Analysis of Joints for Tracking Fitness and Monitoring Progress in Physiotherapy

Rao, Nikitha; Surana, Piyush M.; Ragesh, Rahul; Srinivasa, Gowri

doi:10.1109/icsipa45851.2019.8977749

Cited by 1 publication

(2 citation statements)

References 6 publications

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“…The primary method of dimensionality reduction used in seven of the publications was Principal Component Analysis [66], [68], [105], [107], [108], [109], [115]. Other techniques used include autoencoders [78], [96], discrete wavelet transform [74], and transformation to numeric/word sequences [44], [71], [80].…”

Section: ) Feature Extraction Engineering and Selectionmentioning

confidence: 99%

“…The other big category of algorithms used under discriminative models was Recurrent Neural Networks (RNN), which were used in 11/31 publications. Long Short-Term Memory (LSTM) RNN, in particular, were used in nine publications, making it the most used ML algorithm in the publications [44], [51], [74], [81], [83], [85], [101], [106], [115]. A traditional RNN [102], and a Gated Recurrent Unity Network (GRU) [45] were used in one publication each.…”

Section: ) Model Trainingmentioning

confidence: 99%

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Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

et al. 2022

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The World Health Organization promotes healthy living through regular physical activities, such as exercise and sports, as well as access to healthcare and rehabilitation services for people with motor dysfunctions. However, there is a lack of specialized personnel and increased costs associated with such activities. These have led to the increased use of machine learning for the analysis and evaluation of human motion during exercise. To study the latest advancements in this area, a systematic literature review focusing on publications from 2017 to 2021 was performed. As a result, 88 relevant publications were identified, which developed both shallow machine learning and deep learning algorithms. The results indicated that algorithms for human motion assessment should provide personalized and informative assessments, with explainable and interpretable outcomes, that can be computed in real-time or concurrently with the execution of an exercise. Furthermore, they should be easy to adapt based on the needs of applications and should be able to perform with different motion capture systems. This has been challenging because of the usually small amount of collected data, the lack of large open datasets, and the unique characteristics of exercise motions. Based on the above findings, guidelines for the development of such algorithms are proposed and discussed. They relate to the selection of the type of assessment, handling data imbalances, selecting of motion capture technologies, balancing between accuracy and speed, selecting the right algorithm, performing concurrent assessment during an exercise, personalization and scalability, and evaluation.

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