Privacy-Preserving Human Motion Analysis for Lower Back Pain Stratification through Federated Learning
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 ApplicationsDate Accepted
2024-10-09Event Name
14th EAI International Conference on Big Data Technologies and ApplicationsEvent Dates
1 November 2024Will your conference paper be published in proceedings?
- Yes