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Javeed M, Mudawi NA, Alazeb A, Almakdi S, Alotaibi SS, Chelloug SA, Jalal A. Intelligent ADL Recognition via IoT-Based Multimodal Deep Learning Framework. SENSORS (BASEL, SWITZERLAND) 2023; 23:7927. [PMID: 37765984 PMCID: PMC10537500 DOI: 10.3390/s23187927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/25/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023]
Abstract
Smart home monitoring systems via internet of things (IoT) are required for taking care of elders at home. They provide the flexibility of monitoring elders remotely for their families and caregivers. Activities of daily living are an efficient way to effectively monitor elderly people at home and patients at caregiving facilities. The monitoring of such actions depends largely on IoT-based devices, either wireless or installed at different places. This paper proposes an effective and robust layered architecture using multisensory devices to recognize the activities of daily living from anywhere. Multimodality refers to the sensory devices of multiple types working together to achieve the objective of remote monitoring. Therefore, the proposed multimodal-based approach includes IoT devices, such as wearable inertial sensors and videos recorded during daily routines, fused together. The data from these multi-sensors have to be processed through a pre-processing layer through different stages, such as data filtration, segmentation, landmark detection, and 2D stick model. In next layer called the features processing, we have extracted, fused, and optimized different features from multimodal sensors. The final layer, called classification, has been utilized to recognize the activities of daily living via a deep learning technique known as convolutional neural network. It is observed from the proposed IoT-based multimodal layered system's results that an acceptable mean accuracy rate of 84.14% has been achieved.
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Affiliation(s)
- Madiha Javeed
- Department of Computer Science, Air University, E-9, Islamabad 44000, Pakistan;
| | - Naif Al Mudawi
- Department of Computer Science, College of Computer Science and Information System, Najran University, Najran 55461, Saudi Arabia; (N.A.M.); (A.A.); (S.A.)
| | - Abdulwahab Alazeb
- Department of Computer Science, College of Computer Science and Information System, Najran University, Najran 55461, Saudi Arabia; (N.A.M.); (A.A.); (S.A.)
| | - Sultan Almakdi
- Department of Computer Science, College of Computer Science and Information System, Najran University, Najran 55461, Saudi Arabia; (N.A.M.); (A.A.); (S.A.)
| | - Saud S. Alotaibi
- Information Systems Department, Umm Al-Qura University, Makkah 24382, Saudi Arabia;
| | - Samia Allaoua Chelloug
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Ahmad Jalal
- Department of Computer Science, Air University, E-9, Islamabad 44000, Pakistan;
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Diraco G, Rescio G, Caroppo A, Manni A, Leone A. Human Action Recognition in Smart Living Services and Applications: Context Awareness, Data Availability, Personalization, and Privacy. SENSORS (BASEL, SWITZERLAND) 2023; 23:6040. [PMID: 37447889 DOI: 10.3390/s23136040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 06/20/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023]
Abstract
Smart living, an increasingly prominent concept, entails incorporating sophisticated technologies in homes and urban environments to elevate the quality of life for citizens. A critical success factor for smart living services and applications, from energy management to healthcare and transportation, is the efficacy of human action recognition (HAR). HAR, rooted in computer vision, seeks to identify human actions and activities using visual data and various sensor modalities. This paper extensively reviews the literature on HAR in smart living services and applications, amalgamating key contributions and challenges while providing insights into future research directions. The review delves into the essential aspects of smart living, the state of the art in HAR, and the potential societal implications of this technology. Moreover, the paper meticulously examines the primary application sectors in smart living that stand to gain from HAR, such as smart homes, smart healthcare, and smart cities. By underscoring the significance of the four dimensions of context awareness, data availability, personalization, and privacy in HAR, this paper offers a comprehensive resource for researchers and practitioners striving to advance smart living services and applications. The methodology for this literature review involved conducting targeted Scopus queries to ensure a comprehensive coverage of relevant publications in the field. Efforts have been made to thoroughly evaluate the existing literature, identify research gaps, and propose future research directions. The comparative advantages of this review lie in its comprehensive coverage of the dimensions essential for smart living services and applications, addressing the limitations of previous reviews and offering valuable insights for researchers and practitioners in the field.
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Affiliation(s)
- Giovanni Diraco
- National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy
| | - Gabriele Rescio
- National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy
| | - Andrea Caroppo
- National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy
| | - Andrea Manni
- National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy
| | - Alessandro Leone
- National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy
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Lu T, Ji S, Jin W, Yang Q, Luo Q, Ren TL. Biocompatible and Long-Term Monitoring Strategies of Wearable, Ingestible and Implantable Biosensors: Reform the Next Generation Healthcare. SENSORS (BASEL, SWITZERLAND) 2023; 23:2991. [PMID: 36991702 PMCID: PMC10054135 DOI: 10.3390/s23062991] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/31/2022] [Accepted: 01/04/2023] [Indexed: 06/19/2023]
Abstract
Sensors enable the detection of physiological indicators and pathological markers to assist in the diagnosis, treatment, and long-term monitoring of diseases, in addition to playing an essential role in the observation and evaluation of physiological activities. The development of modern medical activities cannot be separated from the precise detection, reliable acquisition, and intelligent analysis of human body information. Therefore, sensors have become the core of new-generation health technologies along with the Internet of Things (IoTs) and artificial intelligence (AI). Previous research on the sensing of human information has conferred many superior properties on sensors, of which biocompatibility is one of the most important. Recently, biocompatible biosensors have developed rapidly to provide the possibility for the long-term and in-situ monitoring of physiological information. In this review, we summarize the ideal features and engineering realization strategies of three different types of biocompatible biosensors, including wearable, ingestible, and implantable sensors from the level of sensor designing and application. Additionally, the detection targets of the biosensors are further divided into vital life parameters (e.g., body temperature, heart rate, blood pressure, and respiratory rate), biochemical indicators, as well as physical and physiological parameters based on the clinical needs. In this review, starting from the emerging concept of next-generation diagnostics and healthcare technologies, we discuss how biocompatible sensors revolutionize the state-of-art healthcare system unprecedentedly, as well as the challenges and opportunities faced in the future development of biocompatible health sensors.
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Affiliation(s)
- Tian Lu
- School of Integrated Circuit and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Shourui Ji
- School of Integrated Circuit and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Weiqiu Jin
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Qisheng Yang
- School of Integrated Circuit and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Qingquan Luo
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Tian-Ling Ren
- School of Integrated Circuit and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
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Wilkowska W, Offermann J, Colonna L, Florez-Revuelta F, Climent-Pérez P, Mihailidis A, Poli A, Spinsante S, Ziefle M. Interdisciplinary perspectives on privacy awareness in lifelogging technology development. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 14:2291-2312. [PMID: 36530469 PMCID: PMC9742650 DOI: 10.1007/s12652-022-04486-5] [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: 05/25/2021] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
Population aging resulting from demographic changes requires some challenging decisions and necessary steps to be taken by different stakeholders to manage current and future demand for assistance and support. The consequences of population aging can be mitigated to some extent by assisting technologies that can support the autonomous living of older individuals and persons in need of care in their private environments as long as possible. A variety of technical solutions are already available on the market, but privacy protection is a serious, often neglected, issue when using such (assisting) technology. Thus, privacy needs to be thoroughly taken under consideration in this context. In a three-year project PAAL ('Privacy-Aware and Acceptable Lifelogging Services for Older and Frail People'), researchers from different disciplines, such as law, rehabilitation, human-computer interaction, and computer science, investigated the phenomenon of privacy when using assistive lifelogging technologies. In concrete terms, the concept of Privacy by Design was realized using two exemplary lifelogging applications in private and professional environments. A user-centered empirical approach was applied to the lifelogging technologies, investigating the perceptions and attitudes of (older) users with different health-related and biographical profiles. The knowledge gained through the interdisciplinary collaboration can improve the implementation and optimization of assistive applications. In this paper, partners of the PAAL project present insights gained from their cross-national, interdisciplinary work regarding privacy-aware and acceptable lifelogging technologies.
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Affiliation(s)
- Wiktoria Wilkowska
- Human-Computer Interaction Center, RWTH Aachen University, Aachen, Germany
| | - Julia Offermann
- Human-Computer Interaction Center, RWTH Aachen University, Aachen, Germany
| | - Liane Colonna
- Swedish Law and Informatics Research Institute, Stockholm University, Stockholm, Sweden
| | | | - Pau Climent-Pérez
- Department of Computer Technology, University of Alicante, Alicante, Spain
| | - Alex Mihailidis
- Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, Canada
| | - Angelica Poli
- Department of Information Engineering, Marche Polytechnic University, Ancona, Italy
| | - Susanna Spinsante
- Department of Information Engineering, Marche Polytechnic University, Ancona, Italy
| | - Martina Ziefle
- Human-Computer Interaction Center, RWTH Aachen University, Aachen, Germany
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Self-Organizing IoT Device-Based Smart Diagnosing Assistance System for Activities of Daily Living. SENSORS 2021; 21:s21030785. [PMID: 33503949 PMCID: PMC7866208 DOI: 10.3390/s21030785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 01/20/2021] [Accepted: 01/21/2021] [Indexed: 11/24/2022]
Abstract
Activity of daily living (ADL) is a criterion for evaluating the performance ability of daily life by recognizing various activity events occurring in real life. However, most of the data necessary for ADL evaluation are collected only through observation and questionnaire by the patient or the patient’s caregiver. Recently, Internet of Things (IoT) device studies using various environmental sensors are being used for ADL collection and analysis. In this paper, we propose an IoT Device Platform for ADL capability measurement. Wearable devices and stationary devices recognize activity events in real environments and perform user identification through various sensors. The user’s ADL data are sent to the network hub for analysis. The proposed IoT platform devices support many sensor devices such as acceleration, flame, temperature, and humidity in order to recognize various activities in real life. In addition, in this paper, using the implemented platform, ADL measurement test was performed on hospital patients. Through this test, the accuracy and reliability of the platform are analyzed.
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Wojtusiak J, Asadzadehzanjani N, Levy C, Alemi F, Williams AE. Computational Barthel Index: an automated tool for assessing and predicting activities of daily living among nursing home patients. BMC Med Inform Decis Mak 2021; 21:17. [PMID: 33422059 PMCID: PMC7796534 DOI: 10.1186/s12911-020-01368-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 12/08/2020] [Indexed: 12/12/2022] Open
Abstract
Background Assessment of functional ability, including activities of daily living (ADLs), is a manual process completed by skilled health professionals. In the presented research, an automated decision support tool, the Computational Barthel Index Tool (CBIT), was constructed that can automatically assess and predict probabilities of current and future ADLs based on patients’ medical history. Methods The data used to construct the tool include the demographic information, inpatient and outpatient diagnosis codes, and reported disabilities of 181,213 residents of the Department of Veterans Affairs’ (VA) Community Living Centers. Supervised machine learning methods were applied to construct the CBIT. Temporal information about times from the first and the most recent occurrence of diagnoses was encoded. Ten-fold cross-validation was used to tune hyperparameters, and independent test sets were used to evaluate models using AUC, accuracy, recall and precision. Random forest achieved the best model quality. Models were calibrated using isotonic regression. Results The unabridged version of CBIT uses 578 patient characteristics and achieved average AUC of 0.94 (0.93–0.95), accuracy of 0.90 (0.89–0.91), precision of 0.91 (0.89–0.92), and recall of 0.90 (0.84–0.95) when re-evaluating patients. CBIT is also capable of predicting ADLs up to one year ahead, with accuracy decreasing over time, giving average AUC of 0.77 (0.73–0.79), accuracy of 0.73 (0.69–0.80), precision of 0.74 (0.66–0.81), and recall of 0.69 (0.34–0.96). A simplified version of CBIT with 50 top patient characteristics reached performance that does not significantly differ from full CBIT. Conclusion Discharge planners, disability application reviewers and clinicians evaluating comparative effectiveness of treatments can use CBIT to assess and predict information on functional status of patients.
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Affiliation(s)
- Janusz Wojtusiak
- Health Informatics Program, Department of Health Administration and Policy, George Mason University, Fairfax, VA, USA.
| | - Negin Asadzadehzanjani
- Health Informatics Program, Department of Health Administration and Policy, George Mason University, Fairfax, VA, USA
| | - Cari Levy
- Department of Veterans Affairs, Denver, CO, USA
| | - Farrokh Alemi
- Health Informatics Program, Department of Health Administration and Policy, George Mason University, Fairfax, VA, USA
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Simões WCSS, Machado GS, Sales AMA, de Lucena MM, Jazdi N, de Lucena VF. A Review of Technologies and Techniques for Indoor Navigation Systems for the Visually Impaired. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3935. [PMID: 32679720 PMCID: PMC7411868 DOI: 10.3390/s20143935] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 06/30/2020] [Accepted: 07/01/2020] [Indexed: 11/18/2022]
Abstract
Technologies and techniques of location and navigation are advancing, allowing greater precision in locating people in complex and challenging conditions. These advances have attracted growing interest from the scientific community in using indoor positioning systems (IPSs) with a higher degree of precision and fast delivery time, for groups of people such as the visually impaired, to some extent improving their quality of life. Much research brings together various works that deal with the physical and logical approaches of IPSs to give the reader a more general view of the models. These surveys, however, need to be continuously revisited to update the literature on the features described. This paper presents an expansion of the range of technologies and methodologies for assisting the visually impaired in previous works, providing readers and researchers with a more recent version of what was done and the advantages and disadvantages of each approach to guide reviews and discussions about these topics. Finally, we discuss a series of considerations and future trends for the construction of indoor navigation and location systems for the visually impaired.
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Affiliation(s)
- Walter C. S. S. Simões
- PPGI/ICOMP—Programa de Pós-Graduação em Informática, Institute of Computing, UFAM—Federal University of Amazonas, Manaus, AM 69080-900, Brazil;
| | - Guido S. Machado
- PPGEE—Programa de Pós-Graduação em Engenharia, Technology College, UFAM—Federal University of Amazonas, Manaus, AM 69080-900, Brazil; (G.S.M.); (A.M.A.S.)
| | - André M. A. Sales
- PPGEE—Programa de Pós-Graduação em Engenharia, Technology College, UFAM—Federal University of Amazonas, Manaus, AM 69080-900, Brazil; (G.S.M.); (A.M.A.S.)
| | - Mateus M. de Lucena
- Software/Hardware Integration Lab, UFSC—Federal University of Santa Catarina, Florianópolis, SC 88040-900, Brazil;
| | - Nasser Jazdi
- Institute of Industrial Automation and Software Systems, The University of Stuttgart, 70550 Stuttgart, Germany;
| | - Vicente F. de Lucena
- PPGI/ICOMP—Programa de Pós-Graduação em Informática, Institute of Computing, UFAM—Federal University of Amazonas, Manaus, AM 69080-900, Brazil;
- PPGEE—Programa de Pós-Graduação em Engenharia, Technology College, UFAM—Federal University of Amazonas, Manaus, AM 69080-900, Brazil; (G.S.M.); (A.M.A.S.)
- CETELI–Sector North of UFAM’s Main Campus, UFAM—Federal University of Amazonas, Manaus, AM 69080-900, Brazil
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