Accurate Body Pose Matching for Individuals with Stroke using Siamese Networks

Stroke is one of the major causes of long-term disability in United States. With more than 800,00 people experiencing stroke every year, it is important that efficient means for recovery are presented to support stroke subjects. Exoskeleton and serious game based rehabilitation are some of the state...

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Bibliographic Details
Published in:IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (Online) pp. 177 - 181
Main Authors: Gokhman, Ruslan, Sawdayi, Talya, Khan, Rana, Satyanarayana, Ashwin, Vinjamuri, Ramana, Kadiyala, Sai Praveen
Format: Conference Proceeding
Language:English
Published: IEEE 19.06.2024
Subjects:
Accuracy
Exoskeletons
Games
Machine learning
Machine learning algorithms
Pose Matching
Siamese Networks
Stroke (medical condition)
Stroke Rehabilitation
ISSN:2832-2975
Online Access:Get full text
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Summary:Stroke is one of the major causes of long-term disability in United States. With more than 800,00 people experiencing stroke every year, it is important that efficient means for recovery are presented to support stroke subjects. Exoskeleton and serious game based rehabilitation are some of the state-of-art approaches used in the recovery of stroke subjects. Accurate matching of body poses performed by individuals with stroke is essential in understanding the current state of recovery of the subject and plan further rehabilitation. Established machine learning based approaches fall short in accurately matching the poses of stroke subjects with ground truths. In this work, we present algorithms supported by Siamese architectures to effectively identify the poses performed by the subjects. Our proposed framework involves data pre-processing, extraction, building classification models and validating them using a body pose data set of individuals with stroke. On a considered public database, our proposed pose identification models namely, Siamese based LSTM and Siamese based CNN gave 7.8% and 14.2 % better identification accuracy than the traditional LSTM approach.
ISSN:2832-2975
DOI:10.1109/CHASE60773.2024.00030