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Teoh YX, Alwan JK, Shah DS, Teh YW, Goh SL. A scoping review of applications of artificial intelligence in kinematics and kinetics of ankle sprains - current state-of-the-art and future prospects. Clin Biomech (Bristol, Avon) 2024; 113:106188. [PMID: 38350282 DOI: 10.1016/j.clinbiomech.2024.106188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/26/2023] [Accepted: 01/23/2024] [Indexed: 02/15/2024]
Abstract
BACKGROUND Despite the existence of evidence-based rehabilitation strategies that address biomechanical deficits, the persistence of recurrent ankle problems in 70% of patients with acute ankle sprains highlights the unresolved nature of this issue. Artificial intelligence (AI) emerges as a promising tool to identify definitive predictors for ankle sprains. This paper aims to summarize the use of AI in investigating the ankle biomechanics of healthy and subjects with ankle sprains. METHODS Articles published between 2010 and 2023 were searched from five electronic databases. 59 papers were included for analysis with regards to: i). types of motion tested (functional vs. purposeful ankle movement); ii) types of biomechanical parameters measured (kinetic vs kinematic); iii) types of sensor systems used (lab-based vs field-based); and, iv) AI techniques used. FINDINGS Most studies (83.1%) examined biomechanics during functional motion. Single kinematic parameter, specifically ankle range of motion, could obtain accuracy up to 100% in identifying injury status. Wearable sensor exhibited high reliability for use in both laboratory and on-field/clinical settings. AI algorithms primarily utilized electromyography and joint angle information as input data. Support vector machine was the most used supervised learning algorithm (18.64%), while artificial neural network demonstrated the highest accuracy in eight studies. INTERPRETATIONS The potential for remote patient monitoring is evident with the adoption of field-based devices. Nevertheless, AI-based sensors are underutilized in detecting ankle motions at risk of sprain. We identify three key challenges: sensor designs, the controllability of AI models, and the integration of AI-sensor models, providing valuable insights for future research.
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Affiliation(s)
- Yun Xin Teoh
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Jwan K Alwan
- Department of Information Systems, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia; University of Information Technology and Communications, Iraq
| | - Darshan S Shah
- Department of Mechanical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Ying Wah Teh
- Department of Information Systems, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Siew Li Goh
- Sports Medicine Unit, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia; Centre for Epidemiology and Evidence-Based Practice, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
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2
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Meng L, Zhang T, Zhao X, Wang D, Xu R, Yang A, Ming D. A quantitative lower limb function assessment method based on fusion of surface EMG and inertial data in stroke patients during cycling task. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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3
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Banks CL, Patten C. Development of an assessment of bilateral locomotor efficacy for individuals post-stroke. Gait Posture 2023; 103:172-177. [PMID: 37210850 PMCID: PMC10773990 DOI: 10.1016/j.gaitpost.2023.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 03/24/2023] [Accepted: 05/16/2023] [Indexed: 05/23/2023]
Abstract
BACKGROUND A common framework is needed to assess walking impairments in older adults and individuals with stroke. This study develops an Assessment of Bilateral Locomotor Efficacy (ABLE) that is a straightforward indicator of walking function. RESEARCH QUESTION Can we develop a clinically accessible index of walking function that summarizes gait dysfunction secondary to stroke? METHODS The ABLE index was developed using a retrospective sample of 14 community-dwelling older adults. Data from 33 additional older adults and 105 individuals with chronic post-stroke hemiparesis were used to validate the index by factor analysis of the score components and correlation with multiple common assessments of lower extremity impairment and function. RESULTS The ABLE consists of four components summed for a maximum possible score of 12. The components include self-selected walking speed (SSWS), speed change from SSWS to fastest speed, non-paretic leg step length change from SSWS to fastest speed, and peak paretic leg ankle power. The ABLE revealed good concurrent validity with all recorded functional assessments. Factor analysis suggested that the ABLE measures two factors: one for forward progression and another for speed adaptability. SIGNIFICANCE The ABLE offers a straightforward, objective measure of walking function in adults, including individuals with chronic stroke. The index may also prove useful as a screening tool for subclinical pathology in community-dwelling older adults, but further testing is required. We encourage utilization of this index and reproduction of findings to adapt and refine the instrument for wider use and eventual clinical application.
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Affiliation(s)
- Caitlin L Banks
- Biomechanics, Rehabilitation, and Integrative Neuroscience (BRaIN) Lab, Department of Physical Medicine and Rehabilitation, UC Davis School of Medicine, Sacramento, CA, USA; UC Davis Center for Neuroengineering and Medicine, Davis, CA, USA; Biomedical Engineering Graduate Group, UC Davis, Davis, CA, USA; VA Northern California Health Care System, Martinez, CA, USA
| | - Carolynn Patten
- Biomechanics, Rehabilitation, and Integrative Neuroscience (BRaIN) Lab, Department of Physical Medicine and Rehabilitation, UC Davis School of Medicine, Sacramento, CA, USA; UC Davis Center for Neuroengineering and Medicine, Davis, CA, USA; Biomedical Engineering Graduate Group, UC Davis, Davis, CA, USA; VA Northern California Health Care System, Martinez, CA, USA.
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4
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Liang W, Wang F, Fan A, Zhao W, Yao W, Yang P. Extended Application of Inertial Measurement Units in Biomechanics: From Activity Recognition to Force Estimation. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094229. [PMID: 37177436 PMCID: PMC10180901 DOI: 10.3390/s23094229] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/20/2023] [Accepted: 04/22/2023] [Indexed: 05/15/2023]
Abstract
Abnormal posture or movement is generally the indicator of musculoskeletal injuries or diseases. Mechanical forces dominate the injury and recovery processes of musculoskeletal tissue. Using kinematic data collected from wearable sensors (notably IMUs) as input, activity recognition and musculoskeletal force (typically represented by ground reaction force, joint force/torque, and muscle activity/force) estimation approaches based on machine learning models have demonstrated their superior accuracy. The purpose of the present study is to summarize recent achievements in the application of IMUs in biomechanics, with an emphasis on activity recognition and mechanical force estimation. The methodology adopted in such applications, including data pre-processing, noise suppression, classification models, force/torque estimation models, and the corresponding application effects, are reviewed. The extent of the applications of IMUs in daily activity assessment, posture assessment, disease diagnosis, rehabilitation, and exoskeleton control strategy development are illustrated and discussed. More importantly, the technical feasibility and application opportunities of musculoskeletal force prediction using IMU-based wearable devices are indicated and highlighted. With the development and application of novel adaptive networks and deep learning models, the accurate estimation of musculoskeletal forces can become a research field worthy of further attention.
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Affiliation(s)
- Wenqi Liang
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China
| | - Fanjie Wang
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China
| | - Ao Fan
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China
| | - Wenrui Zhao
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China
| | - Wei Yao
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China
| | - Pengfei Yang
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China
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5
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Mantashloo Z, Abbasi A, Tazji MK, Pedram MM. Lower body kinematics estimation during walking using an accelerometer. J Biomech 2023; 151:111548. [PMID: 36944294 DOI: 10.1016/j.jbiomech.2023.111548] [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: 07/12/2022] [Revised: 03/08/2023] [Accepted: 03/13/2023] [Indexed: 03/18/2023]
Abstract
Measuring and predicting accurate joint angles are important to developing analytical tools to gauge users' progress. Such measurement is usually performed in laboratory settings, which is difficult and expensive. So, the aim of this study was continuous estimation of lower limb joint angles during walking using an accelerometer and random forest (RF). Thus, 73 subjects (26 women and 47 men) voluntarily participated in this study. The subjects walked at the slow, moderate, and fast speeds on a walkway, which was covered with 10 Vicon camera. Acceleration was used as input for a RF to estimate ankle, knee, and hip angles (in transverse, frontal, and sagittal planes). Pearson correlation coefficient (r) and Mean Square Error (MSE) were computed between the experimental and estimated data. Paired statistical parametric mapping (SPM) t-test was used to compare the experimental and estimated data throughout gait cycle. The results of this study showed that the MSE of joint angles between the experimental and estimated data ranged from 0.04 to 24.29 and r > 0.91. Moreover, the findings of SPM indicated that there was no significant difference between the experimental and estimated data of ankle, knee, and hip angles in all three planes throughout gait cycle. The results of our research developed a more accessible, portable procedure to quantifying lower limb joint angles by an accelerometer and RF. So, such wearable-based joint angles have the potential to be used in outside-laboratory settings to measure walking kinematics.
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Affiliation(s)
- Zahed Mantashloo
- Department of Biomechanics and Sports Injuries, Faculty of Physical Education and Sports Sciences, Kharazmi University, Tehran, Iran
| | - Ali Abbasi
- Department of Biomechanics and Sports Injuries, Faculty of Physical Education and Sports Sciences, Kharazmi University, Tehran, Iran.
| | - Mehdi Khaleghi Tazji
- Department of Biomechanics and Sports Injuries, Faculty of Physical Education and Sports Sciences, Kharazmi University, Tehran, Iran
| | - Mir Mohsen Pedram
- Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
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Domínguez-Ruiz A, López-Caudana EO, Lugo-González E, Espinosa-García FJ, Ambrocio-Delgado R, García UD, López-Gutiérrez R, Alfaro-Ponce M, Ponce P. Low limb prostheses and complex human prosthetic interaction: A systematic literature review. Front Robot AI 2023; 10:1032748. [PMID: 36860557 PMCID: PMC9968924 DOI: 10.3389/frobt.2023.1032748] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 01/11/2023] [Indexed: 02/15/2023] Open
Abstract
A few years ago, powered prostheses triggered new technological advances in diverse areas such as mobility, comfort, and design, which have been essential to improving the quality of life of individuals with lower limb disability. The human body is a complex system involving mental and physical health, meaning a dependant relationship between its organs and lifestyle. The elements used in the design of these prostheses are critical and related to lower limb amputation level, user morphology and human-prosthetic interaction. Hence, several technologies have been employed to accomplish the end user's needs, for example, advanced materials, control systems, electronics, energy management, signal processing, and artificial intelligence. This paper presents a systematic literature review on such technologies, to identify the latest advances, challenges, and opportunities in developing lower limb prostheses with the analysis on the most significant papers. Powered prostheses for walking in different terrains were illustrated and examined, with the kind of movement the device should perform by considering the electronics, automatic control, and energy efficiency. Results show a lack of a specific and generalised structure to be followed by new developments, gaps in energy management and improved smoother patient interaction. Additionally, Human Prosthetic Interaction (HPI) is a term introduced in this paper since no other research has integrated this interaction in communication between the artificial limb and the end-user. The main goal of this paper is to provide, with the found evidence, a set of steps and components to be followed by new researchers and experts looking to improve knowledge in this field.
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Affiliation(s)
- Adan Domínguez-Ruiz
- Institute for the Future of Education, Tecnologico de Monterrey, Mexico City, México
| | | | - Esther Lugo-González
- Instituto de Electrónica y Mecatrónica, Universidad Tecnológica de la Mixteca, Huajuapan de León, Oaxaca, México
| | | | - Rocío Ambrocio-Delgado
- División de Estudios de Posgrado, Universidad Tecnológica de la Mixteca, Huajuapan de León, Oaxaca, México
| | - Ulises D. García
- CONACYT-CINVESTAV, Av. Instituto Politécnico Nacional 2508, col. San Pedro Zacatenco, Ciudad deMéxico, México
| | - Ricardo López-Gutiérrez
- CONACYT-CINVESTAV, Av. Instituto Politécnico Nacional 2508, col. San Pedro Zacatenco, Ciudad deMéxico, México
| | - Mariel Alfaro-Ponce
- Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Mexico City, México
| | - Pedro Ponce
- Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Mexico City, México,*Correspondence: Pedro Ponce,
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Amrein S, Werner C, Arnet U, de Vries WHK. Machine-Learning-Based Methodology for Estimation of Shoulder Load in Wheelchair-Related Activities Using Wearables. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23031577. [PMID: 36772617 PMCID: PMC9918997 DOI: 10.3390/s23031577] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/27/2023] [Accepted: 01/30/2023] [Indexed: 06/01/2023]
Abstract
There is a high prevalence of shoulder problems in manual wheelchair users (MWUs) with a spinal cord injury. How shoulder load relates to shoulder problems remains unclear. This study aimed to develop a machine-learning-based methodology to estimate the shoulder load in wheelchair-related activities of daily living using wearable sensors. Ten able-bodied participants equipped with five inertial measurement units (IMU) on their thorax, right arm, and wheelchair performed activities exemplary of daily life of MWUs. Electromyography (EMG) was recorded from the long head of the biceps and medial part of the deltoid. A neural network was trained to predict the shoulder load based on IMU and EMG data. Different cross-validation strategies, sensor setups, and model architectures were examined. The predicted shoulder load was compared to the shoulder load determined with musculoskeletal modeling. A subject-specific biLSTM model trained on a sparse sensor setup yielded the most promising results (mean correlation coefficient = 0.74 ± 0.14, relative root-mean-squared error = 8.93% ± 2.49%). The shoulder-load profiles had a mean similarity of 0.84 ± 0.10 over all activities. This study demonstrates the feasibility of using wearable sensors and neural networks to estimate the shoulder load in wheelchair-related activities of daily living.
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Affiliation(s)
- Sabrina Amrein
- Rehabilitation Engineering Laboratory, Department of Health Science and Technology, ETH Zurich, 8049 Zurich, Switzerland
- Swiss Paraplegic Research, Guido A. Zächstrasse 4, 6207 Nottwil, Switzerland
| | - Charlotte Werner
- Rehabilitation Engineering Laboratory, Department of Health Science and Technology, ETH Zurich, 8049 Zurich, Switzerland
- Spinal Cord Injury Center, University Hospital Balgrist, 8008 Zurich, Switzerland
| | - Ursina Arnet
- Swiss Paraplegic Research, Guido A. Zächstrasse 4, 6207 Nottwil, Switzerland
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Kumar KS, Jamsrandorj A, Kim J, Mun KR. Prediction of lower limb kinematics from vision-based system using deep learning approaches. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:177-181. [PMID: 36086538 DOI: 10.1109/embc48229.2022.9871577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The joint angular velocity during daily life exercises is an important clinical outcome for injury risk index, rehabilitation progress monitoring and athlete's performance evaluation. Recently, wearable sensors have been widely used to monitor lower limb kinematics. However, these sensors are difficult and inconvenient to use in daily life. To mitigate these limitations, this study proposes a vision-based system for estimating lower limb joint kinematics using a deep convolution neural network with bi-directional long-short term memory and gated recurrent unit network. The normalized correlation coefficient, and the mean absolute error were computed between the ground truth obtained from the optical motion capture system and estimated joint angular velocities using proposed models. The estimated results show a highest correlation 0.93 in squat and 0.92 in walking on treadmill action. Furthermore, independent model for each joint angular velocity at the hip, knee, and ankle were analyzed and compared. Among the three joint angular velocities, knee joint has a best estimated accuracy (0.96 in squat and 0.96 in walking on the treadmill). The proposed models show higher estimation accuracy under both the lateral and the frontal view regardless of the camera positions and angles. This study proves the applicability of using sensor free vision-based system to monitor the lower limb kinematics during home workouts for healthcare and rehabilitation.
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Heeb O, Barua A, Menon C, Jiang X. Building Effective Machine Learning Models for Ankle Joint Power Estimation During Walking Using FMG Sensors. Front Neurorobot 2022; 16:836779. [PMID: 35431852 PMCID: PMC9010568 DOI: 10.3389/fnbot.2022.836779] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 03/07/2022] [Indexed: 11/26/2022] Open
Abstract
Ankle joint power is usually determined by a complex process that involves heavy equipment and complex biomechanical models. Instead of using heavy equipment, we proposed effective machine learning (ML) and deep learning (DL) models to estimate the ankle joint power using force myography (FMG) sensors. In this study, FMG signals were collected from nine young, healthy participants. The task was to walk on a special treadmill for five different velocities with a respective duration of 1 min. FMG signals were collected from an FMG strap that consists of 8 force resisting sensor (FSR) sensors. The strap was positioned around the lower leg. The ground truth value for ankle joint power was determined with the help of a complex biomechanical model. At first, the predictors' value was preprocessed using a rolling mean filter. Following, three sets of features were formed where the first set includes raw FMG signals, and the other two sets contained time-domain and frequency-domain features extracted using the first set. Cat Boost Regressor (CBR), Long-Short Term Memory (LSTM), and Convolutional Neural Network (CNN) were trained and tested using these three features sets. The results presented in this study showed a correlation coefficient of R = 0.91 ± 0.07 for intrasubject testing and were found acceptable when compared to other similar studies. The CNN on raw features and the LSTM on time-domain features outperformed the other variations. Aside from that, a performance gap between the slowest and fastest walking distance was observed. The results from this study showed that it was possible to achieve an acceptable correlation coefficient in the prediction of ankle joint power using FMG sensors with an appropriate combination of feature set and ML model.
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Affiliation(s)
- Oliver Heeb
- Biomedical and Mobile Health Technology Laboratory, ETH Zurich, Zurich, Switzerland
| | - Arnab Barua
- Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, Canada
| | - Carlo Menon
- Biomedical and Mobile Health Technology Laboratory, ETH Zurich, Zurich, Switzerland
- Menrva Research Group, Schools of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Burnaby, BC, Canada
- Carlo Menon
| | - Xianta Jiang
- Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, Canada
- *Correspondence: Xianta Jiang
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Tavakkoli Oskouei S, Malliaras P, Hill KD, Clark R, Perraton L. Evaluating daily physical activity and biomechanical measures using wearable technology in people with Achilles tendinopathy: A descriptive exploratory study. Musculoskelet Sci Pract 2022; 58:102534. [PMID: 35220207 DOI: 10.1016/j.msksp.2022.102534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 02/10/2022] [Accepted: 02/17/2022] [Indexed: 10/19/2022]
Abstract
BACKGROUND Load management is considered an important factor for prevention and treatment of Achilles tendinopathy. However, little attention has been given to monitoring daily load objectively in this population. OBJECTIVES We aimed to explore patterns in proxies of daily load derived from a six-axis inertial measurement unit (IMU) over a one-week period and the concordance between day-to-day fluctuation in pain intensity and IMU measures. DESIGN Descriptive exploratory study. METHOD Ten participants with Achilles tendinopathy (age: 53.00 ± 12.37) wore an IMU on the affected ankle for one week. Participants were contacted via text message three times daily to rate their worst pain intensity. Physical activity and biomechanical measures derived from the IMU signals including daily number of steps, peak stride rate, peak shank acceleration, and peak shank angular velocity were calculated. RESULTS The relationship between weekly worst pain and physical activity levels appeared modest; with increased steps not seeming to be linked to increased or reduced pain levels. According to the daily pain and daily IMU measures, a concordant pattern was evident in younger, highly active participants. However, in the middle-aged/older less active participants, there was either a fluctuation in pain intensity without fluctuation in the IMU measures, or a stable pattern of both pain and IMU measures. CONCLUSIONS Our exploratory study results suggest that continuous monitoring of proxies of daily load measures in parallel with pain may provide information about load management strategies in people with Achilles tendinopathy. Monitoring of these proxies may ultimately have a role in improving Achilles tendinopathy management.
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Affiliation(s)
- Sanam Tavakkoli Oskouei
- Department of Physiotherapy, School of Primary and Allied Health Care, Faculty of Medicine, Nursing and Health Sciences, Monash University, Victoria, Australia.
| | - Peter Malliaras
- Department of Physiotherapy, School of Primary and Allied Health Care, Faculty of Medicine, Nursing and Health Sciences, Monash University, Victoria, Australia
| | - Keith D Hill
- Rehabilitation, Ageing and Independent Living (RAIL) Research Centre, Monash University, Victoria, Australia
| | - Ross Clark
- School of Health and Behavioural Sciences, University of the Sunshine Coast, Queensland, Australia
| | - Luke Perraton
- Department of Physiotherapy, School of Primary and Allied Health Care, Faculty of Medicine, Nursing and Health Sciences, Monash University, Victoria, Australia
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Lee CJ, Lee JK. Inertial Motion Capture-Based Wearable Systems for Estimation of Joint Kinetics: A Systematic Review. SENSORS 2022; 22:s22072507. [PMID: 35408121 PMCID: PMC9002742 DOI: 10.3390/s22072507] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 11/16/2022]
Abstract
In biomechanics, joint kinetics has an important role in evaluating the mechanical load of the joint and understanding its motor function. Although an optical motion capture (OMC) system has mainly been used to evaluate joint kinetics in combination with force plates, inertial motion capture (IMC) systems have recently been emerging in joint kinetic analysis due to their wearability and ubiquitous measurement capability. In this regard, numerous studies have been conducted to estimate joint kinetics using IMC-based wearable systems. However, these have not been comprehensively addressed yet. Thus, the aim of this review is to explore the methodology of the current studies on estimating joint kinetic variables by means of an IMC system. From a systematic search of the literature, 48 studies were selected. This paper summarizes the content of the selected literature in terms of the (i) study characteristics, (ii) methodologies, and (iii) study results. The estimation methods of the selected studies are categorized into two types: the inverse dynamics-based method and the machine learning-based method. While these two methods presented different characteristics in estimating the kinetic variables, it was demonstrated in the literature that both methods could be applied with good performance for the kinetic analysis of joints in different daily activities.
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Affiliation(s)
- Chang June Lee
- Department of Mechanical Engineering, Hankyong National University, Anseong 17579, Korea;
| | - Jung Keun Lee
- School of ICT, Robotics & Mechanical Engineering, Hankyong National University, Anseong 17579, Korea
- Correspondence: ; Tel.: +82-31-670-5112
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Prill R, Walter M, Królikowska A, Becker R. A Systematic Review of Diagnostic Accuracy and Clinical Applications of Wearable Movement Sensors for Knee Joint Rehabilitation. SENSORS 2021; 21:s21248221. [PMID: 34960315 PMCID: PMC8707010 DOI: 10.3390/s21248221] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 12/06/2021] [Accepted: 12/07/2021] [Indexed: 11/18/2022]
Abstract
In clinical practice, only a few reliable measurement instruments are available for monitoring knee joint rehabilitation. Advances to replace motion capturing with sensor data measurement have been made in the last years. Thus, a systematic review of the literature was performed, focusing on the implementation, diagnostic accuracy, and facilitators and barriers of integrating wearable sensor technology in clinical practices based on a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. For critical appraisal, the COSMIN Risk of Bias tool for reliability and measurement of error was used. PUBMED, Prospero, Cochrane database, and EMBASE were searched for eligible studies. Six studies reporting reliability aspects in using wearable sensor technology at any point after knee surgery in humans were included. All studies reported excellent results with high reliability coefficients, high limits of agreement, or a few detectable errors. They used different or partly inappropriate methods for estimating reliability or missed reporting essential information. Therefore, a moderate risk of bias must be considered. Further quality criterion studies in clinical settings are needed to synthesize the evidence for providing transparent recommendations for the clinical use of wearable movement sensors in knee joint rehabilitation.
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Affiliation(s)
- Robert Prill
- Center of Orthopaedics and Traumatology, Brandenburg Medical School, University Hospital Brandenburg/Havel, 14770 Brandenburg an der Havel, Germany;
- Correspondence:
| | - Marina Walter
- Hasso-Plattner-Institut, University of Potsdam, 14469 Potsdam, Germany;
| | - Aleksandra Królikowska
- Ergonomics and Biomedical Monitoring Laboratory, Department of Physiotherapy, Faculty of Health Sciences, Wroclaw Medical University, 50-367 Wrocław, Poland;
| | - Roland Becker
- Center of Orthopaedics and Traumatology, Brandenburg Medical School, University Hospital Brandenburg/Havel, 14770 Brandenburg an der Havel, Germany;
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Mohan DM, Khandoker AH, Wasti SA, Ismail Ibrahim Ismail Alali S, Jelinek HF, Khalaf K. Assessment Methods of Post-stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis. Front Neurol 2021; 12:650024. [PMID: 34168608 PMCID: PMC8217618 DOI: 10.3389/fneur.2021.650024] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 05/07/2021] [Indexed: 12/26/2022] Open
Abstract
Background: Gait dysfunction or impairment is considered one of the most common and devastating physiological consequences of stroke, and achieving optimal gait is a key goal for stroke victims with gait disability along with their clinical teams. Many researchers have explored post stroke gait, including assessment tools and techniques, key gait parameters and significance on functional recovery, as well as data mining, modeling and analyses methods. Research Question: This study aimed to review and summarize research efforts applicable to quantification and analyses of post-stroke gait with focus on recent technology-driven gait characterization and analysis approaches, including the integration of smart low cost wearables and Artificial Intelligence (AI), as well as feasibility and potential value in clinical settings. Methods: A comprehensive literature search was conducted within Google Scholar, PubMed, and ScienceDirect using a set of keywords, including lower extremity, walking, post-stroke, and kinematics. Original articles that met the selection criteria were included. Results and Significance: This scoping review aimed to shed light on tools and technologies employed in post stroke gait assessment toward bridging the existing gap between the research and clinical communities. Conventional qualitative gait analysis, typically used in clinics is mainly based on observational gait and is hence subjective and largely impacted by the observer's experience. Quantitative gait analysis, however, provides measured parameters, with good accuracy and repeatability for the diagnosis and comparative assessment throughout rehabilitation. Rapidly emerging smart wearable technology and AI, including Machine Learning, Support Vector Machine, and Neural Network approaches, are increasingly commanding greater attention in gait research. Although their use in clinical settings are not yet well leveraged, these tools promise a paradigm shift in stroke gait quantification, as they provide means for acquiring, storing and analyzing multifactorial complex gait data, while capturing its non-linear dynamic variability and offering the invaluable benefits of predictive analytics.
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Affiliation(s)
- Dhanya Menoth Mohan
- Department of Biomedical Engineering, Health Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Ahsan Habib Khandoker
- Department of Biomedical Engineering, Health Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Sabahat Asim Wasti
- Neurological Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Sarah Ismail Ibrahim Ismail Alali
- Department of Biomedical Engineering, Health Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Herbert F Jelinek
- Department of Biomedical Engineering, Health Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Kinda Khalaf
- Department of Biomedical Engineering, Health Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
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Jiang X, Napier C, Hannigan B, Eng JJ, Menon C. Estimating Vertical Ground Reaction Force during Walking Using a Single Inertial Sensor. SENSORS 2020; 20:s20154345. [PMID: 32759831 PMCID: PMC7436236 DOI: 10.3390/s20154345] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 07/29/2020] [Accepted: 07/30/2020] [Indexed: 01/09/2023]
Abstract
The vertical ground reaction force (vGRF) and its passive and active peaks are important gait parameters and of great relevance for musculoskeletal injury analysis and prevention, the detection of gait abnormities, and the evaluation of lower-extremity prostheses. Most currently available methods to estimate the vGRF require a force plate. However, in real-world scenarios, gait monitoring would not be limited to a laboratory setting. This paper reports a novel solution using machine learning algorithms to estimate the vGRF and the timing and magnitude of its peaks from data collected by a single inertial measurement unit (IMU) on one of the lower limb locations. Nine volunteers participated in this study, walking on a force plate-instrumented treadmill at various speeds. Four IMUs were worn on the foot, shank, distal thigh, and proximal thigh, respectively. A random forest model was employed to estimate the vGRF from data collected by each of the IMUs. We evaluated the performance of the models against the gold standard measurement of the vGRF generated by the treadmill. The developed model achieved a high accuracy with a correlation coefficient, root mean square error, and normalized root mean square error of 1.00, 0.02 body weight (BW), and 1.7% in intra-participant testing, and 0.97, 0.10 BW, and 7.15% in inter-participant testing, respectively, for the shank location. The difference between the reference and estimated passive force peak values was 0.02 BW and 0.14 BW with a delay of −0.14% and 0.57% of stance duration for the intra- and inter-participant testing, respectively; the difference between the reference and estimated active force peak values was 0.02 BW and 0.08 BW with a delay of 0.45% and 1.66% of stance duration for the intra- and inter-participant evaluation, respectively. We concluded that vertical ground reaction force can be estimated using only a single IMU via machine learning algorithms. This research sheds light on the development of a portable wearable gait monitoring system reporting the real-time vGRF in real-life scenarios.
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Affiliation(s)
- Xianta Jiang
- Menrva Research Group, Schools of Mechatronic Systems & Engineering Science, Simon Fraser University, Metro Vancouver, BC V3T 0A3, Canada; (X.J.); (C.N.); (B.H.)
- Department of Computer Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada
| | - Christopher Napier
- Menrva Research Group, Schools of Mechatronic Systems & Engineering Science, Simon Fraser University, Metro Vancouver, BC V3T 0A3, Canada; (X.J.); (C.N.); (B.H.)
- Department of Physical Therapy, University of British Columbia, Vancouver, BC V6T 1Z3, Canada;
| | - Brett Hannigan
- Menrva Research Group, Schools of Mechatronic Systems & Engineering Science, Simon Fraser University, Metro Vancouver, BC V3T 0A3, Canada; (X.J.); (C.N.); (B.H.)
| | - Janice J. Eng
- Department of Physical Therapy, University of British Columbia, Vancouver, BC V6T 1Z3, Canada;
- Rehabilitation Research Program, GF Strong Rehab Centre, Vancouver Coastal Health Research Institute, Vancouver, BC V5Z 2G9, Canada
| | - Carlo Menon
- Menrva Research Group, Schools of Mechatronic Systems & Engineering Science, Simon Fraser University, Metro Vancouver, BC V3T 0A3, Canada; (X.J.); (C.N.); (B.H.)
- Correspondence: ; Tel.: +1-778-782-9338
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Estimating Lower Extremity Running Gait Kinematics with a Single Accelerometer: A Deep Learning Approach. SENSORS 2020; 20:s20102939. [PMID: 32455927 PMCID: PMC7287664 DOI: 10.3390/s20102939] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 05/01/2020] [Accepted: 05/20/2020] [Indexed: 11/16/2022]
Abstract
Abnormal running kinematics are associated with an increased incidence of lower extremity injuries among runners. Accurate and unobtrusive running kinematic measurement plays an important role in the detection of gait abnormalities and the prevention of injuries among runners. Inertial-based methods have been proposed to address this need. However, previous methods require cumbersome sensor setup or participant-specific calibration. This study aims to validate a shoe-mounted accelerometer for sagittal plane lower extremity angle measurement during running based on a deep learning approach. A convolutional neural network (CNN) architecture was selected as the regression model to generalize in inter-participant scenarios and to minimize poorly estimated joints. Motion and accelerometer data were recorded from ten participants while running on a treadmill at five different speeds. The reference joint angles were measured by an optical motion capture system. The CNN model predictions deviated from the reference angles with a root mean squared error (RMSE) of less than 3.5° and 6.5° in intra- and inter-participant scenarios, respectively. Moreover, we provide an estimation of six important gait events with a mean absolute error of less than 2.5° and 6.5° in intra- and inter-participants scenarios, respectively. This study highlights an appealing minimal sensor setup approach for gait analysis purposes.
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Gholami M, Rezaei A, Cuthbert TJ, Napier C, Menon C. Lower Body Kinematics Monitoring in Running Using Fabric-Based Wearable Sensors and Deep Convolutional Neural Networks. SENSORS 2019; 19:s19235325. [PMID: 31816931 PMCID: PMC6928687 DOI: 10.3390/s19235325] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 11/27/2019] [Accepted: 11/29/2019] [Indexed: 02/05/2023]
Abstract
Continuous kinematic monitoring of runners is crucial to inform runners of inappropriate running habits. Motion capture systems are the gold standard for gait analysis, but they are spatially limited to laboratories. Recently, wearable sensors have gained attention as an unobtrusive method to analyze performance metrics and the health conditions of runners. In this study, we developed a system capable of estimating joint angles in sagittal, frontal, and transverse planes during running. A prototype with fiber strain sensors was fabricated. The positions of the sensors on the pelvis were optimized using a genetic algorithm. A cohort of ten people completed 15 min of running at five different speeds for gait analysis by our prototype device. The joint angles were estimated by a deep convolutional neural network in inter- and intra-participant scenarios. In intra-participant tests, root mean square error (RMSE) and normalized root mean square error (NRMSE) of less than 2.2° and 5.3%, respectively, were obtained for hip, knee, and ankle joints in sagittal, frontal, and transverse planes. The RMSE and NRMSE in inter-participant tests were less than 6.4° and 10%, respectively, in the sagittal plane. The accuracy of this device and methodology could yield potential applications as a soft wearable device for gait monitoring of runners.
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Can shank acceleration provide a clinically feasible surrogate for individual limb propulsion during walking? J Biomech 2019; 98:109449. [PMID: 31679756 DOI: 10.1016/j.jbiomech.2019.109449] [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: 08/28/2019] [Revised: 10/18/2019] [Accepted: 10/21/2019] [Indexed: 11/20/2022]
Abstract
Aging and many pathologies that affect gait are associated with reduced ankle power output and thus trailing limb propulsion during walking. However, quantifying trailing limb propulsion requires sophisticated measurement equipment at significant expense that fundamentally limits clinical translation for diagnostics or gait rehabilitation. As a component of joint power, our purpose was to determine if shank acceleration estimated via accelerometers during push-off can serve as a clinically feasible surrogate for ankle power output and peak anterior ground reaction forces (GRF) during walking. As hypothesized, we found that young adults modulated walking speed via changes in peak anterior GRF and peak ankle power output that correlated with proportional changes in shank acceleration during push-off, both at the individual subject (R2 ≥ 0.80, p < 0.01) and group average (R2 ≥ 0.74, p < 0.01) levels. In addition, we found that unilateral deficits in trailing limb propulsion induced via a leg bracing elicited unilateral and relatively proportional reductions in peak anterior GRF, peak ankle power, and peak shank acceleration. These unilateral leg bracing effects on peak shank acceleration correlated with those in peak ankle power (braced leg: R2 = 0.43, p = 0.028) but those effects in both peak shank acceleration and peak ankle power were disassociated from those in peak anterior GRF. In conclusion, our findings in young adults provide an early benchmark for the development of affordable and clinically feasible alternatives for assessing and monitoring trailing limb propulsion during walking.
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