Work

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

Year: 2025

Type: article

Abstract: 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 algorithms. 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.

Source: EAI Endorsed Transactions on Pervasive Health and Technology

DOI: 10.4108/eetpht.11.9109

Authors: Liyun Gong, Miao Yu, Saeid Pourroostaei

Institution: University of Lincoln

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

Funders: European Commission, Horizon 2020