Hand action recognition in rehabilitation exercise method using R(2+1)D deep learning network and interactive object information

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Authors

  • Nguyen Sinh Huy (Corresponding Author) Military Information Technology Institute, Academy of Military Science and Technology
  • Le Thi Thu Hong Military Information Technology Institute, Academy of Military Science and Technology
  • Nguyen Hoang Bach Military Information Technology Institute, Academy of Military Science and Technology
  • Nguyen Chi Thanh Military Information Technology Institute, Academy of Military Science and Technology
  • Doan Quang Tu Military Information Technology Institute, Academy of Military Science and Technology
  • Truong Van Minh School of Electrical and Electronics Engineering, Hanoi University of Science and Technology
  • Vu Hai School of Electrical and Electronics Engineering, Hanoi University of Science and Technology

DOI:

https://doi.org/10.54939/1859-1043.j.mst.CSCE6.2022.77-91

Keywords:

Hand action recognition ; Rehabilitation exercises; Object detection and tracking; R(2 1)D

Abstract

Hand action recognition in rehabilitation exercises is to automatically recognize what exercises the patient has done. This is an important step in an AI system to assist doctors to handle, monitor and assess the patient’s rehabilitation. The expected system uses videos obtained from the patient's body-worn camera to recognize hand action automatically. In this paper, we propose a model to recognize the patient's hand action in rehabilitation exercises, which is a combination of the results of a deep learning network recognizing actions on Video RGB, R(2+1)D, and a main interactive object in the exercises detection algorithm. The proposed model is implemented, trained, and tested on a dataset of rehabilitation exercises collected from wearable cameras of patients. The experimental results show that the accuracy in exercise recognition is practicable, averaging 88.43% on the test data independent of the training data. The action recognition results of the proposed method outperform the results of a single R(2+1)D network. Furthermore, the better results show the reduced rate of confusion between exercises with similar hand gestures. They also prove that the combination of interactive object information and the action recognition improve the accuracy significantly.

References

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Published

30-12-2022

How to Cite

Nguyen Sinh Huy, Le Thi Thu Hong, Nguyen Hoang Bach, Nguyen Chi Thanh, Doan Quang Tu, Truong Van Minh, and Vu Hai. “Hand Action Recognition in Rehabilitation Exercise Method Using R(2+1)D Deep Learning Network and Interactive Object Information”. Journal of Military Science and Technology, no. CSCE6, Dec. 2022, pp. 77-91, doi:10.54939/1859-1043.j.mst.CSCE6.2022.77-91.

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