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Klein LC, Chellal AA, Grilo V, Braun J, Gonçalves J, Pacheco MF, Fernandes FP, Monteiro FC, Lima J. Angle Assessment for Upper Limb Rehabilitation: A Novel Light Detection and Ranging (LiDAR)-Based Approach. SENSORS (BASEL, SWITZERLAND) 2024; 24:530. [PMID: 38257623 PMCID: PMC10820647 DOI: 10.3390/s24020530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 12/29/2023] [Accepted: 01/09/2024] [Indexed: 01/24/2024]
Abstract
The accurate measurement of joint angles during patient rehabilitation is crucial for informed decision making by physiotherapists. Presently, visual inspection stands as one of the prevalent methods for angle assessment. Although it could appear the most straightforward way to assess the angles, it presents a problem related to the high susceptibility to error in the angle estimation. In light of this, this study investigates the possibility of using a new approach to angle calculation: a hybrid approach leveraging both a camera and LiDAR technology, merging image data with point cloud information. This method employs AI-driven techniques to identify the individual and their joints, utilizing the cloud-point data for angle computation. The tests, considering different exercises with different perspectives and distances, showed a slight improvement compared to using YOLO v7 for angle calculation. However, the improvement comes with higher system costs when compared with other image-based approaches due to the necessity of equipment such as LiDAR and a loss of fluidity during the exercise performance. Therefore, the cost-benefit of the proposed approach could be questionable. Nonetheless, the results hint at a promising field for further exploration and the potential viability of using the proposed methodology.
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Affiliation(s)
- Luan C. Klein
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal; (L.C.K.); (A.A.C.); (V.G.); (J.B.); (J.G.); (M.F.P.); (F.P.F.); (F.C.M.)
- Department of Electronics (DAELN), Universidade Tecnológica Federal do Paraná (UTFPR), Campus Curitiba, 80230-901 Curitiba, Brazil
| | - Arezki Abderrahim Chellal
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal; (L.C.K.); (A.A.C.); (V.G.); (J.B.); (J.G.); (M.F.P.); (F.P.F.); (F.C.M.)
- School of Science and Technology, Universidade de Trás-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal
| | - Vinicius Grilo
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal; (L.C.K.); (A.A.C.); (V.G.); (J.B.); (J.G.); (M.F.P.); (F.P.F.); (F.C.M.)
| | - João Braun
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal; (L.C.K.); (A.A.C.); (V.G.); (J.B.); (J.G.); (M.F.P.); (F.P.F.); (F.C.M.)
- Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal
| | - José Gonçalves
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal; (L.C.K.); (A.A.C.); (V.G.); (J.B.); (J.G.); (M.F.P.); (F.P.F.); (F.C.M.)
- Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
| | - Maria F. Pacheco
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal; (L.C.K.); (A.A.C.); (V.G.); (J.B.); (J.G.); (M.F.P.); (F.P.F.); (F.C.M.)
- Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
| | - Florbela P. Fernandes
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal; (L.C.K.); (A.A.C.); (V.G.); (J.B.); (J.G.); (M.F.P.); (F.P.F.); (F.C.M.)
- Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
| | - Fernando C. Monteiro
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal; (L.C.K.); (A.A.C.); (V.G.); (J.B.); (J.G.); (M.F.P.); (F.P.F.); (F.C.M.)
- Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
| | - José Lima
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal; (L.C.K.); (A.A.C.); (V.G.); (J.B.); (J.G.); (M.F.P.); (F.P.F.); (F.C.M.)
- Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal
- Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
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Davarzani S, Saucier D, Talegaonkar P, Parker E, Turner A, Middleton C, Carroll W, Ball JE, Gurbuz A, Chander H, Burch RF, Smith BK, Knight A, Freeman C. Closing the Wearable Gap: Foot-ankle kinematic modeling via deep learning models based on a smart sock wearable. WEARABLE TECHNOLOGIES 2023; 4:e4. [PMID: 38487777 PMCID: PMC10936318 DOI: 10.1017/wtc.2023.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 12/09/2022] [Accepted: 01/04/2023] [Indexed: 03/17/2024]
Abstract
The development of wearable technology, which enables motion tracking analysis for human movement outside the laboratory, can improve awareness of personal health and performance. This study used a wearable smart sock prototype to track foot-ankle kinematics during gait movement. Multivariable linear regression and two deep learning models, including long short-term memory (LSTM) and convolutional neural networks, were trained to estimate the joint angles in sagittal and frontal planes measured by an optical motion capture system. Participant-specific models were established for ten healthy subjects walking on a treadmill. The prototype was tested at various walking speeds to assess its ability to track movements for multiple speeds and generalize models for estimating joint angles in sagittal and frontal planes. LSTM outperformed other models with lower mean absolute error (MAE), lower root mean squared error, and higher R-squared values. The average MAE score was less than 1.138° and 0.939° in sagittal and frontal planes, respectively, when training models for each speed and 2.15° and 1.14° when trained and evaluated for all speeds. These results indicate wearable smart socks to generalize foot-ankle kinematics over various walking speeds with relatively low error and could consequently be used to measure gait parameters without the need for a lab-constricted motion capture system.
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Affiliation(s)
- Samaneh Davarzani
- Department of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS, USA
- Human Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA
| | - David Saucier
- Human Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA
| | - Purva Talegaonkar
- Department of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS, USA
- Human Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA
| | - Erin Parker
- Department of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS, USA
- Human Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA
- Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA
| | - Alana Turner
- Human Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA
- Department of Kinesiology, Mississippi State University, Mississippi State, MS, USA
| | - Carver Middleton
- Human Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA
- Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA
| | - Will Carroll
- Human Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA
- Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA
| | - John E. Ball
- Human Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA
- Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA
| | - Ali Gurbuz
- Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA
| | - Harish Chander
- Human Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA
- Department of Kinesiology, Mississippi State University, Mississippi State, MS, USA
| | - Reuben F. Burch
- Department of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS, USA
- Human Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA
| | - Brian K. Smith
- Department of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS, USA
- Human Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA
| | - Adam Knight
- Human Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA
- Department of Kinesiology, Mississippi State University, Mississippi State, MS, USA
| | - Charles Freeman
- Human Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA
- School of Human Sciences, Mississippi State University, Mississippi State, MS, USA
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Persons AK, Middleton C, Parker E, Carroll W, Turner A, Talegaonkar P, Davarzani S, Saucier D, Chander H, Ball JE, Elder SH, Simpson CL, Macias D, Burch V. RF. Comparison of the Capacitance of a Cyclically Fatigued Stretch Sensor to a Non-Fatigued Stretch Sensor When Performing Static and Dynamic Foot-Ankle Motions. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22218168. [PMID: 36365868 PMCID: PMC9661536 DOI: 10.3390/s22218168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 10/19/2022] [Accepted: 10/20/2022] [Indexed: 05/26/2023]
Abstract
Motion capture is the current gold standard for assessing movement of the human body, but laboratory settings do not always mimic the natural terrains and movements encountered by humans. To overcome such limitations, a smart sock that is equipped with stretch sensors is being developed to record movement data outside of the laboratory. For the smart sock stretch sensors to provide valuable feedback, the sensors should have durability of both materials and signal. To test the durability of the stretch sensors, the sensors were exposed to high-cycle fatigue testing with simultaneous capture of the capacitance. Following randomization, either the fatigued sensor or an unfatigued sensor was placed in the plantarflexion position on the smart sock, and participants were asked to complete the following static movements: dorsiflexion, inversion, eversion, and plantarflexion. Participants were then asked to complete gait trials. The sensor was then exchanged for either an unfatigued or fatigued plantarflexion sensor, depending upon which sensor the trials began with, and each trial was repeated by the participant using the opposite sensor. Results of the tests show that for both the static and dynamic movements, the capacitive output of the fatigued sensor was consistently higher than that of the unfatigued sensor suggesting that an upwards drift of the capacitance was occurring in the fatigued sensors. More research is needed to determine whether stretch sensors should be pre-stretched prior to data collection, and to also determine whether the drift stabilizes once the cyclic softening of the materials comprising the sensor has stabilized.
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Affiliation(s)
- Andrea Karen Persons
- The Ohio State Wexner Medical Center, Jameson Crane Sports Medicine Institute, Columbus, OH 43202, USA
| | - Carver Middleton
- Department of Human Factors & Athlete Engineering, Human Performance Lab, Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS 39759, USA
- Department of Electrical & Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
| | - Erin Parker
- Department of Human Factors & Athlete Engineering, Human Performance Lab, Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS 39759, USA
- Department of Electrical & Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
| | - Will Carroll
- Department of Human Factors & Athlete Engineering, Human Performance Lab, Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS 39759, USA
- Department of Electrical & Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
| | - Alana Turner
- Department of Human Factors & Athlete Engineering, Human Performance Lab, Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS 39759, USA
- Department of Kinesiology, Mississippi State University, Starkville, MS 39762, USA
| | - Purva Talegaonkar
- Department of Human Factors & Athlete Engineering, Human Performance Lab, Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS 39759, USA
- Department of Industrial & Systems Engineering, Mississippi State University, Starkville, MS 39762, USA
| | - Samaneh Davarzani
- Department of Human Factors & Athlete Engineering, Human Performance Lab, Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS 39759, USA
- Department of Industrial & Systems Engineering, Mississippi State University, Starkville, MS 39762, USA
| | - David Saucier
- Department of Human Factors & Athlete Engineering, Human Performance Lab, Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS 39759, USA
| | - Harish Chander
- Department of Human Factors & Athlete Engineering, Human Performance Lab, Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS 39759, USA
- Department of Kinesiology, Mississippi State University, Starkville, MS 39762, USA
| | - John E. Ball
- Department of Human Factors & Athlete Engineering, Human Performance Lab, Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS 39759, USA
- Department of Electrical & Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
| | - Steven H. Elder
- Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS 39762, USA
| | - Chartrisa LaShan Simpson
- Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS 39762, USA
| | - David Macias
- OrthoVirginia, 1920 Ballenger Ave., Alexandria, VA 22314, USA
| | - Reuben F. Burch V.
- Department of Human Factors & Athlete Engineering, Human Performance Lab, Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS 39759, USA
- Department of Industrial & Systems Engineering, Mississippi State University, Starkville, MS 39762, USA
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Vu LQ, Kim H, Schulze LJH, Rajulu SL. Evaluating Lumbar Shape Deformation With Fabric Strain Sensors. HUMAN FACTORS 2022; 64:649-661. [PMID: 33121286 DOI: 10.1177/0018720820965302] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
OBJECTIVE To better study human motion inside the space suit and suit-related contact, a multifactor statistical model was developed to predict torso body shape changes and lumbar motion during suited movement by using fabric strain sensors that are placed on the body. BACKGROUND Physical interactions within pressurized space suits can pose an injury risk for astronauts during extravehicular activity (EVA). In particular, poor suit fit can result in an injury due to reduced performance capabilities and excessive body contact within the suit during movement. A wearable solution is needed to measure body motion inside the space suit. METHODS An array of flexible strain sensors was attached to the body of 12 male study participants. The participants performed specific static lumbar postures while 3D body scans and sensor measurements were collected. A model was created to predict the body shape as a function of sensor signal and the accuracy was evaluated using holdout cross-validation. RESULTS Predictions from the torso shape model had an average root mean square error (RMSE) of 2.02 cm. Subtle soft tissue deformations such as skin folding and bulges were accurately replicated in the shape prediction. Differences in posture type did not affect the prediction error. CONCLUSION This method provides a useful tool for suited testing and the information gained will drive the development of injury countermeasures and improve suit fit assessments. APPLICATION In addition to space suit design applications, this technique can provide a lightweight and wearable system to perform ergonomic evaluations in field assessments.
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Affiliation(s)
- Linh Q Vu
- 43834 MEI Technologies, Houston, Texas, USA
| | - Han Kim
- 43834 Leidos Innovations, Houston, Texas, USA
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Non-Contact Smart Sensing of Physical Activities during Quarantine Period Using SDR Technology. SENSORS 2022; 22:s22041348. [PMID: 35214253 PMCID: PMC8963039 DOI: 10.3390/s22041348] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/06/2022] [Accepted: 02/07/2022] [Indexed: 02/04/2023]
Abstract
The global pandemic of the coronavirus disease (COVID-19) is dramatically changing the lives of humans and results in limitation of activities, especially physical activities, which lead to various health issues such as cardiovascular, diabetes, and gout. Physical activities are often viewed as a double-edged sword. On the one hand, it offers enormous health benefits; on the other hand, it can cause irreparable damage to health. Falls during physical activities are a significant cause of fatal and non-fatal injuries. Therefore, continuous monitoring of physical activities is crucial during the quarantine period to detect falls. Even though wearable sensors can detect and recognize human physical activities, in a pandemic crisis, it is not a realistic approach. Smart sensing with the support of smartphones and other wireless devices in a non-contact manner is a promising solution for continuously monitoring physical activities and assisting patients suffering from serious health issues. In this research, a non-contact smart sensing through the walls (TTW) platform is developed to monitor human physical activities during the quarantine period using software-defined radio (SDR) technology. The developed platform is intelligent, flexible, portable, and has multi-functional capabilities. The received orthogonal frequency division multiplexing (OFDM) signals with fine-grained 64-subcarriers wireless channel state information (WCSI) are exploited for classifying different activities by applying machine learning algorithms. The fall activity is classified separately from standing, walking, running, and bending with an accuracy of 99.7% by using a fine tree algorithm. This preliminary smart sensing opens new research directions to detect COVID-19 symptoms and monitor non-communicable and communicable diseases.
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Phan PK, Vo ATN, Bakhtiarydavijani A, Burch R, Smith B, Ball JE, Chander H, Knight A, Prabhu RK. In Silico Finite Element Analysis of the Foot Ankle Complex Biomechanics: A Literature Review. J Biomech Eng 2021; 143:1105251. [PMID: 33764401 DOI: 10.1115/1.4050667] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Indexed: 11/08/2022]
Abstract
Computational approaches, especially finite element analysis (FEA), have been rapidly growing in both academia and industry during the last few decades. FEA serves as a powerful and efficient approach for simulating real-life experiments, including industrial product development, machine design, and biomedical research, particularly in biomechanics and biomaterials. Accordingly, FEA has been a "go-to" high biofidelic software tool to simulate and quantify the biomechanics of the foot-ankle complex, as well as to predict the risk of foot and ankle injuries, which are one of the most common musculoskeletal injuries among physically active individuals. This paper provides a review of the in silico FEA of the foot-ankle complex. First, a brief history of computational modeling methods and finite element (FE) simulations for foot-ankle models is introduced. Second, a general approach to build an FE foot and ankle model is presented, including a detailed procedure to accurately construct, calibrate, verify, and validate an FE model in its appropriate simulation environment. Third, current applications, as well as future improvements of the foot and ankle FE models, especially in the biomedical field, are discussed. Finally, a conclusion is made on the efficiency and development of FEA as a computational approach in investigating the biomechanics of the foot-ankle complex. Overall, this review integrates insightful information for biomedical engineers, medical professionals, and researchers to conduct more accurate research on the foot-ankle FE models in the future.
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Affiliation(s)
- P K Phan
- Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi, MS 39762; Center of Advanced Vehicular System (CAVS), Mississippi State University, Mississippi, MS 39762
| | - A T N Vo
- Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi, MS 39762; Center of Advanced Vehicular System (CAVS), Mississippi State University, Mississippi, MS 39762
| | - A Bakhtiarydavijani
- Center of Advanced Vehicular System (CAVS), Mississippi State University, Mississippi, MS 39762
| | - R Burch
- Center of Advanced Vehicular System (CAVS), Mississippi State University, Mississippi, MS 39762; Department of Industrial and Systems Engineering, Mississippi State University, Mississippi, MS 39762
| | - B Smith
- Department of Industrial and Systems Engineering, Mississippi State University, Mississippi, MS 39762
| | - J E Ball
- Department of Electrical and Computer Engineering, Mississippi State University, Mississippi, MS 39762
| | - H Chander
- Department of Kinesiology, Mississippi State University, Mississippi, MS 39762
| | - A Knight
- Department of Kinesiology, Mississippi State University, Mississippi, MS 39762
| | - R K Prabhu
- Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi, MS 39762; Center of Advanced Vehicular System (CAVS), Mississippi State University, Mississippi, MS 39762
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Cheng Y, Wang K, Xu H, Li T, Jin Q, Cui D. Recent developments in sensors for wearable device applications. Anal Bioanal Chem 2021; 413:6037-6057. [PMID: 34389877 DOI: 10.1007/s00216-021-03602-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 07/26/2021] [Accepted: 08/04/2021] [Indexed: 01/23/2023]
Abstract
Wearable devices are a new means of human-computer interaction with different functions, underlying principles, and forms. They have been widely used in the medical and health fields, in applications including physiological signal monitoring; sports; and environmental detection, while subtly affecting people's lives and work. Wearable sensors as functional components of wearable devices have become a research focus. In this review, we systematically summarize recent progress in the development of wearable sensors and related devices. Wearable sensors in medical health applications, according to the principle of measurement, are divided into physical and chemical quantity detection. These sensors can monitor and measure specific parameters, thereby enabling continuously improvements in the quality and feasibility of medical treatment. Through the detection of human movement, such as breathing, heartbeat, or bending, wearable sensors can evaluate body movement and monitor an individual's physical performance and health status. Wearable devices detecting aspects of the environment while maintaining high adaptability to the human body can be used to evaluate environmental quality and obtain more accurate environmental information. The ultimate goal of this review is to provide new insights and directions for the future development and broader application of wearable devices in various fields.Graphical abstract.
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Affiliation(s)
- Yuemeng Cheng
- Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Engineering Research Center for Intelligent diagnosis and treatment instrument, Key Laboratory of Thin Film and Microfabrication Technology (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Kan Wang
- Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Engineering Research Center for Intelligent diagnosis and treatment instrument, Key Laboratory of Thin Film and Microfabrication Technology (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Hao Xu
- School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Tangan Li
- Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Engineering Research Center for Intelligent diagnosis and treatment instrument, Key Laboratory of Thin Film and Microfabrication Technology (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Qinghui Jin
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.,Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China
| | - Daxiang Cui
- Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Engineering Research Center for Intelligent diagnosis and treatment instrument, Key Laboratory of Thin Film and Microfabrication Technology (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200240, China
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Gait Analysis Accuracy Difference with Different Dimensions of Flexible Capacitance Sensors. SENSORS 2021; 21:s21165299. [PMID: 34450739 PMCID: PMC8401030 DOI: 10.3390/s21165299] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 07/29/2021] [Accepted: 08/04/2021] [Indexed: 11/17/2022]
Abstract
Stroke causes neurological pathologies, including gait pathologies, which are diagnosed by gait analysis. However, existing gait analysis devices are difficult to use in situ or are disrupted by external conditions. To overcome these drawbacks, a flexible capacitance sensor was developed in this study. To date, a performance comparison of flexible sensors with different dimensions has not been carried out. The aim of this study was to provide optimized sensor dimension information for gait analysis. To accomplish this, sensors with seven different dimensions were fabricated. The dimensions of the sensors were based on the average body size and movement range of 20- to 59-year-old adults. The sensors were characterized by 100 oscillations. The minimum hysteresis error was 8%. After that, four subjects were equipped with the sensor and walked on a treadmill at a speed of 3.6 km/h. All walking processes were filmed at 50 fps and analyzed in Kinovea. The RMS error was calculated using the same frame rate of the video and the sampling rate of the signal from the sensor. The smallest RMS error between the sensor data and the ankle angle was 3.13° using the 49 × 8 mm sensor. In this study, we confirm the dimensions of the sensor with the highest gait analysis accuracy; therefore, the results can be used to make decisions regarding sensor dimensions.
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Persons AK, Ball JE, Freeman C, Macias DM, Simpson CL, Smith BK, Burch V. RF. Fatigue Testing of Wearable Sensing Technologies: Issues and Opportunities. MATERIALS (BASEL, SWITZERLAND) 2021; 14:4070. [PMID: 34361264 PMCID: PMC8347841 DOI: 10.3390/ma14154070] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 07/09/2021] [Accepted: 07/16/2021] [Indexed: 12/23/2022]
Abstract
Standards for the fatigue testing of wearable sensing technologies are lacking. The majority of published fatigue tests for wearable sensors are performed on proof-of-concept stretch sensors fabricated from a variety of materials. Due to their flexibility and stretchability, polymers are often used in the fabrication of wearable sensors. Other materials, including textiles, carbon nanotubes, graphene, and conductive metals or inks, may be used in conjunction with polymers to fabricate wearable sensors. Depending on the combination of the materials used, the fatigue behaviors of wearable sensors can vary. Additionally, fatigue testing methodologies for the sensors also vary, with most tests focusing only on the low-cycle fatigue (LCF) regime, and few sensors are cycled until failure or runout are achieved. Fatigue life predictions of wearable sensors are also lacking. These issues make direct comparisons of wearable sensors difficult. To facilitate direct comparisons of wearable sensors and to move proof-of-concept sensors from "bench to bedside", fatigue testing standards should be established. Further, both high-cycle fatigue (HCF) and failure data are needed to determine the appropriateness in the use, modification, development, and validation of fatigue life prediction models and to further the understanding of how cracks initiate and propagate in wearable sensing technologies.
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Affiliation(s)
- Andrea Karen Persons
- Department of Agricultural and Biological Engineering, Mississippi State University, 130 Creelman Street, Starkville, MS 39762, USA; (A.K.P.); (C.L.S.)
- Human Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, 200 Research Boulevard, Starkville, MS 39759, USA;
| | - John E. Ball
- Human Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, 200 Research Boulevard, Starkville, MS 39759, USA;
- Department of Electrical and Computer Engineering, Mississippi State University, 406 Hardy Road, Starkville, MS 39762, USA
| | - Charles Freeman
- School of Human Sciences, Mississippi State University, 255 Tracy Drive, Starkville, MS 39762, USA;
| | - David M. Macias
- Department of Kinesiology, Mississippi State University, P.O. Box 6186, Starkville, MS 39762, USA;
- Columbus Orthopaedic Clinic, 670 Leigh Drive, Columbus, MS 39705, USA
| | - Chartrisa LaShan Simpson
- Department of Agricultural and Biological Engineering, Mississippi State University, 130 Creelman Street, Starkville, MS 39762, USA; (A.K.P.); (C.L.S.)
| | - Brian K. Smith
- Department of Industrial and Systems Engineering, Mississippi State University, 479-2 Hardy Road, Starkville, MS 39762, USA;
| | - Reuben F. Burch V.
- Human Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, 200 Research Boulevard, Starkville, MS 39759, USA;
- Department of Industrial and Systems Engineering, Mississippi State University, 479-2 Hardy Road, Starkville, MS 39762, USA;
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10
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Closing the Wearable Gap-Part VII: A Retrospective of Stretch Sensor Tool Kit Development for Benchmark Testing. ELECTRONICS 2020. [DOI: 10.3390/electronics9091457] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This paper presents a retrospective of the benchmark testing methodologies developed and accumulated into the stretch sensor tool kit (SSTK) by the research team during the Closing the Wearable Gap series of studies. The techniques developed to validate stretchable soft robotic sensors (SRS) as a means for collecting human kinetic and kinematic data at the foot-ankle complex and at the wrist are reviewed. Lessons learned from past experiments are addressed, as well as what comprises the current SSTK based on what the researchers learned over the course of multiple studies. Three core components of the SSTK are featured: (a) material testing tools, (b) data analysis software, and (c) data collection devices. Results collected indicate that the stretch sensors are a viable means for predicting kinematic data based on the most recent gait analysis study conducted by the researchers (average root mean squared error or RMSE = 3.63°). With the aid of SSTK defined in this study summary and shared with the academic community on GitHub, researchers will be able to undergo more rigorous validation methodologies of SRS validation. A summary of the current state of the SSTK is detailed and includes insight into upcoming experiments that will utilize more sophisticated techniques for fatigue testing and gait analysis, utilizing SRS as the data collection solution.
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11
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Chander H, Burch RF, Talegaonkar P, Saucier D, Luczak T, Ball JE, Turner A, Kodithuwakku Arachchige SNK, Carroll W, Smith BK, Knight A, Prabhu RK. Wearable Stretch Sensors for Human Movement Monitoring and Fall Detection in Ergonomics. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17103554. [PMID: 32438649 PMCID: PMC7277680 DOI: 10.3390/ijerph17103554] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Revised: 05/15/2020] [Accepted: 05/16/2020] [Indexed: 11/16/2022]
Abstract
Wearable sensors are beneficial for continuous health monitoring, movement analysis, rehabilitation, evaluation of human performance, and for fall detection. Wearable stretch sensors are increasingly being used for human movement monitoring. Additionally, falls are one of the leading causes of both fatal and nonfatal injuries in the workplace. The use of wearable technology in the workplace could be a successful solution for human movement monitoring and fall detection, especially for high fall-risk occupations. This paper provides an in-depth review of different wearable stretch sensors and summarizes the need for wearable technology in the field of ergonomics and the current wearable devices used for fall detection. Additionally, the paper proposes the use of soft-robotic-stretch (SRS) sensors for human movement monitoring and fall detection. This paper also recapitulates the findings of a series of five published manuscripts from ongoing research that are published as Parts I to V of “Closing the Wearable Gap” journal articles that discuss the design and development of a foot and ankle wearable device using SRS sensors that can be used for fall detection. The use of SRS sensors in fall detection, its current limitations, and challenges for adoption in human factors and ergonomics are also discussed.
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Affiliation(s)
- Harish Chander
- Neuromechanics Laboratory, Department of Kinesiology, Mississippi State University, Mississippi State, MS 39762, USA; (A.T.); (S.N.K.K.A.); (A.K.)
- Correspondence:
| | - Reuben F. Burch
- Department of Human Factors & Athlete Engineering, Center for Advanced Vehicular Systems (CAVS), Mississippi State University, Mississippi State, MS 39762, USA;
| | - Purva Talegaonkar
- Department of Industrial & Systems Engineering, Mississippi State University, Mississippi State, MS 39762, USA; (P.T.); (B.K.S.)
| | - David Saucier
- Department of Electrical & Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA; (D.S.); (J.E.B.); (W.C.)
| | - Tony Luczak
- National Strategic Planning and Analysis Research Center (NSPARC), Mississippi State University, Mississippi State, MS 39762, USA;
| | - John E. Ball
- Department of Electrical & Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA; (D.S.); (J.E.B.); (W.C.)
| | - Alana Turner
- Neuromechanics Laboratory, Department of Kinesiology, Mississippi State University, Mississippi State, MS 39762, USA; (A.T.); (S.N.K.K.A.); (A.K.)
| | | | - Will Carroll
- Department of Electrical & Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA; (D.S.); (J.E.B.); (W.C.)
| | - Brian K. Smith
- Department of Industrial & Systems Engineering, Mississippi State University, Mississippi State, MS 39762, USA; (P.T.); (B.K.S.)
| | - Adam Knight
- Neuromechanics Laboratory, Department of Kinesiology, Mississippi State University, Mississippi State, MS 39762, USA; (A.T.); (S.N.K.K.A.); (A.K.)
| | - Raj K. Prabhu
- Department of Agricultural and Biomedical Engineering, Mississippi State University, Mississippi State, MS 39762, USA;
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Closing the Wearable Gap—Part VI: Human Gait Recognition Using Deep Learning Methodologies. ELECTRONICS 2020. [DOI: 10.3390/electronics9050796] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
A novel wearable solution using soft robotic sensors (SRS) has been investigated to model foot-ankle kinematics during gait cycles. The capacitance of SRS related to foot-ankle basic movements was quantified during the gait movements of 20 participants on a flat surface as well as a cross-sloped surface. In order to evaluate the power of SRS in modeling foot-ankle kinematics, three-dimensional (3D) motion capture data was also collected for analyzing gait movement. Three different approaches were employed to quantify the relationship between the SRS and the 3D motion capture system, including multivariable linear regression, an artificial neural network (ANN), and a time-series long short-term memory (LSTM) network. Models were compared based on the root mean squared error (RMSE) of the prediction of the joint angle of the foot in the sagittal and frontal plane, collected from the motion capture system. There was not a significant difference between the error rates of the three different models. The ANN resulted in an average RMSE of 3.63, being slightly more successful in comparison to the average RMSE values of 3.94 and 3.98 resulting from multivariable linear regression and LSTM, respectively. The low error rate of the models revealed the high performance of SRS in capturing foot-ankle kinematics during the human gait cycle.
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13
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Luczak T, Burch V RF, Smith BK, Carruth DW, Lamberth J, Chander H, Knight A, Ball J, Prabhu R. Closing the Wearable Gap-Part V: Development of a Pressure-Sensitive Sock Utilizing Soft Sensors. SENSORS (BASEL, SWITZERLAND) 2019; 20:E208. [PMID: 31905941 PMCID: PMC6982705 DOI: 10.3390/s20010208] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 12/19/2019] [Accepted: 12/27/2019] [Indexed: 01/24/2023]
Abstract
The purpose of this study was to evaluate the use of compressible soft robotic sensors (C-SRS) in determining plantar pressure to infer vertical and shear forces in wearable technology: A ground reaction pressure sock (GRPS). To assess pressure relationships between C-SRS, pressure cells on a BodiTrakTM Vector Plate, and KistlerTM Force Plates, thirteen volunteers performed three repetitions of three different movements: squats, shifting center-of-pressure right to left foot, and shifting toes to heels with C-SRS in both anterior-posterior (A/P) and medial-lateral (M/L) sensor orientations. Pearson correlation coefficient of C-SRS to BodiTrakTM Vector Plate resulted in an average R-value greater than 0.70 in 618/780 (79%) of sensor to cell comparisons. An average R-value greater than 0.90 was seen in C-SRS comparison to KistlerTM Force Plates during shifting right to left. An autoregressive integrated moving average (ARIMA) was conducted to identify and estimate future C-SRS data. No significant differences were seen in sensor orientation. Sensors in the A/P orientation reported a mean R2 value of 0.952 and 0.945 in the M/L sensor orientation, reducing the effectiveness to infer shear forces. Given the high R values, the use of C-SRSs to infer normal pressures appears to make the development of the GRPS feasible.
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Affiliation(s)
- Tony Luczak
- Department of Industrial & Systems Engineering, Mississippi State University, Mississippi State, MS 39762, USA; (R.F.B.V.); (B.K.S.)
| | - Reuben F. Burch V
- Department of Industrial & Systems Engineering, Mississippi State University, Mississippi State, MS 39762, USA; (R.F.B.V.); (B.K.S.)
| | - Brian K. Smith
- Department of Industrial & Systems Engineering, Mississippi State University, Mississippi State, MS 39762, USA; (R.F.B.V.); (B.K.S.)
| | - Daniel W. Carruth
- Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS 39762, USA;
| | - John Lamberth
- Department of Kinesiology, Mississippi State University, Mississippi State, MS 39762, USA; (J.L.); (H.C.); (A.K.)
| | - Harish Chander
- Department of Kinesiology, Mississippi State University, Mississippi State, MS 39762, USA; (J.L.); (H.C.); (A.K.)
| | - Adam Knight
- Department of Kinesiology, Mississippi State University, Mississippi State, MS 39762, USA; (J.L.); (H.C.); (A.K.)
| | - J.E. Ball
- Department of Electrical & Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA;
| | - R.K. Prabhu
- Department of Agricultural & Biological Engineering, Mississippi State University, Mississippi State, MS 39762, USA;
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Closing the Wearable Gap—Part IV: 3D Motion Capture Cameras Versus Soft Robotic Sensors Comparison of Gait Movement Assessment. ELECTRONICS 2019. [DOI: 10.3390/electronics8121382] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The purpose of this study was to use 3D motion capture and stretchable soft robotic sensors (SRS) to collect foot-ankle movement on participants performing walking gait cycles on flat and sloped surfaces. The primary aim was to assess differences between 3D motion capture and a new SRS-based wearable solution. Given the complex nature of using a linear solution to accurately quantify the movement of triaxial joints during a dynamic gait movement, 20 participants performing multiple walking trials were measured. The participant gait data was then upscaled (for the SRS), time-aligned (based on right heel strikes), and smoothed using filtering methods. A multivariate linear model was developed to assess goodness-of-fit based on mean absolute error (MAE; 1.54), root mean square error (RMSE; 1.96), and absolute R2 (R2; 0.854). Two and three SRS combinations were evaluated to determine if similar fit scores could be achieved using fewer sensors. Inversion (based on MAE and RMSE) and plantar flexion (based on R2) sensor removal provided second-best fit scores. Given that the scores indicate a high level of fit, with further development, an SRS-based wearable solution has the potential to measure motion during gait- based tasks with the accuracy of a 3D motion capture system.
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15
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Closing the Wearable Gap—Part III: Use of Stretch Sensors in Detecting Ankle Joint Kinematics During Unexpected and Expected Slip and Trip Perturbations. ELECTRONICS 2019. [DOI: 10.3390/electronics8101083] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Background: An induced loss of balance resulting from a postural perturbation has been reported as the primary source for postural instability leading to falls. Hence; early detection of postural instability with novel wearable sensor-based measures may aid in reducing falls and fall-related injuries. The purpose of the study was to validate the use of a stretchable soft robotic sensor (SRS) to detect ankle joint kinematics during both unexpected and expected slip and trip perturbations. Methods: Ten participants (age: 23.7 ± 3.13 years; height: 170.47 ± 8.21 cm; mass: 82.86 ± 23.4 kg) experienced a counterbalanced exposure of an unexpected slip, an unexpected trip, an expected slip, and an expected trip using treadmill perturbations. Ankle joint kinematics for dorsiflexion and plantarflexion were quantified using three-dimensional (3D) motion capture through changes in ankle joint range of motion and using the SRS through changes in capacitance when stretched due to ankle movements during the perturbations. Results: A greater R-squared and lower root mean square error in the linear regression model was observed in comparing ankle joint kinematics data from motion capture with stretch sensors. Conclusions: Results from the study demonstrated that 71.25% of the trials exhibited a minimal error of less than 4.0 degrees difference from the motion capture system and a greater than 0.60 R-squared value in the linear model; suggesting a moderate to high accuracy and minimal errors in comparing SRS to a motion capture system. Findings indicate that the stretch sensors could be a feasible option in detecting ankle joint kinematics during slips and trips.
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