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Kontaxis S, Kanellos F, Ntanis A, Kostikis N, Konitsiotis S, Rigas G. An Inertial-Based Wearable System for Monitoring Vital Signs during Sleep. SENSORS (BASEL, SWITZERLAND) 2024; 24:4139. [PMID: 39000917 PMCID: PMC11244494 DOI: 10.3390/s24134139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 06/17/2024] [Accepted: 06/22/2024] [Indexed: 07/16/2024]
Abstract
This study explores the feasibility of a wearable system to monitor vital signs during sleep. The system incorporates five inertial measurement units (IMUs) located on the waist, the arms, and the legs. To evaluate the performance of a novel framework, twenty-three participants underwent a sleep study, and vital signs, including respiratory rate (RR) and heart rate (HR), were monitored via polysomnography (PSG). The dataset comprises individuals with varying severity of sleep-disordered breathing (SDB). Using a single IMU sensor positioned at the waist, strong correlations of more than 0.95 with the PSG-derived vital signs were obtained. Low inter-participant mean absolute errors of about 0.66 breaths/min and 1.32 beats/min were achieved, for RR and HR, respectively. The percentage of data available for analysis, representing the time coverage, was 98.3% for RR estimation and 78.3% for HR estimation. Nevertheless, the fusion of data from IMUs positioned at the arms and legs enhanced the inter-participant time coverage of HR estimation by over 15%. These findings imply that the proposed methodology can be used for vital sign monitoring during sleep, paving the way for a comprehensive understanding of sleep quality in individuals with SDB.
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Affiliation(s)
| | - Foivos Kanellos
- PD Neurotechnology Ltd., 45500 Ioannina, Greece
- Department of Physiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
| | | | | | - Spyridon Konitsiotis
- University Hospital of Ioannina and Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
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Lloyd DG, Saxby DJ, Pizzolato C, Worsey M, Diamond LE, Palipana D, Bourne M, de Sousa AC, Mannan MMN, Nasseri A, Perevoshchikova N, Maharaj J, Crossley C, Quinn A, Mulholland K, Collings T, Xia Z, Cornish B, Devaprakash D, Lenton G, Barrett RS. Maintaining soldier musculoskeletal health using personalised digital humans, wearables and/or computer vision. J Sci Med Sport 2023:S1440-2440(23)00070-1. [PMID: 37149408 DOI: 10.1016/j.jsams.2023.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 03/27/2023] [Accepted: 04/05/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVES The physical demands of military service place soldiers at risk of musculoskeletal injuries and are major concerns for military capability. This paper outlines the development new training technologies to prevent and manage these injuries. DESIGN Narrative review. METHODS Technologies suitable for integration into next-generation training devices were examined. We considered the capability of technologies to target tissue level mechanics, provide appropriate real-time feedback, and their useability in-the-field. RESULTS Musculoskeletal tissues' health depends on their functional mechanical environment experienced in military activities, training and rehabilitation. These environments result from the interactions between tissue motion, loading, biology, and morphology. Maintaining health of and/or repairing joint tissues requires targeting the "ideal" in vivo tissue mechanics (i.e., loading and strain), which may be enabled by real-time biofeedback. Recent research has shown that these biofeedback technologies are possible by integrating a patient's personalised digital twin and wireless wearable devices. Personalised digital twins are personalised neuromusculoskeletal rigid body and finite element models that work in real-time by code optimisation and artificial intelligence. Model personalisation is crucial in obtaining physically and physiologically valid predictions. CONCLUSIONS Recent work has shown that laboratory-quality biomechanical measurements and modelling can be performed outside the laboratory with a small number of wearable sensors or computer vision methods. The next stage is to combine these technologies into well-designed easy to use products.
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Affiliation(s)
- David G Lloyd
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia.
| | - David J Saxby
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Claudio Pizzolato
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Matthew Worsey
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Laura E Diamond
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Dinesh Palipana
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Medicine, Dentistry and Health, Griffith University, Australia
| | - Matthew Bourne
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Ana Cardoso de Sousa
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Malik Muhammad Naeem Mannan
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Azadeh Nasseri
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Nataliya Perevoshchikova
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Jayishni Maharaj
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Claire Crossley
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Alastair Quinn
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Kyle Mulholland
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Tyler Collings
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Zhengliang Xia
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Bradley Cornish
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Daniel Devaprakash
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; VALD Performance, Australia
| | | | - Rodney S Barrett
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
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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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Han HR. Hybrid Fiber Materials according to the Manufacturing Technology Methods and IOT Materials: A Systematic Review. MATERIALS (BASEL, SWITZERLAND) 2023; 16:1351. [PMID: 36836982 PMCID: PMC9962221 DOI: 10.3390/ma16041351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 02/02/2023] [Accepted: 02/03/2023] [Indexed: 06/18/2023]
Abstract
With the development of convergence technology, the Internet of Things (IoT), and artificial intelligence (AI), there has been increasing interest in the materials industry. In recent years, numerous studies have attempted to identify and explore multi-functional cutting-edge hybrid materials. In this paper, the international literature on the materials used in hybrid fibers and manufacturing technologies were investigated and their future utilization in the industry is predicted. Furthermore, a systematic review is also conducted. This includes sputtering, electrospun nanofibers, 3D (three-dimensional) printing, shape memory, and conductive materials. Sputtering technology is an eco-friendly, intelligent material that does not use water and can be applied as an advantageous military stealth material and electromagnetic blocking material, etc. Electrospinning can be applied to breathable fabrics, toxic chemical resistance, fibrous drug delivery systems, and nanoliposomes, etc. 3D printing can be used in various fields, such as core-sheath fibers and artificial organs, etc. Conductive materials include metal nanowires, polypyrrole, polyaniline, and CNT (Carbon Nano Tube), and can be used in actuators and light-emitting devices. When shape-memory materials deform into a temporary shape, they can return to their original shape in response to external stimuli. This study attempted to examine in-depth hybrid fiber materials and manufacturing technologies.
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Affiliation(s)
- Hye Ree Han
- Department of Beauty Art Care, Graduate School of Dongguk University, Seoul 04620, Republic of Korea
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Lin WTM, Lin BS, Lee IJ, Lee SH. Development of a Smartphone-Based mHealth Platform for Telerehabilitation. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2682-2691. [PMID: 36063516 DOI: 10.1109/tnsre.2022.3204148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Telerehabilitation is becoming increasingly valuable as a method for expanding medical services. The smartphone-based mHealth platform (SMPT) has been developed to provide high-quality remote rehabilitation through a smartphone and inertial measurement units. The SMPT uses smartphone as a main platform with connection to medical backend server to provide telerehabilitation. Patients would be referred to therapists to receive a tutorial of exercise technique prior to conducting their home exercise. Once patients begin their home exercises, they can report any problems instantly through the SMPT. The medical staff can adjust the exercise program according to patient feedback and the data collected by the SMPT. After completing the exercise program, patients visit their clinician for re-evaluation. A Service User Technology Acceptability Questionnaire from both medical professional and public perspective revealed a high level of agreement on enhanced care, increased accessibility, and satisfaction and a moderate level of agreement on the use of this platform as a substitute for traditional rehabilitation. Concerns about privacy and discomfort were low in the medical professional and public groups. Concerns about care personnel were also significantly different between the two groups. The SMPT is a promising system for providing telerehabilitation as an adjunct to traditional rehabilitation, which may result in improved outcomes compared with those achieved when using traditional rehabilitation alone.
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6
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Review of Real-Time Biomechanical Feedback Systems in Sport and Rehabilitation. SENSORS 2022; 22:s22083006. [PMID: 35458991 PMCID: PMC9028061 DOI: 10.3390/s22083006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 04/01/2022] [Accepted: 04/11/2022] [Indexed: 02/04/2023]
Abstract
Real-time biomechanical feedback (BMF) is a relatively new area of research. The potential of using advanced technology to improve motion skills in sport and accelerate physical rehabilitation has been demonstrated in a number of studies. This paper provides a literature review of BMF systems in sports and rehabilitation. Our motivation was to examine the history of the field to capture its evolution over time, particularly how technologies are used and implemented in BMF systems, and to identify the most recent studies showing novel solutions and remarkable implementations. We searched for papers in three research databases: Scopus, Web of Science, and PubMed. The initial search yielded 1167 unique papers. After a rigorous and challenging exclusion process, 144 papers were eventually included in this report. We focused on papers describing applications and systems that implement a complete real-time feedback loop, which must include the use of sensors, real-time processing, and concurrent feedback. A number of research questions were raised, and the papers were studied and evaluated accordingly. We identified different types of physical activities, sensors, modalities, actuators, communications, settings and end users. A subset of the included papers, showing the most perspectives, was reviewed in depth to highlight and present their innovative research approaches and techniques. Real-time BMF has great potential in many areas. In recent years, sensors have been the main focus of these studies, but new types of processing devices, methods, and algorithms, actuators, and communication technologies and protocols will be explored in more depth in the future. This paper presents a broad insight into the field of BMF.
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7
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Alizadeh-Meghrazi M, Sidhu G, Jain S, Stone M, Eskandarian L, Toossi A, Popovic MR. A Mass-Producible Washable Smart Garment with Embedded Textile EMG Electrodes for Control of Myoelectric Prostheses: A Pilot Study. SENSORS (BASEL, SWITZERLAND) 2022; 22:666. [PMID: 35062627 PMCID: PMC8779154 DOI: 10.3390/s22020666] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 01/08/2022] [Accepted: 01/12/2022] [Indexed: 06/14/2023]
Abstract
Electromyography (EMG) is the resulting electrical signal from muscle activity, commonly used as a proxy for users' intent in voluntary control of prosthetic devices. EMG signals are recorded with gold standard Ag/AgCl gel electrodes, though there are limitations in continuous use applications, with potential skin irritations and discomfort. Alternative dry solid metallic electrodes also face long-term usability and comfort challenges due to their inflexible and non-breathable structures. This is critical when the anatomy of the targeted body region is variable (e.g., residual limbs of individuals with amputation), and conformal contact is essential. In this study, textile electrodes were developed, and their performance in recording EMG signals was compared to gel electrodes. Additionally, to assess the reusability and robustness of the textile electrodes, the effect of 30 consumer washes was investigated. Comparisons were made between the signal-to-noise ratio (SNR), with no statistically significant difference, and with the power spectral density (PSD), showing a high correlation. Subsequently, a fully textile sleeve was fabricated covering the forearm, with 14 textile electrodes. For three individuals, an artificial neural network model was trained, capturing the EMG of 7 distinct finger movements. The personalized models were then used to successfully control a myoelectric prosthetic hand.
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Affiliation(s)
- Milad Alizadeh-Meghrazi
- The Institute for Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada;
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network (UHN), Toronto, ON M5G 2A2, Canada
- Myant Inc., Toronto, ON M9W 1B6, Canada; (G.S.); (S.J.); (M.S.); (L.E.); (A.T.)
| | - Gurjant Sidhu
- Myant Inc., Toronto, ON M9W 1B6, Canada; (G.S.); (S.J.); (M.S.); (L.E.); (A.T.)
| | - Saransh Jain
- Myant Inc., Toronto, ON M9W 1B6, Canada; (G.S.); (S.J.); (M.S.); (L.E.); (A.T.)
| | - Michael Stone
- Myant Inc., Toronto, ON M9W 1B6, Canada; (G.S.); (S.J.); (M.S.); (L.E.); (A.T.)
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Ladan Eskandarian
- Myant Inc., Toronto, ON M9W 1B6, Canada; (G.S.); (S.J.); (M.S.); (L.E.); (A.T.)
- Department of Materials Science and Engineering, University of Toronto, Toronto, ON M5S 3E4, Canada
| | - Amirali Toossi
- Myant Inc., Toronto, ON M9W 1B6, Canada; (G.S.); (S.J.); (M.S.); (L.E.); (A.T.)
| | - Milos R. Popovic
- The Institute for Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada;
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network (UHN), Toronto, ON M5G 2A2, Canada
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Daily Living Activity Recognition In-The-Wild: Modeling and Inferring Activity-Aware Human Contexts. ELECTRONICS 2022. [DOI: 10.3390/electronics11020226] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Advancement in smart sensing and computing technologies has provided a dynamic opportunity to develop intelligent systems for human activity monitoring and thus assisted living. Consequently, many researchers have put their efforts into implementing sensor-based activity recognition systems. However, recognizing people’s natural behavior and physical activities with diverse contexts is still a challenging problem because human physical activities are often distracted by changes in their surroundings/environments. Therefore, in addition to physical activity recognition, it is also vital to model and infer the user’s context information to realize human-environment interactions in a better way. Therefore, this research paper proposes a new idea for activity recognition in-the-wild, which entails modeling and identifying detailed human contexts (such as human activities, behavioral environments, and phone states) using portable accelerometer sensors. The proposed scheme offers a detailed/fine-grained representation of natural human activities with contexts, which is crucial for modeling human-environment interactions in context-aware applications/systems effectively. The proposed idea is validated using a series of experiments, and it achieved an average balanced accuracy of 89.43%, which proves its effectiveness.
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Walha R, Lebel K, Gaudreault N, Dagenais P, Cereatti A, Della Croce U, Boissy P. The Accuracy and Precision of Gait Spatio-Temporal Parameters Extracted from an Instrumented Sock during Treadmill and Overground Walking in Healthy Subjects and Patients with a Foot Impairment Secondary to Psoriatic Arthritis. SENSORS 2021; 21:s21186179. [PMID: 34577387 PMCID: PMC8472002 DOI: 10.3390/s21186179] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 08/31/2021] [Accepted: 09/10/2021] [Indexed: 11/16/2022]
Abstract
The objectives of this study were to assess the accuracy and precision of a system combining an IMU-instrumented sock and a validated algorithm for the estimation of the spatio-temporal parameters of gait. A total of 25 healthy participants (HP) and 21 patients with foot impairments secondary to psoriatic arthritis (PsA) performed treadmill walking at three different speeds and overground walking at a comfortable speed. HP performed the assessment over two sessions. The proposed system's estimations of cadence (CAD), gait cycle duration (GCD), gait speed (GS), and stride length (SL) obtained for treadmill walking were validated versus those estimated with a motion capture system. The system was also compared with a well-established multi-IMU-based system for treadmill and overground walking. The results showed a good agreement between the motion capture system and the IMU-instrumented sock in estimating the spatio-temporal parameters during the treadmill walking at normal and fast speeds for both HP and PsA participants. The accuracy of GS and SL obtained from the IMU-instrumented sock was better compared to the established multi-IMU-based system in both groups. The precision (inter-session reliability) of the gait parameter estimations obtained from the IMU-instrumented sock was good to excellent for overground walking and treadmill walking at fast speeds, but moderate-to-good for slow and normal treadmill walking. The proposed IMU-instrumented sock offers a novel form factor addressing the wearability issues of IMUs and could potentially be used to measure spatio-temporal parameters under clinical conditions and free-living conditions.
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Affiliation(s)
- Roua Walha
- Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada; (R.W.); (N.G.); (P.D.)
| | - Karina Lebel
- Research Center on Aging, CIUSSS Estrie CHUS, Sherbrooke, QC J1H 4C4, Canada;
- Faculty of Engineering, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
| | - Nathaly Gaudreault
- Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada; (R.W.); (N.G.); (P.D.)
| | - Pierre Dagenais
- Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada; (R.W.); (N.G.); (P.D.)
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy;
| | - Ugo Della Croce
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy;
- Biomedical Engineering Department, Catholic University of America, Washington, DC 20064, USA
| | - Patrick Boissy
- Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada; (R.W.); (N.G.); (P.D.)
- Research Center on Aging, CIUSSS Estrie CHUS, Sherbrooke, QC J1H 4C4, Canada;
- Correspondence: ; Tel.: +1-819-780-2220 (ext. 45628)
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Lloyd D. The future of in-field sports biomechanics: wearables plus modelling compute real-time in vivo tissue loading to prevent and repair musculoskeletal injuries. Sports Biomech 2021:1-29. [PMID: 34496728 DOI: 10.1080/14763141.2021.1959947] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 07/20/2021] [Indexed: 01/13/2023]
Abstract
This paper explores the use of biomechanics in identifying the mechanistic causes of musculoskeletal tissue injury and degeneration. It appraises how biomechanics has been used to develop training programmes aiming to maintain or recover tissue health. Tissue health depends on the functional mechanical environment experienced by tissues during daily and rehabilitation activities. These environments are the result of the interactions between tissue motion, loading, biology, and morphology. Maintaining health of and/or repairing musculoskeletal tissues requires targeting the "ideal" in vivo tissue mechanics (i.e., loading and deformation), which may be enabled by appropriate real-time biofeedback. Recent research shows that biofeedback technologies may increase their quality and effectiveness by integrating a personalised neuromusculoskeletal modelling driven by real-time motion capture and medical imaging. Model personalisation is crucial in obtaining physically and physiologically valid predictions of tissue biomechanics. Model real-time execution is crucial and achieved by code optimisation and artificial intelligence methods. Furthermore, recent work has also shown that laboratory-based motion capture biomechanical measurements and modelling can be performed outside the laboratory with wearable sensors and artificial intelligence. The next stage is to combine these technologies into well-designed easy to use products to guide training to maintain or recover tissue health in the real-world.
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Affiliation(s)
- David Lloyd
- School of Health Sciences and Social Work, Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), in the Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Griffith University, Australia
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11
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An Overview of Wearable Piezoresistive and Inertial Sensors for Respiration Rate Monitoring. ELECTRONICS 2021. [DOI: 10.3390/electronics10172178] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The demand for wearable devices to measure respiratory activity is constantly growing, finding applications in a wide range of scenarios (e.g., clinical environments and workplaces, outdoors for monitoring sports activities, etc.). Particularly, the respiration rate (RR) is a vital parameter since it indicates serious illness (e.g., pneumonia, emphysema, pulmonary embolism, etc.). Therefore, several solutions have been presented in the scientific literature and on the market to make RR monitoring simple, accurate, reliable and noninvasive. Among the different transduction methods, the piezoresistive and inertial ones satisfactorily meet the requirements for smart wearable devices since unobtrusive, lightweight and easy to integrate. Hence, this review paper focuses on innovative wearable devices, detection strategies and algorithms that exploit piezoresistive or inertial sensors to monitor the breathing parameters. At first, this paper presents a comprehensive overview of innovative piezoresistive wearable devices for measuring user’s respiratory variables. Later, a survey of novel piezoresistive textiles to develop wearable devices for detecting breathing movements is reported. Afterwards, the state-of-art about wearable devices to monitor the respiratory parameters, based on inertial sensors (i.e., accelerometers and gyroscopes), is presented for detecting dysfunctions or pathologies in a non-invasive and accurate way. In this field, several processing tools are employed to extract the respiratory parameters from inertial data; therefore, an overview of algorithms and methods to determine the respiratory rate from acceleration data is provided. Finally, comparative analysis for all the covered topics are reported, providing useful insights to develop the next generation of wearable sensors for monitoring respiratory parameters.
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12
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Liu KC, Chan M, Kuo HC, Hsieh CY, Huang HY, Chan CT, Tsao Y. Domain-Adaptive Fall Detection Using Deep Adversarial Training. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1243-1251. [PMID: 34133280 DOI: 10.1109/tnsre.2021.3089685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Fall detection (FD) systems are important assistive technologies for healthcare that can detect emergency fall events and alert caregivers. However, it is not easy to obtain large-scale annotated fall events with various specifications of sensors or sensor positions during the implementation of accurate FD systems. Moreover, the knowledge obtained through machine learning has been restricted to tasks in the same domain. The mismatch between different domains might hinder the performance of FD systems. Cross-domain knowledge transfer is very beneficial for machine-learning based FD systems to train a reliable FD model with well-labeled data in new environments. In this study, we propose domain-adaptive fall detection (DAFD) using deep adversarial training (DAT) to tackle cross-domain problems, such as cross-position and cross-configuration. The proposed DAFD can transfer knowledge from the source domain to the target domain by minimizing the domain discrepancy to avoid mismatch problems. The experimental results show that the average F1-score improvement when using DAFD ranges from 1.5% to 7% in the cross-position scenario, and from 3.5% to 12% in the cross-configuration scenario, compared to using the conventional FD model without domain adaptation training. The results demonstrate that the proposed DAFD successfully helps to deal with cross-domain problems and to achieve better detection performance.
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13
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Porta M, Kim S, Pau M, Nussbaum MA. Classifying diverse manual material handling tasks using a single wearable sensor. APPLIED ERGONOMICS 2021; 93:103386. [PMID: 33609851 DOI: 10.1016/j.apergo.2021.103386] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 02/04/2021] [Accepted: 02/08/2021] [Indexed: 06/12/2023]
Abstract
The use of inertial measurement units (IMUs) for monitoring and classifying physical activities has received substantial attention in recent years, both in occupational and non-occupational contexts. However, a "user-friendly" approach is needed to promote this approach to quantify physical demands in actual workplaces. We explored the use of a single IMU for extracting information about different manual material handling (MMH) tasks (i.e., specific type of task performed, and associated duration and frequency), using a bidirectional long short-term memory network for classification. Classification performance using single IMUs placed on several body parts was compared with performance using multiple IMU configurations (2, 3, and 17 IMUs). Overall, the use of a single sensor led to satisfactory results (e.g., median accuracy >97%) in classifying MMH tasks and estimating task duration and frequency. Limited benefits were obtained using additional sensors, and several sensor locations yielded similar outcomes. Classification performance, though, was relatively inferior for push/pull vs. other tasks.
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Affiliation(s)
- Micaela Porta
- Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Italy
| | - Sunwook Kim
- Department of Industrial and System Engineering, Virginia Tech, Blacksburg, VA, USA
| | - Massimiliano Pau
- Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Italy
| | - Maury A Nussbaum
- Department of Industrial and System Engineering, Virginia Tech, Blacksburg, VA, USA.
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14
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Association between Self-Reported and Accelerometer-Based Estimates of Physical Activity in Portuguese Older Adults. SENSORS 2021; 21:s21072258. [PMID: 33804834 PMCID: PMC8038119 DOI: 10.3390/s21072258] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 03/11/2021] [Accepted: 03/15/2021] [Indexed: 12/13/2022]
Abstract
Accurate assessment of physical activity (PA) is crucial in interventions promoting it and in studies exploring its association with health status. Currently, there is a wide range of assessment tools available, including subjective and objective measures. This study compared accelerometer-based estimates of PA with self-report PA data in older adults. Additionally, the associations between PA and health outcomes and PA profiles were analyzed. Participants (n = 110) wore a Xiaomi Mi Band 2® for fifteen consecutive days. Self-reported PA was assessed using the International Physical Activity Questionnaire (IPAQ) and the Yale Physical Activity Survey (YPAS). The Spearman correlation coefficient was used to compare self-reported and accelerometer-measured PA and associations between PA and health. Bland–Altman plots were performed to assess the agreement between methods. Results highlight a large variation between self-reported and Xiaomi Mi Band 2® estimates, with poor general agreement. The highest difference was found for sedentary time. Low positive correlations were observed for IPAQ estimates (sedentary, vigorous, and total PA) and moderate for YPAS vigorous estimates. Finally, self-reported and objectively measured PA associated differently with health outcomes. Summarily, although accelerometry has the advantage of being an accurate method, self-report questionnaires could provide valuable information about the context of the activity.
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Ciniglio A, Guiotto A, Spolaor F, Sawacha Z. The Design and Simulation of a 16-Sensors Plantar Pressure Insole Layout for Different Applications: From Sports to Clinics, a Pilot Study. SENSORS (BASEL, SWITZERLAND) 2021; 21:1450. [PMID: 33669674 PMCID: PMC7922081 DOI: 10.3390/s21041450] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 02/08/2021] [Accepted: 02/16/2021] [Indexed: 11/26/2022]
Abstract
The quantification of plantar pressure distribution is widely done in the diagnosis of lower limbs deformities, gait analysis, footwear design, and sport applications. To date, a number of pressure insole layouts have been proposed, with different configurations according to their applications. The goal of this study is to assess the validity of a 16-sensors (1.5 × 1.5 cm) pressure insole to detect plantar pressure distribution during different tasks in the clinic and sport domains. The data of 39 healthy adults, acquired with a Pedar-X® system (Novel GmbH, Munich, Germany) during walking, weight lifting, and drop landing, were used to simulate the insole. The sensors were distributed by considering the location of the peak pressure on all trials: 4 on the hindfoot, 3 on the midfoot, and 9 on the forefoot. The following variables were computed with both systems and compared by estimating the Root Mean Square Error (RMSE): Peak/Mean Pressure, Ground Reaction Force (GRF), Center of Pressure (COP), the distance between COP and the origin, the Contact Area. The lowest (0.61%) and highest (82.4%) RMSE values were detected during gait on the medial-lateral COP and the GRF, respectively. This approach could be used for testing different layouts on various applications prior to production.
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Affiliation(s)
- Alfredo Ciniglio
- Department of Information Engineering, DEI, University of Padova, 35131 Padova, Italy; (A.C.); (A.G.); (F.S.)
| | - Annamaria Guiotto
- Department of Information Engineering, DEI, University of Padova, 35131 Padova, Italy; (A.C.); (A.G.); (F.S.)
| | - Fabiola Spolaor
- Department of Information Engineering, DEI, University of Padova, 35131 Padova, Italy; (A.C.); (A.G.); (F.S.)
| | - Zimi Sawacha
- Department of Information Engineering, DEI, University of Padova, 35131 Padova, Italy; (A.C.); (A.G.); (F.S.)
- Department of Medicine, DIMED, University of Padova, 35131 Padova, Italy
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16
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Kwon S, Wan N, Burns RD, Brusseau TA, Kim Y, Kumar S, Ertin E, Wetter DW, Lam CY, Wen M, Byun W. The Validity of MotionSense HRV in Estimating Sedentary Behavior and Physical Activity under Free-Living and Simulated Activity Settings. SENSORS 2021; 21:s21041411. [PMID: 33670507 PMCID: PMC7922785 DOI: 10.3390/s21041411] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 02/05/2021] [Accepted: 02/10/2021] [Indexed: 12/12/2022]
Abstract
MotionSense HRV is a wrist-worn accelerometery-based sensor that is paired with a smartphone and is thus capable of measuring the intensity, duration, and frequency of physical activity (PA). However, little information is available on the validity of the MotionSense HRV. Therefore, the purpose of this study was to assess the concurrent validity of the MotionSense HRV in estimating sedentary behavior (SED) and PA. A total of 20 healthy adults (age: 32.5 ± 15.1 years) wore the MotionSense HRV and ActiGraph GT9X accelerometer (GT9X) on their non-dominant wrist for seven consecutive days during free-living conditions. Raw acceleration data from the devices were summarized into average time (min/day) spent in SED and moderate-to-vigorous PA (MVPA). Additionally, using the Cosemed K5 indirect calorimetry system (K5) as a criterion measure, the validity of the MotionSense HRV was examined in simulated free-living conditions. Pearson correlations, mean absolute percent errors (MAPE), Bland–Altman (BA) plots, and equivalence tests were used to examine the validity of the MotionSense HRV against criterion measures. The correlations between the MotionSense HRV and GT9X were high and the MAPE were low for both the SED (r = 0.99, MAPE = 2.4%) and MVPA (r = 0.97, MAPE = 9.1%) estimates under free-living conditions. BA plots illustrated that there was no systematic bias between the MotionSense HRV and criterion measures. The estimates of SED and MVPA from the MotionSense HRV were significantly equivalent to those from the GT9X; the equivalence zones were set at 16.5% for SED and 29% for MVPA. The estimates of SED and PA from the MotionSense HRV were less comparable when compared with those from the K5. The MotionSense HRV yielded comparable estimates for SED and PA when compared with the GT9X accelerometer under free-living conditions. We confirmed the promising application of the MotionSense HRV for monitoring PA patterns for practical and research purposes.
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Affiliation(s)
- Sunku Kwon
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT 84112, USA; (S.K.); (R.D.B.); (T.A.B.)
| | - Neng Wan
- Department of Geography, University of Utah, Salt Lake City, UT 84112, USA;
| | - Ryan D. Burns
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT 84112, USA; (S.K.); (R.D.B.); (T.A.B.)
| | - Timothy A. Brusseau
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT 84112, USA; (S.K.); (R.D.B.); (T.A.B.)
| | - Youngwon Kim
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Hong Kong;
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0SL, UK
| | - Santosh Kumar
- Department of Computer Science, University of Memphis, Memphis, TN 38152, USA;
| | - Emre Ertin
- Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA;
| | - David W. Wetter
- Department of Population Health Sciences and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84132, USA; (D.W.W.); (C.Y.L.)
| | - Cho Y. Lam
- Department of Population Health Sciences and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84132, USA; (D.W.W.); (C.Y.L.)
| | - Ming Wen
- Department of Sociology, University of Utah, Salt Lake City, UT 84112, USA;
| | - Wonwoo Byun
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT 84112, USA; (S.K.); (R.D.B.); (T.A.B.)
- Correspondence: ; Tel.: +1-801-585-1119
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17
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Sevil M, Rashid M, Maloney Z, Hajizadeh I, Samadi S, Askari MR, Hobbs N, Brandt R, Park M, Quinn L, Cinar A. Determining Physical Activity Characteristics from Wristband Data for Use in Automated Insulin Delivery Systems. IEEE SENSORS JOURNAL 2020; 20:12859-12870. [PMID: 33100923 PMCID: PMC7584145 DOI: 10.1109/jsen.2020.3000772] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Algorithms that can determine the type of physical activity (PA) and quantify the intensity can allow precision medicine approaches, such as automated insulin delivery systems that modulate insulin administration in response to PA. In this work, data from a multi-sensor wristband is used to design classifiers to distinguish among five different physical states (PS) (resting, activities of daily living, running, biking, and resistance training), and to develop models to estimate the energy expenditure (EE) of the PA for diabetes therapy. The data collected are filtered, features are extracted from the reconciled signals, and the extracted features are used by machine learning algorithms, including deep-learning techniques, to obtain accurate PS classification and EE estimation. The various machine learning techniques have different success rates ranging from 75.7% to 94.8% in classifying the five different PS. The deep neural network model with long short-term memory has 94.8% classification accuracy. We achieved 0.5 MET (Metabolic Equivalent of Task) root-mean-square error for EE estimation accuracy, relative to indirect calorimetry with randomly selected testing data (10% of collected data). We also demonstrate a 5% improvement in PS classification accuracy and a 0.34 MET decrease in the mean absolute error when using multi-sensor approach relative to using only accelerometer data.
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Affiliation(s)
- Mert Sevil
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
| | - Mudassir Rashid
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
| | - Zacharie Maloney
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
| | - Iman Hajizadeh
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
| | - Sediqeh Samadi
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
| | - Mohammad Reza Askari
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
| | - Nicole Hobbs
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
| | - Rachel Brandt
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
| | - Minsun Park
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
| | - Laurie Quinn
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
| | - Ali Cinar
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
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Alanezi MA, Bouchekara HREH, Javaid MS. Optimizing Router Placement of Indoor Wireless Sensor Networks in Smart Buildings for IoT Applications. SENSORS (BASEL, SWITZERLAND) 2020; 20:s20216212. [PMID: 33143362 PMCID: PMC7663200 DOI: 10.3390/s20216212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 10/10/2020] [Accepted: 10/27/2020] [Indexed: 06/11/2023]
Abstract
Internet of Things (IoT) is characterized by a system of interconnected devices capable of communicating with each other to carry out specific useful tasks. The connection between these devices is ensured by routers distributed in a network. Optimizing the placement of these routers in a distributed wireless sensor network (WSN) in a smart building is a tedious task. Computer-Aided Design (CAD) programs and software can simplify this task since they provide a robust and efficient tool. At the same time, experienced engineers from different backgrounds must play a prominent role in the abovementioned task. Therefore, specialized companies rely on both; a useful CAD tool along with the experience and the flair of a sound expert/engineer to optimally place routers in a WSN. This paper aims to develop a new approach based on the interaction between an efficient CAD tool and an experienced engineer for the optimal placement of routers in smart buildings for IoT applications. The approach follows a step-by-step procedure to weave an optimal network infrastructure, having both automatic and designer-intervention modes. Several case studies have been investigated, and the obtained results show that the developed approach produces a synthesized network with full coverage and a reduced number of routers.
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Affiliation(s)
- Mohammed A. Alanezi
- Department of Computer Science and Engineering Technology, University of Hafr Al Batin, Hafr Al Batin 31991, Saudi Arabia;
| | | | - Muhammad S. Javaid
- Department of Electrical Engineering, University of Hafr Al Batin, Hafr Al Batin 31991, Saudi Arabia;
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19
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Aqueveque P, Germany E, Osorio R, Pastene F. Gait Segmentation Method Using a Plantar Pressure Measurement System with Custom-Made Capacitive Sensors. SENSORS (BASEL, SWITZERLAND) 2020; 20:E656. [PMID: 31991637 PMCID: PMC7038314 DOI: 10.3390/s20030656] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 12/17/2019] [Accepted: 12/31/2019] [Indexed: 11/16/2022]
Abstract
Gait analysis has been widely studied by researchers due to the impact in clinical fields. It provides relevant information on the condition of a patient's pathologies. In the last decades, different gait measurement methods have been developed in order to identify parameters that can contribute to gait cycles. Analyzing those parameters, it is possible to segment and identify different phases of gait cycles, making these studies easier and more accurate. This paper proposes a simple gait segmentation method based on plantar pressure measurement. Current methods used by researchers and clinicians are based on multiple sensing devices (e.g., multiple cameras, multiple inertial measurement units (IMUs)). Our proposal uses plantar pressure information from only two sensorized insoles that were designed and implemented with eight custom-made flexible capacitive sensors. An algorithm was implemented to calculate gait parameters and segment gait cycle phases and subphases. Functional tests were performed in six healthy volunteers in a 10 m walking test. The designed in-shoe insole presented an average power consumption of 44 mA under operation. The system segmented the gait phases and sub-phases in all subjects. The calculated percentile distribution between stance phase time and swing phase time was almost 60%/40%, which is aligned with literature reports on healthy subjects. Our results show that the system achieves a successful segmentation of gait phases and subphases, is capable of reporting COP velocity, double support time, cadence, stance phase time percentage, swing phase time percentage, and double support time percentage. The proposed system allows for the simplification of the assessment method in the recovery process for both patients and clinicians.
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Affiliation(s)
- Pablo Aqueveque
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, 219 Edmundo Larenas St., 4030000 Concepción, Chile; (E.G.); (R.O.); (F.P.)
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20
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Yang K, Isaia B, Brown LJE, Beeby S. E-Textiles for Healthy Ageing. SENSORS (BASEL, SWITZERLAND) 2019; 19:E4463. [PMID: 31618875 PMCID: PMC6832571 DOI: 10.3390/s19204463] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 10/04/2019] [Accepted: 10/08/2019] [Indexed: 12/13/2022]
Abstract
The ageing population has grown quickly in the last half century with increased longevity and declining birth rate. This presents challenges to health services and the wider society. This review paper considers different aspects (e.g., physical, mental, and social well-being) of healthy ageing and how health devices can help people to monitor health conditions, treat diseases and promote social interactions. Existing technologies for addressing non-physical (e.g., Alzheimer's, loneliness) and physical (e.g., stroke, bedsores, and fall) related challenges are presented together with the drivers and constraints of using e-textiles for these applications. E-textiles provide a platform that enables unobtrusive and ubiquitous deployment of sensors and actuators for healthy ageing applications. However, constraints remain on battery, integration, data accuracy, manufacturing, durability, ethics/privacy issues, and regulations. These challenges can only effectively be met by interdisciplinary teams sharing expertise and methods, and involving end users and other key stakeholders at an early stage in the research.
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Affiliation(s)
- Kai Yang
- Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK.
| | - Beckie Isaia
- Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK.
| | - Laura J E Brown
- School of Health Sciences, University of Manchester, Manchester M13 9PL, UK.
| | - Steve Beeby
- Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK.
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21
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Mokhlespour Esfahani MI, Nussbaum MA. Classifying Diverse Physical Activities Using "Smart Garments". SENSORS 2019; 19:s19143133. [PMID: 31315261 PMCID: PMC6679301 DOI: 10.3390/s19143133] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 07/11/2019] [Accepted: 07/14/2019] [Indexed: 12/17/2022]
Abstract
Physical activities can have important impacts on human health. For example, a physically active lifestyle, which is one of the most important goals for overall health promotion, can diminish the risk for a range of physical disorders, as well as reducing health-related expenditures. Thus, a long-term goal is to detect different physical activities, and an important initial step toward this goal is the ability to classify such activities. A recent and promising technology to discriminate among diverse physical activities is the smart textile system (STS), which is becoming increasingly accepted as a low-cost activity monitoring tool for health promotion. Accordingly, our primary aim was to assess the feasibility and accuracy of using a novel STS to classify physical activities. Eleven participants completed a lab-based experiment to evaluate the accuracy of an STS that featured a smart undershirt (SUS) and commercially available smart socks (SSs) in discriminating several basic postures (sitting, standing, and lying down), as well as diverse activities requiring participants to walk and run at different speeds. We trained three classification methods—K-nearest neighbor, linear discriminant analysis, and artificial neural network—using data from each smart garment separately and in combination. Overall classification performance (global accuracy) was ~98%, which suggests that the STS was effective for discriminating diverse physical activities. We conclude that, overall, smart garments represent a promising area of research and a potential alternative for discriminating a range of physical activities, which can have positive implications for health promotion.
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Affiliation(s)
| | - Maury A Nussbaum
- Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24060, USA.
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22
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Mokhlespour Esfahani MI, Nussbaum MA. Using smart garments to differentiate among normal and simulated abnormal gaits. J Biomech 2019; 93:70-76. [PMID: 31303330 DOI: 10.1016/j.jbiomech.2019.06.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 05/31/2019] [Accepted: 06/14/2019] [Indexed: 11/25/2022]
Abstract
Detecting and assessing an individual's gait can be important for medical diagnostic purposes and for developing and guiding follow-on rehabilitation protocols. Thus, an accurate, objective gait classification system has the potential to facilitate earlier diagnosis and improved clinical decision-making. Systems using smart garments represent an emerging technology for physical activity assessment and that may be relevant for gait classification. The objective of this study was to assess the accuracy of one such system - comprised of commercial instrumented socks and a custom instrument shirt - for differentiating among normal gait and four distinct simulated gait abnormalities. Eleven participants completed an experiment in which they completed several gait trails on a single day. Gait types were classified using diverse modeling approaches (K-nearest neighbors, linear discriminant analyses, support vector machines, and artificial neural networks). High classification accuracy could be obtained, both when classification models were developed and tested using data from each participant separately and grouped together, particularly using the k-nearest neighbor method (>98% accuracy). Some gaits were more often "confused" with other gaits, especially when they shared underlying kinematic aspects. These results support the potential of using "smart" garments for detecting and identifying abnormal gaits, and for future implementation in diagnosis and rehabilitation.
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Affiliation(s)
| | - Maury A Nussbaum
- Department of Industrial and Systems Engineering, Virginia Tech University, Blacksburg, USA.
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Mokhlespour Esfahani MI, Nussbaum MA, Kong ZJ. Using a smart textile system for classifying occupational manual material handling tasks: evidence from lab-based simulations. ERGONOMICS 2019; 62:823-833. [PMID: 30716019 DOI: 10.1080/00140139.2019.1578419] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Accepted: 01/25/2019] [Indexed: 06/09/2023]
Abstract
Physical monitoring systems represent potentially powerful assessment devices to detect and describe occupational physical activities. A promising technology for such use is smart textile systems (STSs). Our goal in this exploratory study was to assess the feasibility and accuracy of using two STSs to classify several manual material handling (MMH) tasks. Specifically, commercially-available 'smart' socks and a custom 'smart' shirt were used individually and in combination. Eleven participants simulated nine separate MMH tasks while wearing the STSs, and task classification accuracy was quantified subsequently using several common models. The shirt and socks, both individually and in combination, could classify the simulated tasks with greater than 97% accuracy. Thus, using STSs appears to have potential utility for discriminating occupational physical tasks in the work environment. Practitioner summary: A smart textile system could classify diverse MMH tasks with high accuracy. This technology may help in developing future ergonomic exposure assessment systems, with the goal of preventing occupational injuries.
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Affiliation(s)
| | - Maury A Nussbaum
- b Department of Industrial and Systems Engineering , Virginia Tech University , Blacksburg , VA , USA
| | - Zhenyu James Kong
- b Department of Industrial and Systems Engineering , Virginia Tech University , Blacksburg , VA , USA
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Godfrey A, Brodie M, van Schooten KS, Nouredanesh M, Stuart S, Robinson L. Inertial wearables as pragmatic tools in dementia. Maturitas 2019; 127:12-17. [PMID: 31351515 DOI: 10.1016/j.maturitas.2019.05.010] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 05/22/2019] [Accepted: 05/23/2019] [Indexed: 01/02/2023]
Abstract
Dementia is a critically important issue due to its wide impact on health services as well as its personal and societal costs. Limitations exist for current dementia protocols, and there are calls to introduce modern technology that facilitates the addition of digital biomarkers to routine clinical practice. Wearable technology (wearables) are nearly ubiquitous in everyday life, gathering discrete and continuous digital data on habitual activities, but their utility in modern medicine remains low. Due to advances in data analytics, wearables are now commonly discussed as pragmatic tools to aid the diagnosis and treatment of a range of neurological disorders. Inertial sensor-based wearables are one such technology; they offer a low-cost approach to quantify routine movements that are fundamental to normal activities of daily living, most notably postural control and gait. Here, we provide a narrative review of how wearables are providing useful postural control and gait data to facilitate the capture of digital markers to aid dementia research. We outline the history of wearables, from their humble beginnings to their current use beyond the clinic, and explore their integration into modern systems, as well as the ongoing standardisation and regulatory efforts to integrate their use in clinical trials.
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Affiliation(s)
- A Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle, UK.
| | - M Brodie
- Falls Balance & Injury Research Centre, Neuroscience Research Australia, NSW, Australia; Graduate School of Biomedical Engineering, University of New South Wales, NSW, Australia
| | - K S van Schooten
- Neuroscience Research Australia, University of New South Wales, Sydney, Australia; School of Public Health and Community Medicine, University of New South Wales, NSW, Australia
| | - M Nouredanesh
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Canada
| | - S Stuart
- Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - L Robinson
- Institute for Ageing, Newcastle University, Newcastle upon Tyne, UK
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A Textile Sensor for Long Durations of Human Motion Capture. SENSORS 2019; 19:s19102369. [PMID: 31126023 PMCID: PMC6566426 DOI: 10.3390/s19102369] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 05/13/2019] [Accepted: 05/18/2019] [Indexed: 11/18/2022]
Abstract
Human posture and movement analysis is important in the areas of rehabilitation, sports medicine, and virtual training. However, the development of sensors with good accuracy, low cost, light weight, and suitability for long durations of human motion capture is still an ongoing issue. In this paper, a new flexible textile sensor for knee joint movement measurements was developed by using ordinary fabrics and conductive yarns. An electrogoniometer was adopted as a standard reference to calibrate the proposed sensor and validate its accuracy. The knee movements of different daily activities were performed to evaluate the performance of the sensor. The results show that the proposed sensor could be used to monitor knee joint motion in everyday life with acceptable accuracy.
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Sharif-Human movement instrumentation system (SHARIF-HMIS): Development and validation. Med Eng Phys 2018; 61:87-94. [PMID: 30181023 DOI: 10.1016/j.medengphy.2018.07.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Revised: 07/21/2018] [Accepted: 07/24/2018] [Indexed: 11/23/2022]
Abstract
The interest in wearable systems among the biomedical engineering and clinical community continues to escalate as technical refinements enhance their potential use for both indoor and outdoor applications. For example, an important wearable technology known as a microelectromechanical system (MEMS) is demonstrating promising applications in the area of biomedical engineering. Accordingly, this study was designed to investigate the Sharif-Human Movement Instrumentation System (SHARIF-HMIS), consisting of inertial measurement units (IMUs), stretchable clothing, and a data logger-all of which can be used outside the controlled environment of a laboratory, thus enhancing its overall utility. This system is lightweight, portable, able to be deliver data for almost 10 h, and features a new data-fusion algorithm using the Kalman filter with an adaptive approach. In specific terms, the data from the system's gyroscope, accelerometer, and magnetometer sensors can be combined to estimate total-body orientation; additionally, the noise level of these sensors can be changed to accommodate faster motions as well as magnetic disturbances. These variations can be incorporated within the extended Kalman filter by changing the parameters of the filter adaptively. In specific terms, the system's interface was developed to acquire data from eighteen IMUs located on the body to collect kinematic data associated with human motion. Meanwhile, a validation test involving one subject performing different shoulder motions was designed to compare data captured by SHARIF-HMIS and the VICON motion-capture system. This validation test demonstrated correlation values of >0.9. Results also confirmed that the output accuracy of the new system's sensor was <0.55, 1.5 and 3.5° for roll, pitch, and yaw directions, respectively. In summary, SHARIF-HMIS successfully collected kinematic data for specific human movements, which has promising implications for a range of sporting, biomedical, and healthcare-related applications.
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