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LightPRA: A Lightweight Temporal Convolutional Network for Automatic Physical Rehabilitation Exercise Assessment
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Sara Sharifzadeh ,
Alireza Daneshkhah,
Seng W. Loke ,
Vasile Palade,
Michael J. Duncan ,
Bahareh Nakisa
Computers in Biology and Medicine, Volume: 173, Start page: 108382
Swansea University Author:
Sara Sharifzadeh
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© 2024 The Authors. This is an open access article under the CC BY license.
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DOI (Published version): 10.1016/j.compbiomed.2024.108382
Abstract
Research evidence shows that physical rehabilitation exercises prescribed by medical experts can assist in restoring physical function, improving life quality, and promoting independence for physically disabled individuals. In response to the absence of immediate expert feedback on performed actions...
| Published in: | Computers in Biology and Medicine |
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| ISSN: | 0010-4825 |
| Published: |
Elsevier BV
2024
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa66848 |
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| Abstract: |
Research evidence shows that physical rehabilitation exercises prescribed by medical experts can assist in restoring physical function, improving life quality, and promoting independence for physically disabled individuals. In response to the absence of immediate expert feedback on performed actions, developing a Human Action Evaluation (HAE) system emerges as a valuable automated solution, addressing the need for accurate assessment of exercises and guidance during physical rehabilitation. Previous HAE systems developed for the rehabilitation exercises have focused on developing models that utilize skeleton data as input to compute a quality score for each action performed by the patient. However, existing studies have focused on improving scoring performance while often overlooking computational efficiency. In this research, we propose LightPRA (Light Physical Rehabilitation Assessment) system, an innovative architectural solution based on a Temporal Convolutional Network (TCN), which harnesses the capabilities of dilated causal Convolutional Neural Networks (CNNs). This approach efficiently captures complex temporal features and characteristics of the skeleton data with lower computational complexity, making it suitable for real-time feedback provided on resource-constrained devices such as Internet of Things (IoT) devices and Edge computing frameworks. Through empirical analysis performed on the University of Idaho-Physical Rehabilitation Movement Data (UI-PRMD) and KInematic assessment of MOvement for remote monitoring of physical REhabilitation (KIMORE) datasets, our proposed LightPRA model demonstrates superior performance over several state-of-the-art approaches such as Spatial–Temporal Graph Convolutional Network (STGCN) and Long Short-Term Memory (LSTM)-based models in scoring human activity performance, while exhibiting lower computational cost and complexity. |
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| Keywords: |
Activity evaluation; Dilated convolutions; Temporal Convolutional Network; Telerehabilitation; Skeleton data |
| College: |
Faculty of Science and Engineering |
| Funders: |
The authors would like to thank Coventry University and Deakin University for jointly funding this Ph.D. project titled “Activity Recognition Using Digital Frame Streams for Monitoring Rehab Period”. |
| Start Page: |
108382 |