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Privacy-Preserving Human Motion Analysis for Lower Back Pain Stratification through Federated Learning

conference contribution
posted on 2025-02-04, 17:54 authored by Afreen NazAfreen Naz, Pandey Shourya Prasad, Sheldon McCallSheldon McCall, Chan Chi Leung, Liyun GongLiyun Gong, Miao YuMiao Yu

Human Gait Analysis is crucial in healthcare applications, with numerous research works focusing on machine learning and deep learning approaches for tasks such as abnormal gait detection and gait quality assessment. However, developing such models requires collecting and sharing a significant amount of patient data, raising privacy concerns. In this study, we introduce the world’s first technique for constructing a deep neural network model to stratify patients’ pain levels based on video recordings of timed up-and-go activities, while ensuring privacy preservation through modern federated learning algo rithms. Our experimental results demonstrate the effectiveness of this technique in accurately stratifying LBP levels without the need for data sharing among local clients to maintain privacy.

History

School affiliated with

  • School of Computer Science (Research Outputs)

Publication Title

14th EAI International Conference on Big Data Technologies and Applications

Date Accepted

2024-10-09

Event Name

14th EAI International Conference on Big Data Technologies and Applications

Event Dates

1 November 2024

Will your conference paper be published in proceedings?

  • Yes

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