Low, Wan Shi and Chan, Chow Khuen and Chuah, Joon Huang and Tee, Yee Kai and Hum, Yan Chai and Salim, Maheza Irna Mohd and Lai, Khin Wee (2022) A review of machine learning network in human motion biomechanics. Journal of Grid Computing, 20 (1). ISSN 1570-7873, DOI https://doi.org/10.1007/s10723-021-09595-7.
Full text not available from this repository.Abstract
Human motion analysis is fundamental in many real applications such as surveillance and monitoring, human-machine interface, medical motion analysis and diagnosis. With the increasing amount of data in biomechanics research, it is becoming increasingly important to automatically analyse and understand object motions from large amount of footage and sensor data. The modalities for capturing the gait data are grouped according to the sensing technology: video sequences, wearable sensors, and floor sensors, as well as the publicly available datasets. In order to extract the essence of these data and make research more efficient, modern machine learning techniques are starting to complement traditional statistical tools. The purpose of this review is to familiarise the readers with key directions of implementation of machine learning techniques for gait analysis. The essential human gait parameters are briefly reviewed, followed by a detailed review of the-state-of-the art in machine learning for the human gait analysis. The machine learning framework used for human analysis, such as support vector machine (SVM), Hidden Markov Model (HMM), Bayesian Network Classifier (BN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM) and Generative Adversarial Networks (GANs), shall too be discussed here. Finally, the challenges and future direction of machine learning's application in motion analysis are outlined and discussed.
| Item Type: | Article |
|---|---|
| Funders: | Fundamental Research Grant Scheme, Ministry of Higher Education, Malaysia [Grant No: FRGS/1/2019/TK04/UM/01/2] |
| Uncontrolled Keywords: | Machine learning; Human; Motion; Biomechanics; Gait analysis |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 05 Aug 2022 06:58 |
| Last Modified: | 05 Aug 2022 06:58 |
| URI: | http://eprints.um.edu.my/id/eprint/33305 |
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