Challenges in Combining EMG, Joint Moments, and GRF from Marker-Less Video-Based Motion Capture Systems
Shranjeno v:
| izdano v: | Bioengineering vol. 12, no. 5 (2025), p. 461 |
|---|---|
| Glavni avtor: | |
| Drugi avtorji: | , |
| Izdano: |
MDPI AG
|
| Teme: | |
| Online dostop: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Oznake: |
Brez oznak, prvi označite!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3211860051 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2306-5354 | ||
| 024 | 7 | |a 10.3390/bioengineering12050461 |2 doi | |
| 035 | |a 3211860051 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Rehan, Afzal H M |u Key Laboratory for Space Bioscience and Biotechnology, Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology and Equipment, School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China | |
| 245 | 1 | |a Challenges in Combining EMG, Joint Moments, and GRF from Marker-Less Video-Based Motion Capture Systems | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The evolution of motion capture technology from marker-based to marker-less systems is a promising field, emphasizing the critical role of combining electromyography (EMG), joint moments, and ground reaction forces (GRF) in advancing biomechanical analysis. This review examines the integration of EMG, joint moments, and GRF in marker-less video-based motion capture systems, focusing on current approaches, challenges, and future research directions. This paper recognizes the significant challenges of integrating the aforementioned modalities, which include problems of acquiring and synchronizing data and the issue of validating results. Particular challenges in accuracy, reliability, calibration, and environmental influences are also pointed out, together with the issue of the standard protocols of multimodal data fusion. Using a comparative analysis of significant case studies, the review examines existing methodologies’ successes and weaknesses and established best practices. New emerging themes of machine learning techniques, real-time analysis, and advancements in sensing technologies are also addressed to improve data fusion. By highlighting both the limitations and potential advancements, this review provides essential insights and recommendations for future research to optimize marker-less motion capture systems for comprehensive biomechanical assessments. | |
| 653 | |a Human mechanics | ||
| 653 | |a Electromyography | ||
| 653 | |a Machine learning | ||
| 653 | |a Motion capture | ||
| 653 | |a Accuracy | ||
| 653 | |a Biomechanics | ||
| 653 | |a Comparative analysis | ||
| 653 | |a Biomechanical engineering | ||
| 653 | |a Data acquisition | ||
| 653 | |a Computer vision | ||
| 653 | |a Ergonomics | ||
| 653 | |a Signal processing | ||
| 653 | |a Synchronism | ||
| 653 | |a Best practice | ||
| 653 | |a Data integration | ||
| 653 | |a Sport science | ||
| 653 | |a Algorithms | ||
| 653 | |a Real time | ||
| 700 | 1 | |a Louhichi Borhen |u Deanship of Scientific Research, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia | |
| 700 | 1 | |a Alrasheedi, Nashmi H |u Department of Mechanical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia | |
| 773 | 0 | |t Bioengineering |g vol. 12, no. 5 (2025), p. 461 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3211860051/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3211860051/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3211860051/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |