of data which remained from the first level. The third level employs a comparison method to determine the direction of the falling. Finally, the fall detection algorithm proposed in this paper has an accuracy of 98.19%, a sensitivity of 97.50%, and a specificity of 98.63%. The classification accuracy of the fall direction reaches 93.44%, and the algorithm can efficiently determine the fall direction.
Collapse
Affiliation(s)
- Liang Zheng
- Bioengineering College, Chongqing University, Chongqing 400044, China
- The 15th Research Institute of China Electronics Technology Group Corporation, Beijing 100083, China
- Wuhan Branch of Beijing Zunguan Technology Co., Ltd., Wuhan 430079, China
| | - Jie Zhao
- Bioengineering College, Chongqing University, Chongqing 400044, China
| | - Fangjie Dong
- Bioengineering College, Chongqing University, Chongqing 400044, China
| | - Zhiyong Huang
- School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
| | - Daidi Zhong
- Bioengineering College, Chongqing University, Chongqing 400044, China
| |
Collapse
|
24
|
Abdulghafor R, Abdelmohsen A, Turaev S, Ali MAH, Wani S. An Analysis of Body Language of Patients Using Artificial Intelligence. Healthcare (Basel) 2022; 10:healthcare10122504. [PMID: 36554028 PMCID: PMC9778650 DOI: 10.3390/healthcare10122504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/29/2022] [Accepted: 12/01/2022] [Indexed: 12/14/2022] Open
Abstract
In recent decades, epidemic and pandemic illnesses have grown prevalent and are a regular source of concern throughout the world. The extent to which the globe has been affected by the COVID-19 epidemic is well documented. Smart technology is now widely used in medical applications, with the automated detection of status and feelings becoming a significant study area. As a result, a variety of studies have begun to focus on the automated detection of symptoms in individuals infected with a pandemic or epidemic disease by studying their body language. The recognition and interpretation of arm and leg motions, facial recognition, and body postures is still a developing field, and there is a dearth of comprehensive studies that might aid in illness diagnosis utilizing artificial intelligence techniques and technologies. This literature review is a meta review of past papers that utilized AI for body language classification through full-body tracking or facial expressions detection for various tasks such as fall detection and COVID-19 detection, it looks at different methods proposed by each paper, their significance and their results.
Collapse
Affiliation(s)
- Rawad Abdulghafor
- Department of Computer Science, Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia
- Correspondence: (R.A.); (S.T.)
| | - Abdelrahman Abdelmohsen
- Department of Computer Science, Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia
| | - Sherzod Turaev
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates
- Correspondence: (R.A.); (S.T.)
| | - Mohammed A. H. Ali
- Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Sharyar Wani
- Department of Computer Science, Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia
| |
Collapse
|
25
|
Momin MS, Sufian A, Barman D, Dutta P, Dong M, Leo M. In-Home Older Adults' Activity Pattern Monitoring Using Depth Sensors: A Review. Sensors (Basel) 2022; 22:9067. [PMID: 36501769 PMCID: PMC9735577 DOI: 10.3390/s22239067] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 11/10/2022] [Accepted: 11/15/2022] [Indexed: 06/17/2023]
Abstract
The global population is aging due to many factors, including longer life expectancy through better healthcare, changing diet, physical activity, etc. We are also witnessing various frequent epidemics as well as pandemics. The existing healthcare system has failed to deliver the care and support needed to our older adults (seniors) during these frequent outbreaks. Sophisticated sensor-based in-home care systems may offer an effective solution to this global crisis. The monitoring system is the key component of any in-home care system. The evidence indicates that they are more useful when implemented in a non-intrusive manner through different visual and audio sensors. Artificial Intelligence (AI) and Computer Vision (CV) techniques may be ideal for this purpose. Since the RGB imagery-based CV technique may compromise privacy, people often hesitate to utilize in-home care systems which use this technology. Depth, thermal, and audio-based CV techniques could be meaningful substitutes here. Due to the need to monitor larger areas, this review article presents a systematic discussion on the state-of-the-art using depth sensors as primary data-capturing techniques. We mainly focused on fall detection and other health-related physical patterns. As gait parameters may help to detect these activities, we also considered depth sensor-based gait parameters separately. The article provides discussions on the topic in relation to the terminology, reviews, a survey of popular datasets, and future scopes.
Collapse
Affiliation(s)
- Md Sarfaraz Momin
- Department of Computer Science, Kaliachak College, University of Gour Banga, Malda 732101, India
- Department of Computer & System Sciences, Visva-Bharati University, Bolpur 731235, India
| | - Abu Sufian
- Department of Computer Science, University of Gour Banga, Malda 732101, India
| | - Debaditya Barman
- Department of Computer & System Sciences, Visva-Bharati University, Bolpur 731235, India
| | - Paramartha Dutta
- Department of Computer & System Sciences, Visva-Bharati University, Bolpur 731235, India
| | - Mianxiong Dong
- Department of Science and Informatics, Muroran Institute of Technology, Muroran 050-8585, Hokkaido, Japan
| | - Marco Leo
- National Research Council of Italy, Institute of Applied Sciences and Intelligent Systems, 73100 Lecce, Italy
| |
Collapse
|
26
|
Sadi MS, Alotaibi M, Islam MR, Islam MS, Alhmiedat T, Bassfar Z. Finger-Gesture Controlled Wheelchair with Enabling IoT. Sensors (Basel) 2022; 22:8716. [PMID: 36433326 PMCID: PMC9693444 DOI: 10.3390/s22228716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/30/2022] [Accepted: 11/07/2022] [Indexed: 06/16/2023]
Abstract
Modern wheelchairs, with advanced and robotic technologies, could not reach the life of millions of disabled people due to their high costs, technical limitations, and safety issues. This paper proposes a gesture-controlled smart wheelchair system with an IoT-enabled fall detection mechanism to overcome these problems. It can recognize gestures using Convolutional Neural Network (CNN) model along with computer vision algorithms and can control the wheelchair automatically by utilizing these gestures. It maintains the safety of the users by performing fall detection with IoT-based emergency messaging systems. The development cost of the overall system is cheap and is lesser than USD 300. Hence, it is expected that the proposed smart wheelchair should be affordable, safe, and helpful to physically disordered people in their independent mobility.
Collapse
Affiliation(s)
- Muhammad Sheikh Sadi
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh
| | - Mohammed Alotaibi
- Faculty of Computers and Information Technology, University of Tabuk, Tabuk 71490, Saudi Arabia
| | - Md. Repon Islam
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh
| | - Md. Saiful Islam
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh
| | - Tareq Alhmiedat
- Faculty of Computers and Information Technology, University of Tabuk, Tabuk 71490, Saudi Arabia
- Industrial Innovation & Robotics Center, University of Tabuk, Tabuk 71490, Saudi Arabia
| | - Zaid Bassfar
- Faculty of Computers and Information Technology, University of Tabuk, Tabuk 71490, Saudi Arabia
| |
Collapse
|
27
|
di Biase L, Pecoraro PM, Pecoraro G, Caminiti ML, Di Lazzaro V. Markerless Radio Frequency Indoor Monitoring for Telemedicine: Gait Analysis, Indoor Positioning, Fall Detection, Tremor Analysis, Vital Signs and Sleep Monitoring. Sensors (Basel) 2022; 22:8486. [PMID: 36366187 PMCID: PMC9656920 DOI: 10.3390/s22218486] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/20/2022] [Accepted: 11/01/2022] [Indexed: 06/16/2023]
Abstract
Quantitative indoor monitoring, in a low-invasive and accurate way, is still an unmet need in clinical practice. Indoor environments are more challenging than outdoor environments, and are where patients experience difficulty in performing activities of daily living (ADLs). In line with the recent trends of telemedicine, there is an ongoing positive impulse in moving medical assistance and management from hospitals to home settings. Different technologies have been proposed for indoor monitoring over the past decades, with different degrees of invasiveness, complexity, and capabilities in full-body monitoring. The major classes of devices proposed are inertial-based sensors (IMU), vision-based devices, and geomagnetic and radiofrequency (RF) based sensors. In recent years, among all available technologies, there has been an increasing interest in using RF-based technology because it can provide a more accurate and reliable method of tracking patients' movements compared to other methods, such as camera-based systems or wearable sensors. Indeed, RF technology compared to the other two techniques has higher compliance, low energy consumption, does not need to be worn, is less susceptible to noise, is not affected by lighting or other physical obstacles, has a high temporal resolution without a limited angle of view, and fewer privacy issues. The aim of the present narrative review was to describe the potential applications of RF-based indoor monitoring techniques and highlight their differences compared to other monitoring technologies.
Collapse
Affiliation(s)
- Lazzaro di Biase
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
- Brain Innovations Lab, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo 21, 00128 Rome, Italy
| | - Pasquale Maria Pecoraro
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
| | - Giovanni Pecoraro
- Department of Electronics Engineering, University of Rome Tor Vergata, 00133 Rome, Italy
| | - Maria Letizia Caminiti
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
| | - Vincenzo Di Lazzaro
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
| |
Collapse
|
28
|
Al-qaness MAA, Helmi AM, Dahou A, Elaziz MA. The Applications of Metaheuristics for Human Activity Recognition and Fall Detection Using Wearable Sensors: A Comprehensive Analysis. Biosensors (Basel) 2022; 12:821. [PMID: 36290958 PMCID: PMC9599938 DOI: 10.3390/bios12100821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 09/25/2022] [Accepted: 09/28/2022] [Indexed: 06/16/2023]
Abstract
In this paper, we study the applications of metaheuristics (MH) optimization algorithms in human activity recognition (HAR) and fall detection based on sensor data. It is known that MH algorithms have been utilized in complex engineering and optimization problems, including feature selection (FS). Thus, in this regard, this paper used nine MH algorithms as FS methods to boost the classification accuracy of the HAR and fall detection applications. The applied MH were the Aquila optimizer (AO), arithmetic optimization algorithm (AOA), marine predators algorithm (MPA), artificial bee colony (ABC) algorithm, genetic algorithm (GA), slime mold algorithm (SMA), grey wolf optimizer (GWO), whale optimization algorithm (WOA), and particle swarm optimization algorithm (PSO). First, we applied efficient prepossessing and segmentation methods to reveal the motion patterns and reduce the time complexities. Second, we developed a light feature extraction technique using advanced deep learning approaches. The developed model was ResRNN and was composed of several building blocks from deep learning networks including convolution neural networks (CNN), residual networks, and bidirectional recurrent neural networks (BiRNN). Third, we applied the mentioned MH algorithms to select the optimal features and boost classification accuracy. Finally, the support vector machine and random forest classifiers were employed to classify each activity in the case of multi-classification and to detect fall and non-fall actions in the case of binary classification. We used seven different and complex datasets for the multi-classification case: the PAMMP2, Sis-Fall, UniMiB SHAR, OPPORTUNITY, WISDM, UCI-HAR, and KU-HAR datasets. In addition, we used the Sis-Fall dataset for the binary classification (fall detection). We compared the results of the nine MH optimization methods using different performance indicators. We concluded that MH optimization algorithms had promising performance in HAR and fall detection applications.
Collapse
Affiliation(s)
- Mohammed A. A. Al-qaness
- College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China
| | - Ahmed M. Helmi
- College of Engineering and Information Technology, Buraydah Private Colleges, Buraydah 51418, Saudi Arabia
- Computer and Systems Engineering Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt
| | - Abdelghani Dahou
- Mathematics and Computer Science Department, University of Ahmed DRAIA, Adrar 01000, Algeria
- LDDI Laboratory, Faculty of Science and Technology, University of Ahmed DRAIA, Adrar 01000, Algeria
| | - Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman 346, United Arab Emirates
- Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon
| |
Collapse
|
29
|
Magnuszewski L, Wojszel A, Kasiukiewicz A, Wojszel ZB. Falls at the Geriatric Hospital Ward in the Context of Risk Factors of Falling Detected in a Comprehensive Geriatric Assessment. Int J Environ Res Public Health 2022; 19:10789. [PMID: 36078502 PMCID: PMC9518316 DOI: 10.3390/ijerph191710789] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/17/2022] [Accepted: 08/26/2022] [Indexed: 06/15/2023]
Abstract
It is only by knowing the most common causes of falls in the hospital that appropriate and targeted fall prevention measures can be implemented. This study aimed to assess the frequency of falls in a hospital geriatrics ward and the circumstances in which they occurred and evaluate the parameters of the comprehensive geriatric assessment (CGA) correlating with falls. We considered medical, functional, and nutritional factors associated with falls and built multivariable logistic regression analysis models. A total of 416 (median age 82 (IQR 77-86) years, 77.4% women) hospitalizations in the geriatrics ward were analyzed within 8 months. We compared the results of a CGA (including health, psycho-physical abilities, nutritional status, risk of falls, frailty syndrome, etc.) in patients who fell and did not fall. Fourteen falls (3.3% of patients) were registered; the rate was 4.4 falls per 1000 patient days. They most often occurred in the patient's room while changing position. Falls happened more frequently among people who were more disabled, had multimorbidity, were taking more medications (certain classes of drugs in particular), had Parkinson's disease and diabetes, reported falls in the last year, and were diagnosed with orthostatic hypotension. Logistic regression determined the significant independent association between in-hospital falls and a history of falls in the previous 12 months, orthostatic hypotension, Parkinson's disease, and taking statins, benzodiazepines, and insulin. Analysis of the registered falls that occurred in the hospital ward allowed for an analysis of the circumstances in which they occurred and helped to identify people at high risk of falling in a hospital, which can guide appropriate intervention and act as an indicator of good hospital care.
Collapse
Affiliation(s)
- Lukasz Magnuszewski
- Department of Geriatrics, Faculty of Health Sciences, Medical University of Bialystok, 15-471 Bialystok, Poland
- Department of Geriatrics, Hospital of the Ministry of Interior and Administration in Bialystok, 15-471 Bialystok, Poland
- Doctoral Studies, Faculty of Health Sciences, Medical University of Bialystok, 15-471 Bialystok, Poland
| | - Aleksandra Wojszel
- Student’s Scientific Society at the Department of Geriatrics, Faculty of Health Sciences, Medical University of Bialystok, 15-471 Bialystok, Poland
| | - Agnieszka Kasiukiewicz
- Department of Geriatrics, Faculty of Health Sciences, Medical University of Bialystok, 15-471 Bialystok, Poland
- Department of Geriatrics, Hospital of the Ministry of Interior and Administration in Bialystok, 15-471 Bialystok, Poland
| | - Zyta Beata Wojszel
- Department of Geriatrics, Faculty of Health Sciences, Medical University of Bialystok, 15-471 Bialystok, Poland
- Department of Geriatrics, Hospital of the Ministry of Interior and Administration in Bialystok, 15-471 Bialystok, Poland
| |
Collapse
|
30
|
Lobanova V, Slizov V, Anishchenko L. Contactless Fall Detection by Means of Multiple Bioradars and Transfer Learning. Sensors (Basel) 2022; 22:6285. [PMID: 36016046 PMCID: PMC9414391 DOI: 10.3390/s22166285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 08/09/2022] [Accepted: 08/18/2022] [Indexed: 06/15/2023]
Abstract
Fall detection in humans is critical in the prevention of life-threatening conditions. This is especially important for elderly people who are living alone. Therefore, automatic fall detection is one of the most relevant problems in geriatrics. Bioradiolocation-based methods have already shown their efficiency in contactless fall detection. However, there is still a wide range of areas to improve the precision of fall recognition based on view-independent concepts. In particular, in this paper, we propose an approach based on a more complex multi-channel system (three or four bioradars) in combination with the wavelet transform and transfer learning. In the experiments, we have used several radar configurations for recording different movement types. Then, for the binary classification task, a pre-trained convolutional neural network AlexNet has been fine-tuned using scalograms. The proposed systems have shown a noticeable improvement in the fall recognition performance in comparison with the previously used two-bioradar system. The accuracy and Cohen's kappa of the two-bioradar system are 0.92 and 0.86 respectively, whereas the accuracy and Cohen's kappa of the four-bioradar system are 0.99 and 0.99 respectively. The three-bioradar system's performance turned out to be in between two of the aforementioned systems and its calculated accuracy and Cohen's kappa are 0.98 and 0.97 respectively. These results may be potentially used in the design of a contactless multi-bioradar fall detection system.
Collapse
|
31
|
Liu W, Liu X, Hu Y, Shi J, Chen X, Zhao J, Wang S, Hu Q. Fall Detection for Shipboard Seafarers Based on Optimized BlazePose and LSTM. Sensors (Basel) 2022; 22:5449. [PMID: 35891143 PMCID: PMC9317772 DOI: 10.3390/s22145449] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 07/17/2022] [Accepted: 07/19/2022] [Indexed: 06/01/2023]
Abstract
Aiming to avoid personal injury caused by the failure of timely medical assistance following a fall by seafarer members working on ships, research on the detection of seafarer's falls and timely warnings to safety officers can reduce the loss and severe consequences of falls to seafarers. To improve the detection accuracy and real-time performance of the seafarer fall detection algorithm, a seafarer fall detection algorithm based on BlazePose-LSTM is proposed. This algorithm can automatically extract the human body key point information from the video image obtained by the vision sensor, analyze its internal data correlation characteristics, and realize the process from RGB camera image processing to seafarer fall detection. This fall detection algorithm extracts the human body key point information through the optimized BlazePose human body key point information extraction network. In this section, a new method for human bounding-box acquisition is proposed. In this study, a head detector based on the Vitruvian theory was used to replace the pre-trained SSD body detector in the BlazePose preheating module. Simultaneously, an offset vector is proposed to update the bounding box obtained. This method can reduce the frequency of repeated use of the head detection module. The algorithm then uses the long short-term memory neural network to detect seafarer falls. After extracting fall and related behavior data from the URFall public data set and FDD public data set to enrich the self-made data set, the experimental results show that the algorithm can achieve 100% accuracy and 98.5% specificity for the seafarer's falling behavior, indicating that the algorithm has reasonable practicability and strong generalization ability. The detection frame rate can reach 29 fps on a CPU, which can meet the effect of real-time detection. The proposed method can be deployed on common vision sensors.
Collapse
Affiliation(s)
- Wei Liu
- Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China; (W.L.); (X.L.); (J.S.); (X.C.); (J.Z.); (S.W.)
| | - Xu Liu
- Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China; (W.L.); (X.L.); (J.S.); (X.C.); (J.Z.); (S.W.)
| | - Yuan Hu
- College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China;
| | - Jie Shi
- Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China; (W.L.); (X.L.); (J.S.); (X.C.); (J.Z.); (S.W.)
| | - Xinqiang Chen
- Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China; (W.L.); (X.L.); (J.S.); (X.C.); (J.Z.); (S.W.)
| | - Jiansen Zhao
- Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China; (W.L.); (X.L.); (J.S.); (X.C.); (J.Z.); (S.W.)
| | - Shengzheng Wang
- Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China; (W.L.); (X.L.); (J.S.); (X.C.); (J.Z.); (S.W.)
| | - Qingsong Hu
- College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China;
| |
Collapse
|
32
|
Feng M, Liu J. A pre-impact fall detection data segmentation method based on multi-channel convolutional neural network and class activation mapping. Physiol Meas 2022; 43. [PMID: 35688139 DOI: 10.1088/1361-6579/ac77d4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 06/10/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE A segmentation method for pre-impact fall detection data is investigated. Specifically, it studies how to partition data segments that are important for classification from continuous inertial sensor data for pre-impact fall detection. APPROACH In this study, a trigger-based algorithm combining multi-channel convolutional neural network and class activation mapping was proposed to solve the problem of data segmentation. First, a pre-impact fall detection training dataset was established and divided into two parts. For falls, the 1-second data was divided from the peak value of the acceleration signal magnitude vector to the starting direction. For activities of daily living, the cycle segmentation was performed for a 1-second window size. Second, a heat map of the class activation regions of the sensor data was formed using a multi-channel convolutional neural network and a class activation mapping algorithm. Finally, the data segmentation strategy was established based on the heat map, the basic law of falls and the real-time requirements. MAIN RESULTS This method was verified by the SisFall dataset. The obtained segmentation strategy (i.e., to start segmenting a small data segment with a window duration of 325 ms when the acceleration signal magnitude vector is less than 9.217 m/s2) met the real-time requirements for pre-impact fall detection. Moreover, it was suitable for various machine learning algorithms, and the accuracy of the machine learning algorithms used exceeded 94.8%, with the machine learning algorithms verifying the data segmentation strategy. SIGNIFICANCE The proposed method can automatically identify the class activation area, save the computing resources of wearable devices, shorten the duration of segmentation window, and ensure the real-time performance of pre-impact fall detection.
Collapse
Affiliation(s)
- Mingxu Feng
- Nanchang University, Qianhu Campus of Nanchang University, Nanchang City, Jiangxi Province, Nanchang, 330031, CHINA
| | - Jizhong Liu
- Nanchang University, China, Nanchang, Jiangxi, 330031, CHINA
| |
Collapse
|
33
|
Fernández-Bermejo Ruiz J, Dorado Chaparro J, Santofimia Romero MJ, Villanueva Molina FJ, Del Toro García X, Bolaños Peño C, Llumiguano Solano H, Colantonio S, Flórez-Revuelta F, López JC. Bedtime Monitoring for Fall Detection and Prevention in Older Adults. Int J Environ Res Public Health 2022; 19:7139. [PMID: 35742388 DOI: 10.3390/ijerph19127139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/03/2022] [Accepted: 06/08/2022] [Indexed: 11/16/2022]
Abstract
Life expectancy has increased, so the number of people in need of intensive care and attention is also growing. Falls are a major problem for older adult health, mainly because of the consequences they entail. Falls are indeed the second leading cause of unintentional death in the world. The impact on privacy, the cost, low performance, or the need to wear uncomfortable devices are the main causes for the lack of widespread solutions for fall detection and prevention. This work present a solution focused on bedtime that addresses all these causes. Bed exit is one of the most critical moments, especially when the person suffers from a cognitive impairment or has mobility problems. For this reason, this work proposes a system that monitors the position in bed in order to identify risk situations as soon as possible. This system is also combined with an automatic fall detection system. Both systems work together, in real time, offering a comprehensive solution to automatic fall detection and prevention, which is low cost and guarantees user privacy. The proposed system was experimentally validated with young adults. Results show that falls can be detected, in real time, with an accuracy of 93.51%, sensitivity of 92.04% and specificity of 95.45%. Furthermore, risk situations, such as transiting from lying on the bed to sitting on the bed side, are recognized with a 96.60% accuracy, and those where the user exits the bed are recognized with a 100% accuracy.
Collapse
|
34
|
Hsu WW, Guo JM, Chen CY, Chang YC. Fall Detection with the Spatial-Temporal Correlation Encoded by a Sequence-to-Sequence Denoised GAN. Sensors (Basel) 2022; 22:s22114194. [PMID: 35684812 PMCID: PMC9185321 DOI: 10.3390/s22114194] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 05/27/2022] [Accepted: 05/28/2022] [Indexed: 02/01/2023]
Abstract
Falling is a major cause of personal injury and accidental death worldwide, in particular for the elderly. For aged care, a falling alarm system is highly demanded so that medical aid can be obtained immediately when the fall accidents happen. Previous studies on fall detection lacked practical considerations to deal with real-world situations, including the camera’s mounting angle, lighting differences between day and night, and the privacy protection for users. In our experiments, IR-depth images and thermal images were used as the input source for fall detection; as a result, detailed facial information is not captured by the system for privacy reasons, and it is invariant to the lighting conditions. Due to the different occurrence rates between fall accidents and other normal activities, supervised learning approaches may suffer from the problem of data imbalance in the training phase. Accordingly, in this study, anomaly detection is performed using unsupervised learning approaches so that the models were trained only with the normal cases while the fall accident was defined as an anomaly event. The proposed system takes sequential frames as the inputs to predict future frames based on a GAN structure, and it provides (1) multi-subject detection, (2) real-time fall detection triggered by motion, (3) a solution to the situation that subjects were occluded after falling, and (4) a denoising scheme for depth images. The experimental results show that the proposed system achieves the state-of-the-art performance and copes with the real-world cases successfully.
Collapse
Affiliation(s)
- Wei-Wen Hsu
- Department of Computer Science and Information Engineering, National Taitung University, Tatung 950309, Taiwan; (W.-W.H.); (Y.-C.C.)
| | - Jing-Ming Guo
- Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan;
- Advanced Intelligent Image and Vision Technology Research Center, National Taiwan University of Science and Technology, Taipei 106335, Taiwan
- Correspondence:
| | - Chien-Yu Chen
- Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan;
| | - Yao-Chung Chang
- Department of Computer Science and Information Engineering, National Taitung University, Tatung 950309, Taiwan; (W.-W.H.); (Y.-C.C.)
| |
Collapse
|
35
|
Syed AS, Sierra-Sosa D, Kumar A, Elmaghraby A. A Deep Convolutional Neural Network-XGB for Direction and Severity Aware Fall Detection and Activity Recognition. Sensors (Basel) 2022; 22:s22072547. [PMID: 35408163 PMCID: PMC9002977 DOI: 10.3390/s22072547] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 03/16/2022] [Accepted: 03/24/2022] [Indexed: 01/12/2023]
Abstract
Activity and Fall detection have been a topic of keen interest in the field of ambient assisted living system research. Such systems make use of different sensing mechanisms to monitor human motion and aim to ascertain the activity being performed for health monitoring and other purposes. Towards this end, in addition to activity recognition, fall detection is an especially important task as falls can lead to injuries and sometimes even death. This work presents a fall detection and activity recognition system that not only considers various activities of daily living but also considers detection of falls while taking into consideration the direction and severity. Inertial Measurement Unit (accelerometer and gyroscope) data from the SisFall dataset is first windowed into non-overlapping segments of duration 3 s. After suitable data augmentation, it is then passed on to a Convolutional Neural Network (CNN) for feature extraction with an eXtreme Gradient Boosting (XGB) last stage for classification into the various output classes. The experiments show that the gradient boosted CNN performs better than other comparable techniques, achieving an unweighted average recall of 88%.
Collapse
Affiliation(s)
- Abbas Shah Syed
- Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40208, USA; (A.K.); (A.E.)
- Correspondence:
| | - Daniel Sierra-Sosa
- Department of Computer Science and Information Technology, Hood College, Frederick, MD 21701, USA;
| | - Anup Kumar
- Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40208, USA; (A.K.); (A.E.)
| | - Adel Elmaghraby
- Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40208, USA; (A.K.); (A.E.)
| |
Collapse
|
36
|
Saho K, Hayashi S, Tsuyama M, Meng L, Masugi M. Machine Learning-Based Classification of Human Behaviors and Falls in Restroom via Dual Doppler Radar Measurements. Sensors (Basel) 2022; 22:s22051721. [PMID: 35270868 PMCID: PMC8915019 DOI: 10.3390/s22051721] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 02/18/2022] [Accepted: 02/20/2022] [Indexed: 12/04/2022]
Abstract
This study presents a radar-based remote measurement system for classification of human behaviors and falls in restrooms without privacy invasion. Our system uses a dual Doppler radar mounted onto a restroom ceiling and wall. Machine learning methods, including the convolutional neural network (CNN), long short-term memory, support vector machine, and random forest methods, are applied to the Doppler radar data to verify the model’s efficiency and features. Experimental results from 21 participants demonstrated the accurate classification of eight realistic behaviors, including falling. Using the Doppler spectrograms (time–velocity distribution) as the inputs, CNN showed the best results with an overall classification accuracy of 95.6% and 100% fall classification accuracy. We confirmed that these accuracies were better than those achieved by conventional restroom monitoring techniques using thermal sensors and radars. Furthermore, the comparison results of various machine learning methods and cases using each radar’s data show that the higher-order derivative parameters of acceleration and jerk, and the motion information in the horizontal direction are the efficient features for behavior classification in a restroom. These findings indicate that daily restroom monitoring using the proposed radar system accurately recognizes human behaviors and allows early detection of fall accidents.
Collapse
Affiliation(s)
- Kenshi Saho
- Department of Intelligent Robotics, Toyama Prefectural University, Imizu 939-0398, Japan
- Department of Electronic and Computer Engineering, Ritsumeikan University, Kusatsu 525-8577, Japan; (S.H.); (M.T.); (L.M.); (M.M.)
- Correspondence:
| | - Sora Hayashi
- Department of Electronic and Computer Engineering, Ritsumeikan University, Kusatsu 525-8577, Japan; (S.H.); (M.T.); (L.M.); (M.M.)
| | - Mutsuki Tsuyama
- Department of Electronic and Computer Engineering, Ritsumeikan University, Kusatsu 525-8577, Japan; (S.H.); (M.T.); (L.M.); (M.M.)
| | - Lin Meng
- Department of Electronic and Computer Engineering, Ritsumeikan University, Kusatsu 525-8577, Japan; (S.H.); (M.T.); (L.M.); (M.M.)
| | - Masao Masugi
- Department of Electronic and Computer Engineering, Ritsumeikan University, Kusatsu 525-8577, Japan; (S.H.); (M.T.); (L.M.); (M.M.)
| |
Collapse
|
37
|
Nikolov I, Liu J, Moeslund T. Imitating Emergencies: Generating Thermal Surveillance Fall Data Using Low-Cost Human-like Dolls. Sensors (Basel) 2022; 22:825. [PMID: 35161571 DOI: 10.3390/s22030825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/15/2022] [Accepted: 01/17/2022] [Indexed: 11/26/2022]
Abstract
Outdoor fall detection, in the context of accidents, such as falling from heights or in water, is a research area that has not received as much attention as other automated surveillance areas. Gathering sufficient data for developing deep-learning models for such applications has also proven to be not a straight-forward task. Normally, footage of volunteer people falling is used for providing data, but that can be a complicated and dangerous process. In this paper, we propose an application for thermal images of a low-cost rubber doll falling in a harbor, for simulating real emergencies. We achieve thermal signatures similar to a human on different parts of the doll’s body. The change of these thermal signatures over time is measured, and its stability is verified. We demonstrate that, even with the size and weight differences of the doll, the produced videos of falls have a similar motion and appearance to what is expected from real people. We show that the captured thermal doll data can be used for the real-world application of pedestrian detection by running the captured data through a state-of-the-art object detector trained on real people. An average confidence score of 0.730 is achieved, compared to a confidence score of 0.761 when using footage of real people falling. The captured fall sequences using the doll can be used as a substitute to sequences of people.
Collapse
|
38
|
Kristoffersson A, Lindén M. A Systematic Review of Wearable Sensors for Monitoring Physical Activity. Sensors (Basel) 2022; 22:s22020573. [PMID: 35062531 PMCID: PMC8778538 DOI: 10.3390/s22020573] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/27/2021] [Accepted: 01/05/2022] [Indexed: 01/01/2023]
Abstract
This article reviews the use of wearable sensors for the monitoring of physical activity (PA) for different purposes, including assessment of gait and balance, prevention and/or detection of falls, recognition of various PAs, conduction and assessment of rehabilitation exercises and monitoring of neurological disease progression. The article provides in-depth information on the retrieved articles and discusses study shortcomings related to demographic factors, i.e., age, gender, healthy participants vs patients, and study conditions. It is well known that motion patterns change with age and the onset of illnesses, and that the risk of falling increases with age. Yet, studies including older persons are rare. Gender distribution was not even provided in several studies, and others included only, or a majority of, men. Another shortcoming is that none of the studies were conducted in real-life conditions. Hence, there is still important work to be done in order to increase the usefulness of wearable sensors in these areas. The article highlights flaws in how studies based on previously collected datasets report on study samples and the data collected, which makes the validity and generalizability of those studies low. Exceptions exist, such as the promising recently reported open dataset FallAllD, wherein a longitudinal study with older adults is ongoing.
Collapse
|
39
|
Mehmood A. LightAnomalyNet: A Lightweight Framework for Efficient Abnormal Behavior Detection. Sensors (Basel) 2021; 21:s21248501. [PMID: 34960594 PMCID: PMC8704800 DOI: 10.3390/s21248501] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/14/2021] [Accepted: 12/16/2021] [Indexed: 11/16/2022]
Abstract
The continuous development of intelligent video surveillance systems has increased the demand for enhanced vision-based methods of automated detection of anomalies within various behaviors found in video scenes. Several methods have appeared in the literature that detect different anomalies by using the details of motion features associated with different actions. To enable the efficient detection of anomalies, alongside characterizing the specificities involved in features related to each behavior, the model complexity leading to computational expense must be reduced. This paper provides a lightweight framework (LightAnomalyNet) comprising a convolutional neural network (CNN) that is trained using input frames obtained by a computationally cost-effective method. The proposed framework effectively represents and differentiates between normal and abnormal events. In particular, this work defines human falls, some kinds of suspicious behavior, and violent acts as abnormal activities, and discriminates them from other (normal) activities in surveillance videos. Experiments on public datasets show that LightAnomalyNet yields better performance comparative to the existing methods in terms of classification accuracy and input frames generation.
Collapse
Affiliation(s)
- Abid Mehmood
- Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| |
Collapse
|
40
|
Baserga A, Grandi F, Masciadri A, Comai S, Salice F. High-Efficiency Multi-Sensor System for Chair Usage Detection. Sensors (Basel) 2021; 21:s21227580. [PMID: 34833654 PMCID: PMC8620359 DOI: 10.3390/s21227580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 11/02/2021] [Accepted: 11/05/2021] [Indexed: 11/16/2022]
Abstract
Recognizing Activities of Daily Living (ADL) or detecting falls in domestic environments require monitoring the movements and positions of a person. Several approaches use wearable devices or cameras, especially for fall detection, but they are considered intrusive by many users. To support such activities in an unobtrusive way, ambient-based solutions are available (e.g., based on PIRs, contact sensors, etc.). In this paper, we focus on the problem of sitting detection exploiting only unobtrusive sensors. In fact, sitting detection can be useful to understand the position of the user in many activities of the daily routines. While identifying sitting/lying on a sofa or bed is reasonably simple with pressure sensors, detecting whether a person is sitting on a chair is an open problem due to the natural chair position volatility. This paper proposes a reliable, not invasive and energetically sustainable system that can be used on chairs already present in the home. In particular, the proposed solution fuses the data of an accelerometer and a capacitive coupling sensor to understand if a person is sitting or not, discriminating the case of objects left on the chair. The results obtained in a real environment setting show an accuracy of 98.6% and a precision of 95%.
Collapse
Affiliation(s)
- Alessandro Baserga
- Department of Physics, Politecnico di Milano, 20133 Milan, Italy; (A.B.); (F.G.)
| | - Federico Grandi
- Department of Physics, Politecnico di Milano, 20133 Milan, Italy; (A.B.); (F.G.)
| | - Andrea Masciadri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milano, Italy; (A.M.); (F.S.)
| | - Sara Comai
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milano, Italy; (A.M.); (F.S.)
- Correspondence:
| | - Fabio Salice
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milano, Italy; (A.M.); (F.S.)
| |
Collapse
|
41
|
Calvillo-Arbizu J, Naranjo-Hernández D, Barbarov-Rostán G, Talaminos-Barroso A, Roa-Romero LM, Reina-Tosina J. A Sensor-Based mHealth Platform for Remote Monitoring and Intervention of Frailty Patients at Home. Int J Environ Res Public Health 2021; 18:11730. [PMID: 34770244 DOI: 10.3390/ijerph182111730] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/28/2021] [Accepted: 11/03/2021] [Indexed: 11/25/2022]
Abstract
Frailty syndrome is an independent risk factor for serious health episodes, disability, hospitalization, falls, loss of mobility, and cardiovascular disease. Its high reversibility demands personalized interventions among which exercise programs are highly efficient to contribute to its delay. Information technology-based solutions to support frailty have been recently approached, but most of them are focused on assessment and not on intervention. This paper describes a sensor-based mHealth platform integrated in a service-based architecture inside the FRAIL project towards the remote monitoring and intervention of pre-frail and frail patients at home. The aim of this platform is constituting an efficient and scalable system for reducing both the impact of aging and the advance of frailty syndrome. Among the results of this work are: (1) the development of elderly-focused sensors and platform; (2) a technical validation process of the sensor devices and the mHealth platform with young adults; and (3) an assessment of usability and acceptability of the devices with a set of pre-frail and frail patients. After the promising results obtained, future steps of this work involve performing a clinical validation in order to quantify the impact of the platform on health outcomes of frail patients.
Collapse
|
42
|
Alizadeh J, Bogdan M, Classen J, Fricke C. Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults. Sensors (Basel) 2021; 21:s21217166. [PMID: 34770473 PMCID: PMC8588363 DOI: 10.3390/s21217166] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 10/12/2021] [Accepted: 10/25/2021] [Indexed: 11/29/2022]
Abstract
Falls are a major cause of morbidity and mortality in neurological disorders. Technical means of detecting falls are of high interest as they enable rapid notification of caregivers and emergency services. Such approaches must reliably differentiate between normal daily activities and fall events. A promising technique might be based on the classification of movements based on accelerometer signals by machine-learning algorithms, but the generalizability of classifiers trained on laboratory data to real-world datasets is a common issue. Here, three machine-learning algorithms including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were trained to detect fall events. We used a dataset containing intentional falls (SisFall) to train the classifier and validated the approach on a different dataset which included real-world accidental fall events of elderly people (FARSEEING). The results suggested that the linear SVM was the most suitable classifier in this cross-dataset validation approach and reliably distinguished a fall event from normal everyday activity at an accuracy of 93% and similarly high sensitivity and specificity. Thus, classifiers based on linear SVM might be useful for automatic fall detection in real-world applications.
Collapse
Affiliation(s)
- Jalal Alizadeh
- Department of Neurology, Leipzig University, 04103 Leipzig, Germany; (J.A.); (J.C.)
- Department of Neuromorphic Information Processing, Leipzig University, 04009 Leipzig, Germany;
| | - Martin Bogdan
- Department of Neuromorphic Information Processing, Leipzig University, 04009 Leipzig, Germany;
| | - Joseph Classen
- Department of Neurology, Leipzig University, 04103 Leipzig, Germany; (J.A.); (J.C.)
| | - Christopher Fricke
- Department of Neurology, Leipzig University, 04103 Leipzig, Germany; (J.A.); (J.C.)
- Correspondence:
| |
Collapse
|
43
|
Syed AS, Sierra-Sosa D, Kumar A, Elmaghraby A. A Hierarchical Approach to Activity Recognition and Fall Detection Using Wavelets and Adaptive Pooling. Sensors (Basel) 2021; 21:s21196653. [PMID: 34640974 PMCID: PMC8512095 DOI: 10.3390/s21196653] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 09/19/2021] [Accepted: 10/04/2021] [Indexed: 02/01/2023]
Abstract
Human activity recognition has been a key study topic in the development of cyber physical systems and assisted living applications. In particular, inertial sensor based systems have become increasingly popular because they do not restrict users’ movement and are also relatively simple to implement compared to other approaches. In this paper, we present a hierarchical classification framework based on wavelets and adaptive pooling for activity recognition and fall detection predicting fall direction and severity. To accomplish this, windowed segments were extracted from each recording of inertial measurements from the SisFall dataset. A combination of wavelet based feature extraction and adaptive pooling was used before a classification framework was applied to determine the output class. Furthermore, tests were performed to determine the best observation window size and the sensor modality to use. Based on the experiments the best window size was found to be 3 s and the best sensor modality was found to be a combination of accelerometer and gyroscope measurements. These were used to perform activity recognition and fall detection with a resulting weighted F1 score of 94.67%. This framework is novel in terms of the approach to the human activity recognition and fall detection problem as it provides a scheme that is computationally less intensive while providing promising results and therefore can contribute to edge deployment of such systems.
Collapse
Affiliation(s)
- Abbas Shah Syed
- Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40208, USA; (A.K.); (A.E.)
- Correspondence:
| | - Daniel Sierra-Sosa
- Department of Computer Science and Information Technology, Hood College, Frederick, MD 21701, USA;
| | - Anup Kumar
- Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40208, USA; (A.K.); (A.E.)
| | - Adel Elmaghraby
- Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40208, USA; (A.K.); (A.E.)
| |
Collapse
|
44
|
Fabry DA, Bhowmik AK. Improving Speech Understanding and Monitoring Health with Hearing Aids Using Artificial Intelligence and Embedded Sensors. Semin Hear 2021; 42:295-308. [PMID: 34594091 PMCID: PMC8463124 DOI: 10.1055/s-0041-1735136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
This article details ways that machine learning and artificial intelligence technologies are being integrated in modern hearing aids to improve speech understanding in background noise and provide a gateway to overall health and wellness. Discussion focuses on how Starkey incorporates automatic and user-driven optimization of speech intelligibility with onboard hearing aid signal processing and machine learning algorithms, smartphone-based deep neural network processing, and wireless hearing aid accessories. The article will conclude with a review of health and wellness tracking capabilities that are enabled by embedded sensors and artificial intelligence.
Collapse
Affiliation(s)
- David A Fabry
- Starkey Hearing Technologies, Eden Prairie, Minnesota
| | | |
Collapse
|
45
|
Zhang J, Li J, Wang W. A Class-Imbalanced Deep Learning Fall Detection Algorithm Using Wearable Sensors. Sensors (Basel) 2021; 21:s21196511. [PMID: 34640830 PMCID: PMC8512051 DOI: 10.3390/s21196511] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/16/2021] [Accepted: 09/27/2021] [Indexed: 12/01/2022]
Abstract
Falling represents one of the most serious health risks for elderly people; it may cause irreversible injuries if the individual cannot obtain timely treatment after the fall happens. Therefore, timely and accurate fall detection algorithm research is extremely important. Recently, a number of researchers have focused on fall detection and made many achievements, and most of the relevant algorithm studies are based on ideal class-balanced datasets. However, in real-life applications, the possibilities of Activities of Daily Life (ADL) and fall events are different, so the data collected by wearable sensors suffers from class imbalance. The previously developed algorithms perform poorly on class-imbalanced data. In order to solve this problem, this paper proposes an algorithm that can effectively distinguish falls from a large amount of ADL signals. Compared with the state-of-the-art fall detection algorithms, the proposed method can achieve the highest score in multiple evaluation methods, with a sensitivity of 99.33%, a specificity of 91.86%, an F-Score of 98.44% and an AUC of 98.35%. The results prove that the proposed algorithm is effective on class-imbalanced data and more suitable for real-life application compared to previous works.
Collapse
Affiliation(s)
- Jing Zhang
- School of University of Chinese Academy of Sciences, Beijing 100049, China; (J.Z.); (W.W.)
- Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
| | - Jia Li
- School of University of Chinese Academy of Sciences, Beijing 100049, China; (J.Z.); (W.W.)
- Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- Correspondence:
| | - Weibing Wang
- School of University of Chinese Academy of Sciences, Beijing 100049, China; (J.Z.); (W.W.)
- Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
| |
Collapse
|
46
|
Abstract
The implementation of people monitoring system is an evolving research theme. This paper introduces an elderly monitoring system that recognizes human posture from overlapping cameras for people fall detection in a smart home environment. In these environments, the zone of movement is limited. Our approach used this characteristic to recognize human posture fastly by proposing a region-wise modelling approach. It classifies persons pose in four groups: standing, crouching, sitting and lying on the floor. These postures are obtained by calculating an estimation of the human bounding volume. This volume is estimated by obtaining the height of the person and its surface that is in contact with the ground according to the foreground information of each camera. Using them, we distinguish each postures and differentiate lying on floor posture, which can be considered as the falling posture from other postures. The global multiview information of the scene is obtaining by using homographic projection. We test our proposed algorithm on multiple cameras based fall detection public dataset and the results prove the efficiency of our method.
Collapse
Affiliation(s)
| | - Béthel Atohoun
- Ecole Supérieure de Gestion d'Informatique et des Sciences, Benin
| |
Collapse
|
47
|
Nikolaidou M, Kotronis C, Routis I, Politi E, Dimitrakopoulos G, Anagnostopoulos D, Djelouat H, Amira A, Bensaali F. Incorporating patient concerns into design requirements for IoMT-based systems: The fall detection case study. Health Informatics J 2021; 27:1460458220982640. [PMID: 33570009 DOI: 10.1177/1460458220982640] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Internet of Medical Things (IoMT) systems are envisioned to provide high-quality healthcare services to patients in the comfort of their home, utilizing cutting-edge Internet of Things (IoT) technologies and medical sensors. Patient comfort and willingness to participate in such efforts is a prominent factor for their adoption. As IoT technology has provided solutions for all technical issues, patient concerns are those that seem to restrict their wider adoption. To enhance patient awareness of the system properties and enhance their willingness to adopt IoMT solutions, this paper presents a novel methodology to integrate patient concerns in the design requirements of such systems. It comprises a number of straightforward steps that an IoMT designer can follow, starting from identifying patient concerns, incorporating them in system design requirements as criticalities, proceeding to system implementation and testing, and finally, verifying that it fulfills the concerns of the patients. To showcase the effectiveness of the proposed methodology, the paper applies it in the design and implementation of a fall detection system for elderly patients remotely monitored in their homes.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Abbes Amira
- Institute of Artificial Intelligence, De Montfort University, UK
| | | |
Collapse
|
48
|
Usmani S, Saboor A, Haris M, Khan MA, Park H. Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review. Sensors (Basel) 2021; 21:s21155134. [PMID: 34372371 PMCID: PMC8347190 DOI: 10.3390/s21155134] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/16/2021] [Accepted: 07/24/2021] [Indexed: 12/15/2022]
Abstract
Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues.
Collapse
Affiliation(s)
- Sara Usmani
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan; (S.U.); (M.H.)
| | - Abdul Saboor
- Department of Electrical Engineering (ESAT), Katholieke Universiteit (KU) Leuven, 3000 Leuven, Belgium;
| | - Muhammad Haris
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan; (S.U.); (M.H.)
| | - Muneeb A. Khan
- Department of Software, Sangmyung University, Cheonan 31066, Korea;
| | - Heemin Park
- Department of Software, Sangmyung University, Cheonan 31066, Korea;
- Correspondence:
| |
Collapse
|
49
|
Cardenas JD, Gutierrez CA, Aguilar-Ponce R. Influence of the Antenna Orientation on WiFi-Based Fall Detection Systems. Sensors (Basel) 2021; 21:s21155121. [PMID: 34372358 PMCID: PMC8347439 DOI: 10.3390/s21155121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 07/22/2021] [Accepted: 07/23/2021] [Indexed: 11/16/2022]
Abstract
The growing elderly population living independently demands remote systems for health monitoring. Falls are considered recurring fatal events and therefore have become a global health problem. Fall detection systems based on WiFi radio frequency signals still have limitations due to the difficulty of differentiating the features of a fall from other similar activities. Additionally, the antenna orientation has not been taking into account as an influencing factor of classification performance. Therefore, we present in this paper an analysis of the classification performance in relation to the antenna orientation and the effects related to polarization and radiation pattern. Furthermore, the implementation of a device-free fall detection platform to collect empirical data on falls is shown. The platform measures the Doppler spectrum of a probe signal to extract the Doppler signatures generated by human movement and whose features can be used to identify falling events. The system explores two antenna polarization: horizontal and vertical. The accuracy reached by horizontal polarization is 92% with a false negative rate of 8%. Vertical polarization achieved 50% accuracy and false negatives rate.
Collapse
|
50
|
Yu X, Jang J, Xiong S. A Large-Scale Open Motion Dataset (KFall) and Benchmark Algorithms for Detecting Pre-impact Fall of the Elderly Using Wearable Inertial Sensors. Front Aging Neurosci 2021; 13:692865. [PMID: 34335231 PMCID: PMC8322729 DOI: 10.3389/fnagi.2021.692865] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 06/15/2021] [Indexed: 11/18/2022] Open
Abstract
Research on pre-impact fall detection with wearable inertial sensors (detecting fall accidents prior to body-ground impacts) has grown rapidly in the past decade due to its great potential for developing an on-demand fall-related injury prevention system. However, most researchers use their own datasets to develop fall detection algorithms and rarely make these datasets publicly available, which poses a challenge to fairly evaluate the performance of different algorithms on a common basis. Even though some open datasets have been established recently, most of them are impractical for pre-impact fall detection due to the lack of temporal labels for fall time and limited types of motions. In order to overcome these limitations, in this study, we proposed and publicly provided a large-scale motion dataset called “KFall,” which was developed from 32 Korean participants while wearing an inertial sensor on the low back and performing 21 types of activities of daily living and 15 types of simulated falls. In addition, ready-to-use temporal labels of the fall time based on synchronized motion videos were published along with the dataset. Those enhancements make KFall the first public dataset suitable for pre-impact fall detection, not just for post-fall detection. Importantly, we have also developed three different types of latest algorithms (threshold based, support-vector machine, and deep learning), using the KFall dataset for pre-impact fall detection so that researchers and practitioners can flexibly choose the corresponding algorithm. Deep learning algorithm achieved both high overall accuracy and balanced sensitivity (99.32%) and specificity (99.01%) for pre-impact fall detection. Support vector machine also demonstrated a good performance with a sensitivity of 99.77% and specificity of 94.87%. However, the threshold-based algorithm showed relatively poor results, especially the specificity (83.43%) was much lower than the sensitivity (95.50%). The performance of these algorithms could be regarded as a benchmark for further development of better algorithms with this new dataset. This large-scale motion dataset and benchmark algorithms could provide researchers and practitioners with valuable data and references to develop new technologies and strategies for pre-impact fall detection and proactive injury prevention for the elderly.
Collapse
Affiliation(s)
- Xiaoqun Yu
- Department of Industrial and Systems Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| | - Jaehyuk Jang
- Department of Industrial and Systems Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| | - Shuping Xiong
- Department of Industrial and Systems Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| |
Collapse
|