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Inai T, Kobayashi Y, Sudo M, Yamashiro Y, Ueda T. Errors in Estimating Lower-Limb Joint Angles and Moments during Walking Based on Pelvic Accelerations: Influence of Virtual Inertial Measurement Unit's Frontal Plane Misalignment. SENSORS (BASEL, SWITZERLAND) 2024; 24:5096. [PMID: 39204793 PMCID: PMC11359074 DOI: 10.3390/s24165096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2024] [Revised: 08/01/2024] [Accepted: 08/05/2024] [Indexed: 09/04/2024]
Abstract
The accurate estimation of lower-limb joint angles and moments is crucial for assessing the progression of orthopedic diseases, with continuous monitoring during daily walking being essential. An inertial measurement unit (IMU) attached to the lower back has been used for this purpose, but the effect of IMU misalignment in the frontal plane on estimation accuracy remains unclear. This study investigated the impact of virtual IMU misalignment in the frontal plane on estimation errors of lower-limb joint angles and moments during walking. Motion capture data were recorded from 278 healthy adults walking at a comfortable speed. An estimation model was developed using principal component analysis and linear regression, with pelvic accelerations as independent variables and lower-limb joint angles and moments as dependent variables. Virtual IMU misalignments of -20°, -10°, 0°, 10°, and 20° in the frontal plane (five conditions) were simulated. The joint angles and moments were estimated and compared across these conditions. The results indicated that increasing virtual IMU misalignment in the frontal plane led to greater errors in the estimation of pelvis and hip angles, particularly in the frontal plane. For misalignments of ±20°, the errors in pelvis and hip angles were significantly amplified compared to well-aligned conditions. These findings underscore the importance of accounting for IMU misalignment when estimating these variables.
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Affiliation(s)
- Takuma Inai
- Health and Medical Research Institute, National Institute of Advanced Industrial Science and Technology, 2217-14 Hayashi-cho, Takamatsu 761-0395, Kagawa, Japan
| | - Yoshiyuki Kobayashi
- Human Augmentation Research Center, National Institute of Advanced Industrial Science and Technology, 6-2-3 Kashiwanoha, Kashiwa 277-0882, Chiba, Japan;
| | - Motoki Sudo
- Tokyo Research Laboratories, Kao Corporation, 2-1-3 Bunka, Sumida-ku 131-8501, Tokyo, Japan; (M.S.); (Y.Y.); (T.U.)
| | - Yukari Yamashiro
- Tokyo Research Laboratories, Kao Corporation, 2-1-3 Bunka, Sumida-ku 131-8501, Tokyo, Japan; (M.S.); (Y.Y.); (T.U.)
| | - Tomoya Ueda
- Tokyo Research Laboratories, Kao Corporation, 2-1-3 Bunka, Sumida-ku 131-8501, Tokyo, Japan; (M.S.); (Y.Y.); (T.U.)
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Reiter AJ, Martin JA, Knurr KA, Adamczyk PG, Thelen DG. Achilles Tendon Loading during Running Estimated Via Shear Wave Tensiometry: A Step Toward Wearable Kinetic Analysis. Med Sci Sports Exerc 2024; 56:1077-1084. [PMID: 38240495 PMCID: PMC11096059 DOI: 10.1249/mss.0000000000003396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2024]
Abstract
PURPOSE Understanding muscle-tendon forces (e.g., triceps surae and Achilles tendon) during locomotion may aid in the assessment of human performance, injury risk, and rehabilitation progress. Shear wave tensiometry is a noninvasive technique for assessing in vivo tendon forces that has been recently adapted to a wearable technology. However, previous laboratory-based and outdoor tensiometry studies have not evaluated running. This study was undertaken to assess the capacity for shear wave tensiometry to produce valid measures of Achilles tendon loading during running at a range of speeds. METHODS Participants walked (1.34 m·s -1 ) and ran (2.68, 3.35, and 4.47 m·s -1 ) on an instrumented treadmill while shear wave tensiometers recorded Achilles tendon wave speeds simultaneously with whole-body kinematic and ground reaction force data. A simple isometric task allowed for the participant-specific conversion of Achilles tendon wave speeds to forces. Achilles tendon forces were compared with ankle torque measures obtained independently via inverse dynamics analyses. Differences in Achilles tendon wave speed, Achilles tendon force, and ankle torque across walking and running speeds were analyzed with linear mixed-effects models. RESULTS Achilles tendon wave speed, Achilles tendon force, and ankle torque exhibited similar temporal patterns across the stance phase of walking and running. Significant monotonic increases in peak Achilles tendon wave speed (56.0-83.8 m·s -1 ), Achilles tendon force (44.0-98.7 N·kg -1 ), and ankle torque (1.72-3.68 N·m·(kg -1 )) were observed with increasing locomotion speed (1.34-4.47 m·s -1 ). Tensiometry estimates of peak Achilles tendon force during running (8.2-10.1 body weights) were within the range of those estimated previously via indirect methods. CONCLUSIONS These results set the stage for using tensiometry to evaluate Achilles tendon loading during unobstructed athletic movements, such as running, performed in the field.
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Affiliation(s)
- Alex J Reiter
- Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI
| | | | | | - Peter G Adamczyk
- Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI
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Krishnakumar S, van Beijnum BJF, Baten CTM, Veltink PH, Buurke JH. Estimation of Kinetics Using IMUs to Monitor and Aid in Clinical Decision-Making during ACL Rehabilitation: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:2163. [PMID: 38610374 PMCID: PMC11014074 DOI: 10.3390/s24072163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 03/18/2024] [Accepted: 03/23/2024] [Indexed: 04/14/2024]
Abstract
After an ACL injury, rehabilitation consists of multiple phases, and progress between these phases is guided by subjective visual assessments of activities such as running, hopping, jump landing, etc. Estimation of objective kinetic measures like knee joint moments and GRF during assessment can help physiotherapists gain insights on knee loading and tailor rehabilitation protocols. Conventional methods deployed to estimate kinetics require complex, expensive systems and are limited to laboratory settings. Alternatively, multiple algorithms have been proposed in the literature to estimate kinetics from kinematics measured using only IMUs. However, the knowledge about their accuracy and generalizability for patient populations is still limited. Therefore, this article aims to identify the available algorithms for the estimation of kinetic parameters using kinematics measured only from IMUs and to evaluate their applicability in ACL rehabilitation through a comprehensive systematic review. The papers identified through the search were categorized based on the modelling techniques and kinetic parameters of interest, and subsequently compared based on the accuracies achieved and applicability for ACL patients during rehabilitation. IMUs have exhibited potential in estimating kinetic parameters with good accuracy, particularly for sagittal movements in healthy cohorts. However, several shortcomings were identified and future directions for improvement have been proposed, including extension of proposed algorithms to accommodate multiplanar movements and validation of the proposed techniques in diverse patient populations and in particular the ACL population.
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Affiliation(s)
- Sanchana Krishnakumar
- Department of Biomedical Signals and System, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands; (B.-J.F.v.B.); (P.H.V.); (J.H.B.)
| | - Bert-Jan F. van Beijnum
- Department of Biomedical Signals and System, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands; (B.-J.F.v.B.); (P.H.V.); (J.H.B.)
| | - Chris T. M. Baten
- Roessingh Research and Development, Roessinghsbleekweg 33B, 7522 AH Enschede, The Netherlands;
| | - Peter H. Veltink
- Department of Biomedical Signals and System, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands; (B.-J.F.v.B.); (P.H.V.); (J.H.B.)
| | - Jaap H. Buurke
- Department of Biomedical Signals and System, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands; (B.-J.F.v.B.); (P.H.V.); (J.H.B.)
- Roessingh Research and Development, Roessinghsbleekweg 33B, 7522 AH Enschede, The Netherlands;
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Ajdaroski M, Baek SY, Ashton-Miller JA, Esquivel AO. Predicting Leg Forces and Knee Moments Using Inertial Measurement Units: An In Vitro Study. J Biomech Eng 2024; 146:021006. [PMID: 38019183 PMCID: PMC10750790 DOI: 10.1115/1.4064145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 11/14/2023] [Accepted: 11/27/2023] [Indexed: 11/30/2023]
Abstract
We compared the ability of seven machine learning algorithms to use wearable inertial measurement unit (IMU) data to identify the severe knee loading cycles known to induce microdamage associated with anterior cruciate ligament rupture. Sixteen cadaveric knee specimens, dissected free of skin and muscle, were mounted in a rig simulating standardized jump landings. One IMU was located above and the other below the knee, the applied three-dimensional action and reaction loads were measured via six-axis load cells, and the three-dimensional knee kinematics were also recorded by a laboratory motion capture system. Machine learning algorithms were used to predict the knee moments and the tibial and femur vertical forces; 13 knees were utilized for training each model, while three were used for testing its accuracy (i.e., normalized root-mean-square error) and reliability (Bland-Altman limits of agreement). The results showed the models predicted force and knee moment values with acceptable levels of error and, although several models exhibited some form of bias, acceptable reliability. Further research will be needed to determine whether these types of models can be modified to attenuate the inevitable in vivo soft tissue motion artifact associated with highly dynamic activities like jump landings.
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Affiliation(s)
- Mirel Ajdaroski
- Department of Mechanical Engineering, University of Michigan – Dearborn, 4901 Evergreen Road, Dearborn, MI 48128
| | - So Young Baek
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109
| | | | - Amanda O. Esquivel
- Department of Mechanical Engineering, University of Michigan – Dearborn, 4901 Evergreen Road, Dearborn, MI 48128
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Adamczyk PG, Harper SE, Reiter AJ, Roembke RA, Wang Y, Nichols KM, Thelen DG. Wearable sensing for understanding and influencing human movement in ecological contexts. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2023; 28:100492. [PMID: 37663049 PMCID: PMC10469849 DOI: 10.1016/j.cobme.2023.100492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Wearable sensors offer a unique opportunity to study movement in ecological contexts - that is, outside the laboratory where movement happens in ordinary life. This article discusses the purpose, means, and impact of using wearable sensors to assess movement context, kinematics, and kinetics during locomotion, and how this information can be used to better understand and influence movement. We outline the types of information wearable sensors can gather and highlight recent developments in sensor technology, data analysis, and applications. We close with a vision for important future research and key questions the field will need to address to bring the potential benefits of wearable sensing to fruition.
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Affiliation(s)
- Peter Gabriel Adamczyk
- University of Wisconsin – Madison, Department of Mechanical Engineering, 1513 University Ave., Madison, Wisconsin, USA
| | - Sara E Harper
- University of Wisconsin – Madison, Department of Biomedical Engineering, 1550 Engineering Dr., Madison, Wisconsin, USA
| | - Alex J Reiter
- University of Wisconsin – Madison, Department of Mechanical Engineering, 1513 University Ave., Madison, Wisconsin, USA
| | - Rebecca A Roembke
- University of Wisconsin – Madison, Department of Mechanical Engineering, 1513 University Ave., Madison, Wisconsin, USA
| | - Yisen Wang
- University of Wisconsin – Madison, Department of Mechanical Engineering, 1513 University Ave., Madison, Wisconsin, USA
| | - Kieran M Nichols
- University of Wisconsin – Madison, Department of Mechanical Engineering, 1513 University Ave., Madison, Wisconsin, USA
| | - Darryl G. Thelen
- University of Wisconsin – Madison, Department of Mechanical Engineering, 1513 University Ave., Madison, Wisconsin, USA
- University of Wisconsin – Madison, Department of Biomedical Engineering, 1550 Engineering Dr., Madison, Wisconsin, USA
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Liew BXW, Rügamer D, Mei Q, Altai Z, Zhu X, Zhai X, Cortes N. Smooth and accurate predictions of joint contact force time-series in gait using over parameterised deep neural networks. Front Bioeng Biotechnol 2023; 11:1208711. [PMID: 37465692 PMCID: PMC10350628 DOI: 10.3389/fbioe.2023.1208711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 06/25/2023] [Indexed: 07/20/2023] Open
Abstract
Alterations in joint contact forces (JCFs) are thought to be important mechanisms for the onset and progression of many musculoskeletal and orthopaedic pain disorders. Computational approaches to JCFs assessment represent the only non-invasive means of estimating in-vivo forces; but this cannot be undertaken in free-living environments. Here, we used deep neural networks to train models to predict JCFs, using only joint angles as predictors. Our neural network models were generally able to predict JCFs with errors within published minimal detectable change values. The errors ranged from the lowest value of 0.03 bodyweight (BW) (ankle medial-lateral JCF in walking) to a maximum of 0.65BW (knee VT JCF in running). Interestingly, we also found that over parametrised neural networks by training on longer epochs (>100) resulted in better and smoother waveform predictions. Our methods for predicting JCFs using only joint kinematics hold a lot of promise in allowing clinicians and coaches to continuously monitor tissue loading in free-living environments.
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Affiliation(s)
- Bernard X. W. Liew
- School of Sport, Rehabilitation, and Exercise Sciences, University of Essex, Colchester, United Kingdom
| | - David Rügamer
- Department of Statistics, Ludwig-Maximilians-Universität München, Munich, Germany
- Munich Center for Machine Learning, Munich, Germany
| | - Qichang Mei
- Faculty of Sports Science, Ningbo University, Ningbo, China
- Research Academy of Grand Health, Ningbo University, Ningbo, China
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Zainab Altai
- School of Sport, Rehabilitation, and Exercise Sciences, University of Essex, Colchester, United Kingdom
| | - Xuqi Zhu
- School of Computer Science and Electrical Engineering, University of Essex, Colchester, United Kingdom
| | - Xiaojun Zhai
- School of Computer Science and Electrical Engineering, University of Essex, Colchester, United Kingdom
| | - Nelson Cortes
- School of Sport, Rehabilitation, and Exercise Sciences, University of Essex, Colchester, United Kingdom
- Department of Bioengineering, George Mason University, Fairfax, VA, United States
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Wang D, Li S, Song Q, Mao D, Hao W. Predicting vertical ground reaction force in rearfoot running: A wavelet neural network model and factor loading. J Sports Sci 2023; 41:955-963. [PMID: 37634140 DOI: 10.1080/02640414.2023.2251767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 08/17/2023] [Indexed: 08/29/2023]
Abstract
This study proposed a simple method for selecting input variables by factor loading and inputting these variables into a wavelet neural network (WNN) model to predict vertical ground reaction force (vGRF). The kinematic data and vGRF of 9 rearfoot strikers at 12, 14, and 16 km/h were collected using a motion capture system and an instrumented treadmill. The input variables were screened by factor loading and utilized to predict vGRF with the WNN. Nine kinematic variables were selected, corresponding to nine principal components, mainly focusing on the knee and ankle joints. The prediction results of vGRF were effective and accurate at different speeds, namely, the coefficient of multiple correlation (CMC) > 0.98 (0.984-0.988), the normalized root means square error (NRMSE) < 15% (9.34-11.51%). The NRMSEs of impact force (8.18-10.01%), active force (4.92-7.42%), and peak time (7.16-12.52%) were less than 15%. There was a small number (peak, 4.12-6.18%; time, 4.71-6.76%) exceeding the 95% confidence interval (CI) using the Bland-Altman method. The knee joint was the optimal location for estimating vGRF, followed by the ankle. There were high accuracy and agreement for predicting vGRF with the peak and peak time at 12, 14, and 16 km/h. Therefore, factor loading could be a valid method to screen kinematic variables in artificial neural networks.
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Affiliation(s)
- Dongmei Wang
- Biomechanics Laboratory College of Human Movement Science, Beijing Sport University, Beijing, China
- Department of Sport and Health, Shandong Sport University, Jinan, China
| | - Shangxiao Li
- Research Center for Sports Psychology and Biomechanics, China Institute of Sport Science, Beijing, China
| | - Qipeng Song
- Department of Sport and Health, Shandong Sport University, Jinan, China
| | - Dewei Mao
- Department of Sport and Health, Shandong Sport University, Jinan, China
| | - Weiya Hao
- Research Center for Sports Psychology and Biomechanics, China Institute of Sport Science, Beijing, China
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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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Helwig J, Diels J, Röll M, Mahler H, Gollhofer A, Roecker K, Willwacher S. Relationships between External, Wearable Sensor-Based, and Internal Parameters: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23020827. [PMID: 36679623 PMCID: PMC9864675 DOI: 10.3390/s23020827] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 01/05/2023] [Accepted: 01/09/2023] [Indexed: 05/27/2023]
Abstract
Micro electro-mechanical systems (MEMS) are used to record training and match play of intermittent team sport athletes. Paired with estimates of internal responses or adaptations to exercise, practitioners gain insight into players' dose-response relationship which facilitates the prescription of the training stimuli to optimize performance, prevent injuries, and to guide rehabilitation processes. A systematic review on the relationship between external, wearable-based, and internal parameters in team sport athletes, compliant with the PRISMA guidelines, was conducted. The literature research was performed from earliest record to 1 September 2020 using the databases PubMed, Web of Science, CINAHL, and SportDISCUS. A total of 66 full-text articles were reviewed encompassing 1541 athletes. About 109 different relationships between variables have been reviewed. The most investigated relationship across sports was found between (session) rating of perceived exertion ((session-)RPE) and PlayerLoad™ (PL) with, predominantly, moderate to strong associations (r = 0.49-0.84). Relationships between internal parameters and highly dynamic, anaerobic movements were heterogenous. Relationships between average heart rate (HR), Edward's and Banister's training impulse (TRIMP) seem to be reflected in parameters of overall activity such as PL and TD for running-intensive team sports. PL may further be suitable to estimate the overall subjective perception. To identify high fine-structured loading-relative to a certain type of sport-more specific measures and devices are needed. Individualization of parameters could be helpful to enhance practicality.
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Affiliation(s)
- Janina Helwig
- Institute of Sport and Sport Science, Albert-Ludwigs University Freiburg, 79117 Freiburg, Germany
- Institute for Advanced Biomechanics and Motion Studies, Offenburg University, Max-Planck Straße 1, 77656 Offenburg, Germany
| | - Janik Diels
- Institute of Sport and Sport Science, Albert-Ludwigs University Freiburg, 79117 Freiburg, Germany
| | - Mareike Röll
- Institute of Sport and Sport Science, Albert-Ludwigs University Freiburg, 79117 Freiburg, Germany
| | - Hubert Mahler
- Institute of Sport and Sport Science, Albert-Ludwigs University Freiburg, 79117 Freiburg, Germany
- Sport-Club Freiburg e.V., Achim-Stocker-Str. 1, 79108 Freiburg, Germany
| | - Albert Gollhofer
- Institute of Sport and Sport Science, Albert-Ludwigs University Freiburg, 79117 Freiburg, Germany
| | - Kai Roecker
- Institute of Sport and Sport Science, Albert-Ludwigs University Freiburg, 79117 Freiburg, Germany
- Institute for Applied Health Promotion and Exercise Medicine, Furtwangen University, 78120 Furtwangen, Germany
| | - Steffen Willwacher
- Institute for Advanced Biomechanics and Motion Studies, Offenburg University, Max-Planck Straße 1, 77656 Offenburg, Germany
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Inai T, Takabayashi T. Estimation of lower-limb sagittal joint moments during gait using vertical ground reaction force. J Biomech 2022; 145:111389. [PMID: 36410202 DOI: 10.1016/j.jbiomech.2022.111389] [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: 05/17/2022] [Revised: 10/21/2022] [Accepted: 11/14/2022] [Indexed: 11/18/2022]
Abstract
Lower-limb sagittal joint moments during gait are important variables for evaluating the risk of disease progression, such as that of orthopedic diseases. Therefore, quantifying lower-limb sagittal joint moments during walking is important to continuously evaluate the risk of disease progression. A motion capture system and force plate are employed in the calculation of lower-limb sagittal joint moments during gait. However, they cannot be used during daily walking. Therefore, it is important to estimate these moments during walking from the vertical ground reaction force (vGRF), which can be measured using a wearable sensor, such as an insole device. Thus, this study aimed to estimate the lower-limb sagittal joint moments during gait using only the vGRF and confirmed its accuracy. This study included 188 healthy adults, and each participant walked at a comfortable speed (10 trials). We estimated the moments from the vGRF using a feedforward neural network. Our major findings are that our method can estimate lower-limb sagittal joint moments using the vGRF with accuracies of NRMSE¯ within 6.0-11.7% (NRMSEs¯ of the hip, knee, and ankle were 8.4, 11.7, and 6.0%, respectively). To the best of our knowledge, this study is the first to estimate lower-limb sagittal joint moments (including those of the hip, knee, and ankle joints) during gait using only the vGRF. Our method may be useful to estimate lower-limb sagittal joint moments during daily walking using only the vGRF, which can be measured by an insole device in the future.
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Affiliation(s)
- Takuma Inai
- QOL and Materials Research Group, National Institute of Advanced Industrial Science and Technology, Japan.
| | - Tomoya Takabayashi
- Institute for Human Movement and Medical Sciences, Niigata University of Health and Welfare, Japan
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Bach MM, Dominici N, Daffertshofer A. Predicting vertical ground reaction forces from 3D accelerometry using reservoir computers leads to accurate gait event detection. Front Sports Act Living 2022; 4:1037438. [PMID: 36385782 PMCID: PMC9644164 DOI: 10.3389/fspor.2022.1037438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/04/2022] [Indexed: 11/06/2022] Open
Abstract
Accelerometers are low-cost measurement devices that can readily be used outside the lab. However, determining isolated gait events from accelerometer signals, especially foot-off events during running, is an open problem. We outline a two-step approach where machine learning serves to predict vertical ground reaction forces from accelerometer signals, followed by force-based event detection. We collected shank accelerometer signals and ground reaction forces from 21 adults during comfortable walking and running on an instrumented treadmill. We trained one common reservoir computer using segmented data using both walking and running data. Despite being trained on just a small number of strides, this reservoir computer predicted vertical ground reaction forces in continuous gait with high quality. The subsequent foot contact and foot off event detection proved highly accurate when compared to the gold standard based on co-registered ground reaction forces. Our proof-of-concept illustrates the capacity of combining accelerometry with machine learning for detecting isolated gait events irrespective of mode of locomotion.
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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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Fusion of Wearable Kinetic and Kinematic Sensors to Estimate Triceps Surae Work during Outdoor Locomotion on Slopes. SENSORS 2022; 22:s22041589. [PMID: 35214491 PMCID: PMC8880119 DOI: 10.3390/s22041589] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 02/07/2022] [Accepted: 02/15/2022] [Indexed: 02/04/2023]
Abstract
Muscle–tendon power output is commonly assessed in the laboratory through the work loop, a paired analysis of muscle force and length during a cyclic task. Work-loop analysis of muscle–tendon function in out-of-lab conditions has been elusive due to methodological limitations. In this work, we combined kinetic and kinematic measures from shear wave tensiometry and inertial measurement units, respectively, to establish a wearable system for estimating work and power output from the soleus and gastrocnemius muscles during outdoor locomotion. Across 11 healthy young adults, we amassed 4777 strides of walking on slopes from −10° to +10°. Results showed that soleus work scales with incline, while gastrocnemius work is relatively insensitive to incline. These findings agree with previous results from laboratory-based studies while expanding technological capabilities by enabling wearable analysis of muscle–tendon kinetics. Applying this system in additional settings and activities could improve biomechanical knowledge and evaluation of protocols in scenarios such as rehabilitation, device design, athletics, and military training.
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Lam SK, Vujaklija I. Joint Torque Prediction via Hybrid Neuromusculoskeletal Modelling during Gait Using Statistical Ground Reaction Estimates: An Exploratory Study. SENSORS 2021; 21:s21196597. [PMID: 34640917 PMCID: PMC8512679 DOI: 10.3390/s21196597] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 01/03/2023]
Abstract
Joint torques of lower extremity are important clinical indicators of gait capability. This parameter can be quantified via hybrid neuromusculoskeletal modelling that combines electromyography-driven modelling and static optimisation. The simulations rely on kinematics and external force measurements, for example, ground reaction forces (GRF) and the corresponding centres of pressure (COP), which are conventionally acquired using force plates. This bulky equipment, however, hinders gait analysis in real-world environments. While this portability issue could potentially be solved by estimating the parameters through machine learning, the effect of the estimation errors on joint torque prediction with biomechanical models remains to be investigated. This study first estimated GRF and COP through feedforward artificial neural networks, and then leveraged them to predict lower-limb sagittal joint torques via (i) inverse dynamics and (ii) hybrid modelling. The approach was evaluated on five healthy subjects, individually. The predicted torques were validated with the measured torques, showing that hip was the most sensitive whereas ankle was the most resistive to the GRF/COP estimates for both models, with average metrics values being 0.70 < R2 < 0.97 and 0.069 < RMSE < 0.15 (Nm/kg). This study demonstrated the feasibility of torque prediction based on personalised (neuro)musculoskeletal modelling using statistical ground reaction estimates, thus providing insights into potential real-world mobile joint torque quantification.
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