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Bonato P, Feipel V, Corniani G, Arin-Bal G, Leardini A. Position paper on how technology for human motion analysis and relevant clinical applications have evolved over the past decades: Striking a balance between accuracy and convenience. Gait Posture 2024; 113:191-203. [PMID: 38917666 DOI: 10.1016/j.gaitpost.2024.06.007] [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: 01/24/2024] [Revised: 05/30/2024] [Accepted: 06/10/2024] [Indexed: 06/27/2024]
Abstract
BACKGROUND Over the past decades, tremendous technological advances have emerged in human motion analysis (HMA). RESEARCH QUESTION How has technology for analysing human motion evolved over the past decades, and what clinical applications has it enabled? METHODS The literature on HMA has been extensively reviewed, focusing on three main approaches: Fully-Instrumented Gait Analysis (FGA), Wearable Sensor Analysis (WSA), and Deep-Learning Video Analysis (DVA), considering both technical and clinical aspects. RESULTS FGA techniques relying on data collected using stereophotogrammetric systems, force plates, and electromyographic sensors have been dramatically improved providing highly accurate estimates of the biomechanics of motion. WSA techniques have been developed with the advances in data collection at home and in community settings. DVA techniques have emerged through artificial intelligence, which has marked the last decade. Some authors have considered WSA and DVA techniques as alternatives to "traditional" HMA techniques. They have suggested that WSA and DVA techniques are destined to replace FGA. SIGNIFICANCE We argue that FGA, WSA, and DVA complement each other and hence should be accounted as "synergistic" in the context of modern HMA and its clinical applications. We point out that DVA techniques are especially attractive as screening techniques, WSA methods enable data collection in the home and community for extensive periods of time, and FGA does maintain superior accuracy and should be the preferred technique when a complete and highly accurate biomechanical data is required. Accordingly, we envision that future clinical applications of HMA would favour screening patients using DVA in the outpatient setting. If deemed clinically appropriate, then WSA would be used to collect data in the home and community to derive relevant information. If accurate kinetic data is needed, then patients should be referred to specialized centres where an FGA system is available, together with medical imaging and thorough clinical assessments.
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Affiliation(s)
- Paolo Bonato
- Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, USA
| | - Véronique Feipel
- Laboratory of Functional Anatomy, Faculty of Motor Sciences, Laboratory of Anatomy, Biomechanics and Organogenesis, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium
| | - Giulia Corniani
- Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, USA
| | - Gamze Arin-Bal
- Faculty of Physical Therapy and Rehabilitation, Hacettepe University, Ankara, Turkey; Movement Analysis Laboratory, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.
| | - Alberto Leardini
- Movement Analysis Laboratory, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
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Xia Z, Cornish BM, Devaprakash D, Barrett RS, Lloyd DG, Hams AH, Pizzolato C. Prediction of Achilles Tendon Force During Common Motor Tasks From Markerless Video. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2070-2077. [PMID: 38787676 DOI: 10.1109/tnsre.2024.3403092] [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: 05/26/2024]
Abstract
Remodeling of the Achilles tendon (AT) is partly driven by its mechanical environment. AT force can be estimated with neuromusculoskeletal (NMSK) modeling; however, the complex experimental setup required to perform the analyses confines use to the laboratory. We developed task-specific long short-term memory (LSTM) neural networks that employ markerless video data to predict the AT force during walking, running, countermovement jump, single-leg landing, and single-leg heel rise. The task-specific LSTM models were trained on pose estimation keypoints and corresponding AT force data from 16 subjects, calculated via an established NMSK modeling pipeline, and cross-validated using a leave-one-subject-out approach. As proof-of-concept, new motion data of one participant was collected with two smartphones and used to predict AT forces. The task-specific LSTM models predicted the time-series AT force using synthesized pose estimation data with root mean square error (RMSE) ≤ 526 N, normalized RMSE (nRMSE) ≤ 0.21 , R 2 ≥ 0.81 . Walking task resulted the most accurate with RMSE = 189±62 N; nRMSE = 0.11±0.03 , R 2 = 0.92±0.04 . AT force predicted with smartphones video data was physiologically plausible, agreeing in timing and magnitude with established force profiles. This study demonstrated the feasibility of using low-cost solutions to deploy complex biomechanical analyses outside the laboratory.
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Lee P, Chen TB, Lin HY, Yeh LR, Liu CH, Chen YL. Integrating OpenPose and SVM for Quantitative Postural Analysis in Young Adults: A Temporal-Spatial Approach. Bioengineering (Basel) 2024; 11:548. [PMID: 38927784 PMCID: PMC11200693 DOI: 10.3390/bioengineering11060548] [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] [Received: 04/29/2024] [Revised: 05/12/2024] [Accepted: 05/25/2024] [Indexed: 06/28/2024] Open
Abstract
Noninvasive tracking devices are widely used to monitor real-time posture. Yet significant potential exists to enhance postural control quantification through walking videos. This study advances computational science by integrating OpenPose with a Support Vector Machine (SVM) to perform highly accurate and robust postural analysis, marking a substantial improvement over traditional methods which often rely on invasive sensors. Utilizing OpenPose-based deep learning, we generated Dynamic Joint Nodes Plots (DJNP) and iso-block postural identity images for 35 young adults in controlled walking experiments. Through Temporal and Spatial Regression (TSR) models, key features were extracted for SVM classification, enabling the distinction between various walking behaviors. This approach resulted in an overall accuracy of 0.990 and a Kappa index of 0.985. Cutting points for the ratio of top angles (TAR) and the ratio of bottom angles (BAR) effectively differentiated between left and right skews with AUC values of 0.772 and 0.775, respectively. These results demonstrate the efficacy of integrating OpenPose with SVM, providing more precise, real-time analysis without invasive sensors. Future work will focus on expanding this method to a broader demographic, including individuals with gait abnormalities, to validate its effectiveness across diverse clinical conditions. Furthermore, we plan to explore the integration of alternative machine learning models, such as deep neural networks, enhancing the system's robustness and adaptability for complex dynamic environments. This research opens new avenues for clinical applications, particularly in rehabilitation and sports science, promising to revolutionize noninvasive postural analysis.
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Affiliation(s)
- Posen Lee
- Department of Occupational Therapy, College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan;
| | - Tai-Been Chen
- Department of Radiological Technology, Faculty of Medical Technology, Teikyo University, Tokyo 173-8605, Japan;
| | - Hung-Yu Lin
- Department of Occupational Therapy, College of Medical and Health Science, Asia University, Taichung 41354, Taiwan;
| | - Li-Ren Yeh
- Department of Anesthesiology, E-DA Cancer Hospital, I-Shou University, Kaohsiung 82445, Taiwan;
| | - Chin-Hsuan Liu
- Department of Occupational Therapy, College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan;
| | - Yen-Lin Chen
- Department of Computer Science and Information Engineering, College of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei 10608, Taiwan;
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Chaumeil A, Lahkar BK, Dumas R, Muller A, Robert T. Agreement between a markerless and a marker-based motion capture systems for balance related quantities. J Biomech 2024; 165:112018. [PMID: 38412623 DOI: 10.1016/j.jbiomech.2024.112018] [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: 07/21/2023] [Revised: 02/07/2024] [Accepted: 02/19/2024] [Indexed: 02/29/2024]
Abstract
Balance studies usually focus on quantities describing the global body motion. Assessing such quantities using classical marker-based approach can be tedious and modify the participant's behaviour. The recent development of markerless motion capture methods could bypass the issues related to the use of markers. This work compared dynamic balance related quantities obtained with markers and videos. Sixteen young healthy participants performed four different motor tasks: walking at self-selected speed, balance loss, walking on a narrow beam and countermovement jumps. Their movements were recorded simultaneously by marker-based and markerless motion capture systems. Videos were processed using a commercial markerless pose estimation software, Theia3D. The centre of mass position (CoM) was computed, and the associated extrapolated centre of mass position (XCoM) and whole-body angular momentum (WBAM) were derived. Bland-Altman analysis was performed and root mean square difference (RMSD) and coefficient of correlation were computed to compare the results obtained with marker-based and markerless methods. Bias remained of the magnitude of a few mm for CoM and XCoM positions, and RMSD of CoM and XCoM was around 1 cm. RMSD of the WBAM was less than 10 % of the total amplitude in any direction, and bias was less than 1 %. Results suggest that outcomes of balance studies will be similar whether marker-based or markerless motion capture system are used. Nevertheless, one should be careful when assessing dynamic movements such as jumping, as they displayed the biggest differences (both bias and RMSD), although it is unclear whether these differences are due to errors in markerless or marker-based motion capture system.
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Affiliation(s)
- Anaïs Chaumeil
- Univ Lyon, Univ Gustave Eiffel, Univ Claude Bernard Lyon 1, LBMC UMR_T 9406, F-69622 Lyon, France
| | - Bhrigu Kumar Lahkar
- Univ Lyon, Univ Gustave Eiffel, Univ Claude Bernard Lyon 1, LBMC UMR_T 9406, F-69622 Lyon, France
| | - Raphaël Dumas
- Univ Lyon, Univ Gustave Eiffel, Univ Claude Bernard Lyon 1, LBMC UMR_T 9406, F-69622 Lyon, France.
| | - Antoine Muller
- Univ Lyon, Univ Gustave Eiffel, Univ Claude Bernard Lyon 1, LBMC UMR_T 9406, F-69622 Lyon, France
| | - Thomas Robert
- Univ Lyon, Univ Gustave Eiffel, Univ Claude Bernard Lyon 1, LBMC UMR_T 9406, F-69622 Lyon, France
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Cimorelli A, Patel A, Karakostas T, Cotton RJ. Validation of portable in-clinic video-based gait analysis for prosthesis users. Sci Rep 2024; 14:3840. [PMID: 38360820 PMCID: PMC10869722 DOI: 10.1038/s41598-024-53217-7] [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: 01/17/2023] [Accepted: 01/30/2024] [Indexed: 02/17/2024] Open
Abstract
Despite the common focus of gait in rehabilitation, there are few tools that allow quantitatively characterizing gait in the clinic. We recently described an algorithm, trained on a large dataset from our clinical gait analysis laboratory, which produces accurate cycle-by-cycle estimates of spatiotemporal gait parameters including step timing and walking velocity. Here, we demonstrate this system generalizes well to clinical care with a validation study on prosthetic users seen in therapy and outpatient clinics. Specifically, estimated walking velocity was similar to annotated 10-m walking velocities, and cadence and foot contact times closely mirrored our wearable sensor measurements. Additionally, we found that a 2D keypoint detector pretrained on largely able-bodied individuals struggles to localize prosthetic joints, particularly for those individuals with more proximal or bilateral amputations, but after training a prosthetic-specific joint detector video-based gait analysis also works on these individuals. Further work is required to validate the other outputs from our algorithm including sagittal plane joint angles and step length. Code for the gait transformer and the trained weights are available at https://github.com/peabody124/GaitTransformer .
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Affiliation(s)
| | - Ankit Patel
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Department of Electrical & Computer Engineering, Rice University, Houston, USA
| | - Tasos Karakostas
- Shirley Ryan AbilityLab, Chicago, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University, Evanston, USA
| | - R James Cotton
- Shirley Ryan AbilityLab, Chicago, USA.
- Department of Physical Medicine and Rehabilitation, Northwestern University, Evanston, USA.
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Gildea K, Hall D, Cherry CR, Simms C. Forward dynamics computational modelling of a cyclist fall with the inclusion of protective response using deep learning-based human pose estimation. J Biomech 2024; 163:111959. [PMID: 38286096 DOI: 10.1016/j.jbiomech.2024.111959] [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: 03/30/2023] [Revised: 12/16/2023] [Accepted: 01/17/2024] [Indexed: 01/31/2024]
Abstract
Single bicycle crashes, i.e., falls and impacts not involving a collision with another road user, are a significantly underestimated road safety problem. The motions and behaviours of falling people, or fall kinematics, are often investigated in the injury biomechanics research field. Understanding the mechanics of a fall can help researchers develop better protective gear and safety measures to reduce the risk of injury. However, little is known about cyclist fall kinematics or dynamics. Therefore, in this study, a video analysis of cyclist falls is performed to investigate common kinematic forms and impact patterns. Furthermore, a pipeline involving deep learning-based human pose estimation and inverse kinematics optimisation is created for extracting human motion from real-world footage of falls to initialise forward dynamics computational human body models. A bracing active response is then optimised for using a genetic algorithm. This is then applied to a case study of a cyclist fall. The kinematic forms characterised in this study can be used to inform initial conditions for computational modelling and injury estimation in cyclist falls. Findings indicate that protective response is an important consideration in fall kinematics and dynamics, and should be included in computational modelling. Furthermore, the novel reconstruction pipeline proposed here can be applied more broadly for traumatic injury biomechanics tasks. The tool developed in this study is available at https://kevgildea.github.io/KinePose/.
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Affiliation(s)
- Kevin Gildea
- Department of Mechanical, Manufacturing & Biomedical Engineering, Trinity College Dublin, Ireland; Department of Technology & Society, Faculty of Engineering, Lund University, Sweden.
| | - Daniel Hall
- Department of Technology & Society, Faculty of Engineering, Lund University, Sweden
| | | | - Ciaran Simms
- Department of Mechanical, Manufacturing & Biomedical Engineering, Trinity College Dublin, Ireland
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Obukhov A, Volkov A, Pchelintsev A, Nazarova A, Teselkin D, Surkova E, Fedorchuk I. Examination of the Accuracy of Movement Tracking Systems for Monitoring Exercise for Musculoskeletal Rehabilitation. SENSORS (BASEL, SWITZERLAND) 2023; 23:8058. [PMID: 37836887 PMCID: PMC10575050 DOI: 10.3390/s23198058] [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: 08/19/2023] [Revised: 09/15/2023] [Accepted: 09/22/2023] [Indexed: 10/15/2023]
Abstract
When patients perform musculoskeletal rehabilitation exercises, it is of great importance to observe the correctness of their performance. The aim of this study is to increase the accuracy of recognizing human movements during exercise. The process of monitoring and evaluating musculoskeletal rehabilitation exercises was modeled using various tracking systems, and the necessary algorithms for processing information for each of the tracking systems were formalized. An approach to classifying exercises using machine learning methods is presented. Experimental studies were conducted to identify the most accurate tracking systems (virtual reality trackers, motion capture, and computer vision). A comparison of machine learning models is carried out to solve the problem of classifying musculoskeletal rehabilitation exercises, and 96% accuracy is obtained when using multilayer dense neural networks. With the use of computer vision technologies and the processing of a full set of body points, the accuracy of classification achieved is 100%. The hypotheses on the ranking of tracking systems based on the accuracy of positioning of human target points, the presence of restrictions on application in the field of musculoskeletal rehabilitation, and the potential to classify exercises are fully confirmed.
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Affiliation(s)
- Artem Obukhov
- Laboratory of VR Simulators, Tambov State Technical University, 392000 Tambov, Russia; (A.V.); (A.N.); (D.T.); (E.S.); (I.F.)
| | - Andrey Volkov
- Laboratory of VR Simulators, Tambov State Technical University, 392000 Tambov, Russia; (A.V.); (A.N.); (D.T.); (E.S.); (I.F.)
| | - Alexander Pchelintsev
- Department of Higher Mathematics, Tambov State Technical University, 392000 Tambov, Russia;
| | - Alexandra Nazarova
- Laboratory of VR Simulators, Tambov State Technical University, 392000 Tambov, Russia; (A.V.); (A.N.); (D.T.); (E.S.); (I.F.)
| | - Daniil Teselkin
- Laboratory of VR Simulators, Tambov State Technical University, 392000 Tambov, Russia; (A.V.); (A.N.); (D.T.); (E.S.); (I.F.)
| | - Ekaterina Surkova
- Laboratory of VR Simulators, Tambov State Technical University, 392000 Tambov, Russia; (A.V.); (A.N.); (D.T.); (E.S.); (I.F.)
| | - Ivan Fedorchuk
- Laboratory of VR Simulators, Tambov State Technical University, 392000 Tambov, Russia; (A.V.); (A.N.); (D.T.); (E.S.); (I.F.)
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Cotton RJ, Cimorelli A, Shah K, Anarwala S, Uhlrich S, Karakostas T. Improved Trajectory Reconstruction for Markerless Pose Estimation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-7. [PMID: 38083280 DOI: 10.1109/embc40787.2023.10340745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Markerless pose estimation allows reconstructing human movement from multiple synchronized and calibrated views, and has the potential to make movement analysis easy and quick, including gait analysis. This could enable much more frequent and quantitative characterization of gait impairments, allowing better monitoring of outcomes and responses to interventions. However, the impact of different keypoint detectors and reconstruction algorithms on markerless pose estimation accuracy has not been thoroughly evaluated. We tested these algorithmic choices on data acquired from a multicamera system from a heterogeneous sample of 53 individuals in a rehabilitation hospital. We found that using a top-down keypoint detector and reconstructing trajectories with an implicit function enabled accurate, smooth, and anatomically plausible trajectories, with a noise in the step width estimates compared to a GaitRite walkway of only 9mm.
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Bittner M, Yang WT, Zhang X, Seth A, van Gemert J, van der Helm FCT. Towards Single Camera Human 3D-Kinematics. SENSORS (BASEL, SWITZERLAND) 2022; 23:341. [PMID: 36616937 PMCID: PMC9823525 DOI: 10.3390/s23010341] [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: 11/01/2022] [Revised: 12/17/2022] [Accepted: 12/21/2022] [Indexed: 06/17/2023]
Abstract
Markerless estimation of 3D Kinematics has the great potential to clinically diagnose and monitor movement disorders without referrals to expensive motion capture labs; however, current approaches are limited by performing multiple de-coupled steps to estimate the kinematics of a person from videos. Most current techniques work in a multi-step approach by first detecting the pose of the body and then fitting a musculoskeletal model to the data for accurate kinematic estimation. Errors in training data of the pose detection algorithms, model scaling, as well the requirement of multiple cameras limit the use of these techniques in a clinical setting. Our goal is to pave the way toward fast, easily applicable and accurate 3D kinematic estimation. To this end, we propose a novel approach for direct 3D human kinematic estimation D3KE from videos using deep neural networks. Our experiments demonstrate that the proposed end-to-end training is robust and outperforms 2D and 3D markerless motion capture based kinematic estimation pipelines in terms of joint angles error by a large margin (35% from 5.44 to 3.54 degrees). We show that D3KE is superior to the multi-step approach and can run at video framerate speeds. This technology shows the potential for clinical analysis from mobile devices in the future.
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Affiliation(s)
- Marian Bittner
- Vicarious Perception Technologies (VicarVision), 1015 AH Amsterdam, The Netherlands
- Computer Vision Lab, Delft University of Technology, 2628 XE Delft, The Netherlands
- Biomechanical Engineering, Delft University of Technology, 2628 CN Delft, The Netherlands
| | - Wei-Tse Yang
- Computer Vision Lab, Delft University of Technology, 2628 XE Delft, The Netherlands
| | - Xucong Zhang
- Computer Vision Lab, Delft University of Technology, 2628 XE Delft, The Netherlands
| | - Ajay Seth
- Biomechanical Engineering, Delft University of Technology, 2628 CN Delft, The Netherlands
| | - Jan van Gemert
- Computer Vision Lab, Delft University of Technology, 2628 XE Delft, The Netherlands
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Baca A, Dabnichki P, Hu CW, Kornfeind P, Exel J. Ubiquitous Computing in Sports and Physical Activity-Recent Trends and Developments. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22218370. [PMID: 36366068 PMCID: PMC9659168 DOI: 10.3390/s22218370] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 10/27/2022] [Accepted: 10/28/2022] [Indexed: 05/27/2023]
Abstract
The use of small, interconnected and intelligent tools within the broad framework of pervasive computing for analysis and assessments in sport and physical activity is not a trend in itself but defines a way for information to be handled, processed and utilised: everywhere, at any time. The demand for objective data to support decision making prompted the adoption of wearables that evolve to fulfil the aims of assessing athletes and practitioners as closely as possible with their performance environments. In the present paper, we mention and discuss the advancements in ubiquitous computing in sports and physical activity in the past 5 years. Thus, recent developments in wearable sensors, cloud computing and artificial intelligence tools have been the pillars for a major change in the ways sport-related analyses are performed. The focus of our analysis is wearable technology, computer vision solutions for markerless tracking and their major contribution to the process of acquiring more representative data from uninhibited actions in realistic ecological conditions. We selected relevant literature on the applications of such approaches in various areas of sports and physical activity while outlining some limitations of the present-day data acquisition and data processing practices and the resulting sensors' functionalities, as well as the limitations to the data-driven informed decision making in the current technological and scientific framework. Finally, we hypothesise that a continuous merger of measurement, processing and analysis will lead to the development of more reliable models utilising the advantages of open computing and unrestricted data access and allow for the development of personalised-medicine-type approaches to sport training and performance.
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Affiliation(s)
- Arnold Baca
- Centre for Sport Science and University Sports, University of Vienna, 1150 Vienna, Austria
| | - Peter Dabnichki
- STEM College, RMIT University, Melbourne, VIC 3000, Australia
| | - Che-Wei Hu
- STEM College, RMIT University, Melbourne, VIC 3000, Australia
| | - Philipp Kornfeind
- Centre for Sport Science and University Sports, University of Vienna, 1150 Vienna, Austria
| | - Juliana Exel
- Centre for Sport Science and University Sports, University of Vienna, 1150 Vienna, Austria
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A New Approach for the Tribological and Mechanical Characterization of a Hip Prosthesis Trough a Numerical Model Based on Artificial Intelligence Algorithms and Humanoid Multibody Model. LUBRICANTS 2022. [DOI: 10.3390/lubricants10070160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In recent years, thanks to the development of additive manufacturing techniques, pros-thetic surgery has reached increasingly cutting-edge levels, revolutionizing the clinical course of patients suffering from joint arthritis, rheumatoid arthritis, post-traumatic arthrosis, etc. This work aims to evaluate the best materials for prosthetic surgery in hip implants from a tribological and mechanical point of view by using a machine-learning algorithm coupling with multi-body modeling and Finite Element Method (FEM) simulations. The innovative aspect is represented by the use of machine learning for the creation of a humanoid model in a multibody software environment that aimed to evaluate the load and rotation condition at the hip joint. After the boundary conditions have been defined, a Finite Element (FE) model of the hip implant has been created. The material properties and the information on the tribological behavior of the material couplings under investigation have been obtained from literature studies. The wear process has been investigated through the implementation of the Archard’s wear law in the FE model. The results of the FE simulation show that the best wear behavior has been obtained by CoCr alloy/UHMWPE coupling with a volume loss due to a wear of 0.004 μm3 at the end of the simulation of ten sitting cycles. After the best pairs in terms of wear has been established, a topology optimization of the whole hip implant structure has been performed. The results show that, after the optimization process, it was possible to reduce implant mass making the implant 28.12% more lightweight with respect to the original one.
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12
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Lee P, Chen TB, Liu CH, Wang CY, Huang GH, Lu NH. Identifying the Posture of Young Adults in Walking Videos by Using a Fusion Artificial Intelligent Method. BIOSENSORS 2022; 12:bios12050295. [PMID: 35624595 PMCID: PMC9139042 DOI: 10.3390/bios12050295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 04/29/2022] [Accepted: 05/02/2022] [Indexed: 11/23/2022]
Abstract
Many neurological and musculoskeletal disorders are associated with problems related to postural movement. Noninvasive tracking devices are used to record, analyze, measure, and detect the postural control of the body, which may indicate health problems in real time. A total of 35 young adults without any health problems were recruited for this study to participate in a walking experiment. An iso-block postural identity method was used to quantitatively analyze posture control and walking behavior. The participants who exhibited straightforward walking and skewed walking were defined as the control and experimental groups, respectively. Fusion deep learning was applied to generate dynamic joint node plots by using OpenPose-based methods, and skewness was qualitatively analyzed using convolutional neural networks. The maximum specificity and sensitivity achieved using a combination of ResNet101 and the naïve Bayes classifier were 0.84 and 0.87, respectively. The proposed approach successfully combines cell phone camera recordings, cloud storage, and fusion deep learning for posture estimation and classification.
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Affiliation(s)
- Posen Lee
- Department of Occupation Therapy, I-Shou University, No. 8, Yida Road, Jiaosu Village, Yanchao District, Kaohsiung 82445, Taiwan;
| | - Tai-Been Chen
- Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road, Jiaosu Village Yanchao District, Kaohsiung 82445, Taiwan; (T.-B.C.); (C.-Y.W.); (N.-H.L.)
- Institute of Statistics, National Yang Ming Chiao Tung University, No. 1001, University Road, Hsinchu 30010, Taiwan;
| | - Chin-Hsuan Liu
- Department of Occupation Therapy, I-Shou University, No. 8, Yida Road, Jiaosu Village, Yanchao District, Kaohsiung 82445, Taiwan;
- Department of Occupational Therapy, Kaohsiung Municipal Kai-Syuan Psychiatric Hospital, No. 130, Kaisyuan 2nd Road, Lingya District, Kaohsiung 80276, Taiwan
- Correspondence: ; Tel.: +886-7-6151100 (ext. 7516)
| | - Chi-Yuan Wang
- Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road, Jiaosu Village Yanchao District, Kaohsiung 82445, Taiwan; (T.-B.C.); (C.-Y.W.); (N.-H.L.)
| | - Guan-Hua Huang
- Institute of Statistics, National Yang Ming Chiao Tung University, No. 1001, University Road, Hsinchu 30010, Taiwan;
| | - Nan-Han Lu
- Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road, Jiaosu Village Yanchao District, Kaohsiung 82445, Taiwan; (T.-B.C.); (C.-Y.W.); (N.-H.L.)
- Department of Pharmacy, Tajen University, No. 20, Weixin Road, Yanpu Township, Pingtung County 90741, Taiwan
- Department of Radiology, E-DA Hospital, I-Shou University, No. 1, Yida Road, Jiaosu Village, Yanchao District, Kaohsiung City 82445, Taiwan
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Pagnon D, Domalain M, Reveret L. Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 2: Accuracy. SENSORS 2022; 22:s22072712. [PMID: 35408326 PMCID: PMC9002957 DOI: 10.3390/s22072712] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 03/21/2022] [Accepted: 03/27/2022] [Indexed: 02/04/2023]
Abstract
Two-dimensional deep-learning pose estimation algorithms can suffer from biases in joint pose localizations, which are reflected in triangulated coordinates, and then in 3D joint angle estimation. Pose2Sim, our robust markerless kinematics workflow, comes with a physically consistent OpenSim skeletal model, meant to mitigate these errors. Its accuracy was concurrently validated against a reference marker-based method. Lower-limb joint angles were estimated over three tasks (walking, running, and cycling) performed multiple times by one participant. When averaged over all joint angles, the coefficient of multiple correlation (CMC) remained above 0.9 in the sagittal plane, except for the hip in running, which suffered from a systematic 15° offset (CMC = 0.65), and for the ankle in cycling, which was partially occluded (CMC = 0.75). When averaged over all joint angles and all degrees of freedom, mean errors were 3.0°, 4.1°, and 4.0°, in walking, running, and cycling, respectively; and range of motion errors were 2.7°, 2.3°, and 4.3°, respectively. Given the magnitude of error traditionally reported in joint angles computed from a marker-based optoelectronic system, Pose2Sim is deemed accurate enough for the analysis of lower-body kinematics in walking, cycling, and running.
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Affiliation(s)
- David Pagnon
- Laboratoire Jean Kuntzmann, CNRS UMR 5224, Université Grenoble Alpes, 38400 Saint Martin d’Hères, France;
- Institut Pprime, CNRS UPR 3346, Université de Poitiers, 86360 Chasseneuil-du-Poitou, France;
- Correspondence:
| | - Mathieu Domalain
- Institut Pprime, CNRS UPR 3346, Université de Poitiers, 86360 Chasseneuil-du-Poitou, France;
| | - Lionel Reveret
- Laboratoire Jean Kuntzmann, CNRS UMR 5224, Université Grenoble Alpes, 38400 Saint Martin d’Hères, France;
- INRIA Grenoble Rhône-Alpes, 38330 Montbonnot-Saint-Martin, France
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Armitano-Lago C, Willoughby D, Kiefer AW. A SWOT Analysis of Portable and Low-Cost Markerless Motion Capture Systems to Assess Lower-Limb Musculoskeletal Kinematics in Sport. Front Sports Act Living 2022; 3:809898. [PMID: 35146425 PMCID: PMC8821890 DOI: 10.3389/fspor.2021.809898] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 12/24/2021] [Indexed: 01/06/2023] Open
Abstract
Markerless motion capture systems are promising for the assessment of movement in more real world research and clinical settings. While the technology has come a long way in the last 20 years, it is important for researchers and clinicians to understand the capacities and considerations for implementing these types of systems. The current review provides a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis related to the successful adoption of markerless motion capture technology for the assessment of lower-limb musculoskeletal kinematics in sport medicine and performance settings. 31 articles met the a priori inclusion criteria of this analysis. Findings from the analysis indicate that the improving accuracy of these systems via the refinement of machine learning algorithms, combined with their cost efficacy and the enhanced ecological validity outweighs the current weaknesses and threats. Further, the analysis makes clear that there is a need for multidisciplinary collaboration between sport scientists and computer vision scientists to develop accurate clinical and research applications that are specific to sport. While work remains to be done for broad application, markerless motion capture technology is currently on a positive trajectory and the data from this analysis provide an efficient roadmap toward widespread adoption.
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Affiliation(s)
- Cortney Armitano-Lago
- Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Dominic Willoughby
- Department of Exercise Science, Elon University, Elon, NC, United States
| | - Adam W. Kiefer
- Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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