NDLI: Transformation classification of human squat/sit-to-stand based on multichannel information fusion

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Transformation classification of human squat/sit-to-stand based on multichannel information fusion

Content Provider	SAGE Publishing
Author	Wang, Yu Song, Quanjun Ma, Tingting Chen, Yong Li, Hao Liu, Rongkai
Copyright Year	2022
Abstract	In existing rehabilitation training, research on the accuracy of recognizing completed actions has achieved good results; however, the reduction in the misjudgment rate in the action conversion process needs further research. This article proposes a multichannel information fusion method for the movement conversion process of squat/sit-to-stand, which can help online movement conversion classification during rehabilitation training. We collected a training dataset from a total of eight subjects performing three different motions, including half squat, full squat, and sitting, equipped with plantar pressure sensors, RGB cameras, and five inertial measurement units. Our evaluation includes the misjudgment rate for each action and the time needed for classification. The experimental results show that, compared with the recognition of a single sensor, the accuracy after fusion can reach 96.6% in the case of no occlusion and 86.7% in the case of occlusion. Compared with the complete time window, the classification time window is shortened by approximately 25%.
Related Links	https://journals.sagepub.com/doi/pdf/10.1177/17298806221103708?download=true
ISSN	17298806
Issue Number	4
Volume Number	19
Journal	International Journal of Advanced Robotic Systems (ARX)
e-ISSN	17298814
DOI	10.1177/17298806221103708
Language	English
Publisher	Sage Publications UK
Publisher Date	2022-07-10
Publisher Place	London
Access Restriction	Open
Rights Holder	© The Author(s) 2022
Subject Keyword	Human action recognition inertial sensor feature-level fusion pressure sensor RGB camera decision-level fusion
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Science Applications Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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