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Yuhai O, Choi A, Cho Y, Kim H, Mun JH. Deep-Learning-Based Recovery of Missing Optical Marker Trajectories in 3D Motion Capture Systems. Bioengineering (Basel) 2024; 11:560. [PMID: 38927796 PMCID: PMC11200691 DOI: 10.3390/bioengineering11060560] [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: 04/24/2024] [Revised: 05/17/2024] [Accepted: 05/30/2024] [Indexed: 06/28/2024] Open
Abstract
Motion capture (MoCap) technology, essential for biomechanics and motion analysis, faces challenges from data loss due to occlusions and technical issues. Traditional recovery methods, based on inter-marker relationships or independent marker treatment, have limitations. This study introduces a novel U-net-inspired bi-directional long short-term memory (U-Bi-LSTM) autoencoder-based technique for recovering missing MoCap data across multi-camera setups. Leveraging multi-camera and triangulated 3D data, this method employs a sophisticated U-shaped deep learning structure with an adaptive Huber regression layer, enhancing outlier robustness and minimizing reconstruction errors, proving particularly beneficial for long-term data loss scenarios. Our approach surpasses traditional piecewise cubic spline and state-of-the-art sparse low rank methods, demonstrating statistically significant improvements in reconstruction error across various gap lengths and numbers. This research not only advances the technical capabilities of MoCap systems but also enriches the analytical tools available for biomechanical research, offering new possibilities for enhancing athletic performance, optimizing rehabilitation protocols, and developing personalized treatment plans based on precise biomechanical data.
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Affiliation(s)
- Oleksandr Yuhai
- Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Republic of Korea; (O.Y.); (Y.C.)
| | - Ahnryul Choi
- Department of Biomedical Engineering, College of Medical Convergence, Catholic Kwandong University, Gangneung 25601, Republic of Korea;
| | - Yubin Cho
- Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Republic of Korea; (O.Y.); (Y.C.)
| | - Hyunggun Kim
- Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Republic of Korea; (O.Y.); (Y.C.)
| | - Joung Hwan Mun
- Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Republic of Korea; (O.Y.); (Y.C.)
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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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Das K, de Paula Oliveira T, Newell J. Comparison of markerless and marker-based motion capture systems using 95% functional limits of agreement in a linear mixed-effects modelling framework. Sci Rep 2023; 13:22880. [PMID: 38129434 PMCID: PMC10739832 DOI: 10.1038/s41598-023-49360-2] [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] [Received: 08/01/2023] [Accepted: 12/07/2023] [Indexed: 12/23/2023] Open
Abstract
Biomechanics analysis of human movement has been proven useful for maintenance of health, injury prevention, and rehabilitation in both sports and clinical populations. A marker-based motion capture system is considered the gold standard method of measurement for three dimensional kinematics measurements. However, the application of markers to anatomical bony points is a time consuming process and constrained by inter-, intra-tester and session reliability issues. The emergence of novel markerless motion capture systems without the use of reflective markers is a rapidly growing field in motion analysis. However an assessment of the level of agreement of a markerless system with an established gold standard marker-based system is needed to ensure the applicability of a markerless system. An extra layer of complexity is involved as the kinematics measurements are functional responses. In this paper a new approach is proposed to generate 95% functional limits of agreement (fLoA) using the linear mixed-effects modelling framework for hierarchical study designs. This approach is attractive as it will allow practitioners to extend their use of linear mixed models to assess agreement in method comparison studies in all domains where functional responses are recorded.
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Affiliation(s)
- Kishor Das
- School of Mathematical and Statistical Sciences, University of Galway, Galway, Ireland.
- CÚRAM, SFI Research Centre for Medical Devices, University of Galway, Galway, Ireland.
| | | | - John Newell
- School of Mathematical and Statistical Sciences, University of Galway, Galway, Ireland
- CÚRAM, SFI Research Centre for Medical Devices, University of Galway, Galway, Ireland
- The Insight Centre for Data Analytics, University of Galway, Galway, Ireland
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Rodríguez J, Marín J, Royo AC, Padrón L, Pérez-Soto M, Marín JJ. KeepRunning: A MoCap-Based Rapid Test to Prevent Musculoskeletal Running Injuries. SENSORS (BASEL, SWITZERLAND) 2023; 23:9336. [PMID: 38067707 PMCID: PMC10708810 DOI: 10.3390/s23239336] [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: 10/11/2023] [Revised: 11/10/2023] [Accepted: 11/18/2023] [Indexed: 12/18/2023]
Abstract
The worldwide popularisation of running as a sport and recreational practice has led to a high rate of musculoskeletal injuries, usually caused by a lack of knowledge about the most suitable running technique for each runner. This running technique is determined by a runner's anthropometric body characteristics, dexterity and skill. Therefore, this study aims to develop a motion capture-based running analysis test on a treadmill called KeepRunning to obtain running patterns rapidly, which will aid coaches and clinicians in assessing changes in running technique considering changes in the study variables. Therefore, a review and proposal of the most representative events and variables of analysis in running was conducted to develop the KeepRunning test. Likewise, the minimal detectable change (MDC) in these variables was obtained using test-retest reliability to demonstrate the reproducibility and viability of the test, as well as the use of MDC as a threshold for future assessments. The test-retest consisted of 32 healthy volunteer athletes with a running training routine of at least 15 km per week repeating the test twice. In each test, clusters of markers were placed on the runners' body segments using elastic bands and the volunteers' movements were captured while running on a treadmill. In this study, reproducibility was defined by the intraclass correlation coefficient (ICC) and MDC, obtaining a mean value of ICC = 0.94 ± 0.05 for all variables and MDC = 2.73 ± 1.16° for the angular kinematic variables. The results obtained in the test-retest reveal that the reproducibility of the test was similar or better than that found in the literature. KeepRunning is a running analysis test that provides data from the involved body segments rapidly and easily interpretable. This data allows clinicians and coaches to objectively provide indications for runners to improve their running technique and avoid possible injury. The proposed test can be used in the future with inertial motion capture and other wearable technologies.
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Affiliation(s)
- Javier Rodríguez
- IDERGO (Research and Development in Ergonomics), I3A (Instituto de Investigación en Ingeniería de Aragón), University of Zaragoza, C/María de Luna, 3, 50018 Zaragoza, Spain (A.C.R.); (L.P.); (M.P.-S.); (J.J.M.)
| | - Javier Marín
- IDERGO (Research and Development in Ergonomics), I3A (Instituto de Investigación en Ingeniería de Aragón), University of Zaragoza, C/María de Luna, 3, 50018 Zaragoza, Spain (A.C.R.); (L.P.); (M.P.-S.); (J.J.M.)
- Department of Design and Manufacturing Engineering, University of Zaragoza, C/María de Luna, 3, 50018 Zaragoza, Spain
| | - Ana C. Royo
- IDERGO (Research and Development in Ergonomics), I3A (Instituto de Investigación en Ingeniería de Aragón), University of Zaragoza, C/María de Luna, 3, 50018 Zaragoza, Spain (A.C.R.); (L.P.); (M.P.-S.); (J.J.M.)
- Department of Design and Manufacturing Engineering, University of Zaragoza, C/María de Luna, 3, 50018 Zaragoza, Spain
| | - Luis Padrón
- IDERGO (Research and Development in Ergonomics), I3A (Instituto de Investigación en Ingeniería de Aragón), University of Zaragoza, C/María de Luna, 3, 50018 Zaragoza, Spain (A.C.R.); (L.P.); (M.P.-S.); (J.J.M.)
- Department of Biomedical Engineering, University of Zaragoza, C/Mariano Esquillor s/n, 50018 Zaragoza, Spain
| | - Manuel Pérez-Soto
- IDERGO (Research and Development in Ergonomics), I3A (Instituto de Investigación en Ingeniería de Aragón), University of Zaragoza, C/María de Luna, 3, 50018 Zaragoza, Spain (A.C.R.); (L.P.); (M.P.-S.); (J.J.M.)
- Department of Design and Manufacturing Engineering, University of Zaragoza, C/María de Luna, 3, 50018 Zaragoza, Spain
| | - José J. Marín
- IDERGO (Research and Development in Ergonomics), I3A (Instituto de Investigación en Ingeniería de Aragón), University of Zaragoza, C/María de Luna, 3, 50018 Zaragoza, Spain (A.C.R.); (L.P.); (M.P.-S.); (J.J.M.)
- Department of Design and Manufacturing Engineering, University of Zaragoza, C/María de Luna, 3, 50018 Zaragoza, Spain
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Motion Capture and Dance Image Discrimination Based on Silent Positioning Algorithm. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1187548. [PMID: 35733581 PMCID: PMC9208926 DOI: 10.1155/2022/1187548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 05/24/2022] [Accepted: 05/31/2022] [Indexed: 11/22/2022]
Abstract
The silent location algorithm of the sensor network is a time synchronization location algorithm that can serve multiple users. Based on the detailed analysis of the development status of human motion measurement technology, this paper proposes a design of human motion capture device based on sensor network and realizes data conversion and filtering through experimental design. In this experiment, the sensor data processing is used to capture the human body positioning information in real time. The established node does not need to make a sound during the whole process and, at the same time, has strong concealment and easy expansion. In the silent positioning algorithm for sensor network nodes, combined with the sound ray tracking technology, the correction parameters are controlled according to the change of the objective function, and the effectiveness of the method is verified by simulation analysis. In terms of motion capture, the system can monitor, detect, and record human motion in real time. In order to obtain more high-resolution dance images with high-definition details and correspondingly process the dance images to improve the resolution, this paper uses the silent positioning algorithm technology to study the image resolution, and this is of great help to improve the resolution.
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Nie S, Li Y, Ma B, Zhang Y, Song J. The Construction of Basketball Training System Based on Motion Capture Technology. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:2481686. [PMID: 34567479 PMCID: PMC8460370 DOI: 10.1155/2021/2481686] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 08/25/2021] [Accepted: 08/27/2021] [Indexed: 11/17/2022]
Abstract
Motion capture is a cross-cutting application field developed in recent years, which comprises electronics, communications, control, computer graphics, ergonomics, navigation, and other disciplines. The accurate application of basketball technical movements in the basketball game is very important. Therefore, it is of great significance to capture and standardize athletes' movements and improve their training. Unfortunately, there are numerous issues in traditional classroom teaching that largely helps to train the athletes. To solve the issues of traditional basketball classroom teaching, a virtual simulation system for students' sports training is designed in this paper. Firstly, the information of basketball dribbling movement is captured and simulated in three dimensions. Secondly, we compare it with the standard database to judge the irregularities of athletes' movements, and carry out digital processing on athletes' movements and skill improvements statistics in combination with system functions. Thirdly, we set up a gradual training cycle. Finally, the Kinect-based capture technology is adopted to obtain the activity information of different joints of the human body. Through processing the motion data, relevant motion analysis data are fed to the established motion model, to realize the comparative analysis of motion pictures. In our experiments, we observed better training of the physical education.
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Affiliation(s)
- Shangqi Nie
- School of Physical Education, Huanghuai University, Zhumadian, China
- Department of Sports Science, Wonkwang University, Iksan-si, Republic of Korea
| | - Yuanqing Li
- School of Physical Education, Huanghuai University, Zhumadian, China
- Department of Sports Science, Wonkwang University, Iksan-si, Republic of Korea
| | - Biao Ma
- Department of Sports Science, Wonkwang University, Iksan-si, Republic of Korea
| | - Yufeng Zhang
- Department of Sports Science, Wonkwang University, Iksan-si, Republic of Korea
| | - Jeho Song
- Department of Sports Science, Wonkwang University, Iksan-si, Republic of Korea
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