RARN: A Real-Time Skeleton-based Action Recognition Network for Auxiliary Rehabilitation Therapy
- Resource Type
- Conference
- Authors
- Shen, Mengqi; Lu, Hong
- Source
- 2022 IEEE International Symposium on Circuits and Systems (ISCAS) Circuits and Systems (ISCAS), 2022 IEEE International Symposium on. :2482-2486 May, 2022
- Subject
- Components, Circuits, Devices and Systems
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Training
Costs
Circuits and systems
Pose estimation
Medical treatment
Real-time systems
Skeleton
Action Recognition
Recurrent Neural Network
Multimedia Application
Rehabilitation Therapy
Datasets
- Language
- ISSN
- 2158-1525
Rehabilitation gymnastic training is an effective therapy for degeneration of spine disease in Traditional Chinese Medicine (TCM). In this paper, we propose a lightweight real-time Rehabilitation Action Recognition Network (RARN) using skeleton sequence obtained through 2d pose estimation and build an intelligent auxiliary rehabilitation therapy system. We design a set of training exercises consisting of 8 actions, and construct a dataset called Rehabilitation Action for Degenerative Spine Diseases (RDSD), containing 1012 skeleton sequences. We describe a demo application to conduct real-time action evaluation for rehabilitation therapy. The experimental results on RDSD shows that our system achieves high accuracy while still working under 6ms per frame averagely and the inference costs only about 0. 2ms per frame.