Movability assessment on physiotherapy for shoulder periarthritis via fine-grained 3D ResNet deep learning

Raymond Tan Rui Ming; Chengxuan Feng; Hock Soon Seah; Feng Lin

doi:10.1117/12.2588350

20 April 2021 Movability assessment on physiotherapy for shoulder periarthritis via fine-grained 3D ResNet deep learning

Raymond Tan Rui Ming, Chengxuan Feng, Hock Soon Seah, Feng Lin

Author Affiliations +

Proceedings Volume 11792, International Forum on Medical Imaging in Asia 2021; 117920H (2021) https://doi.org/10.1117/12.2588350
Event: International Forum on Medical Imaging in Asia 2021, 2021, Taipei, Taiwan

Abstract

We present a novel approach to movability assessment on physiotherapy for shoulder periarthritis via fine-grained 3D Residual Networks (R3D) deep learning. The unique deep neural networks is able to automatically extract the spatiotemporal features from the RGB-D videos. In our preliminary studies, we have a set of VR sports games customized for the immersive and interactive sports environment, to regulate the patient’s rehabilitation exercises. In this way, acquisition of RGB-D action videos can be more specific to the subject and defined movements; and fine-grained feature discrimination of the same subject can be better achieved from the longitudinal study, to increase the accuracy of therapeutic assessment.

Citation Download Citation

Raymond Tan Rui Ming, Chengxuan Feng, Hock Soon Seah, and Feng Lin "Movability assessment on physiotherapy for shoulder periarthritis via fine-grained 3D ResNet deep learning", Proc. SPIE 11792, International Forum on Medical Imaging in Asia 2021, 117920H (20 April 2021); https://doi.org/10.1117/12.2588350

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