Leveraging Multi-Sensor Data and Domain Adaptation for Improved Parkinson's Disease Assessment

Parkinson's disease (PD) is a progressive neurode-generative disorder characterized by motor symptoms such as tremors, rigidity, and bradykinesia. Accurate and early diagnosis is crucial for effective management and treatment. Some quantitative studies have combined wearable technology with machine learning methods, demonstrating a high potential for practical application. However, these studies mostly use single-location, single-sensor data collected from PD patients in clinical settings, neglecting the diversity of PD symptoms and the real-world application scenarios in free-living environments. This paper proposes an auxiliary diagnosis framework for PD based on multi-location, multi-sensor fusion, and unsupervised domain adaptation. The multi-location, multi-sensor fusion can mitigate the asymmetry of Parkinson's symptoms, while unsupervised domain adaptation helps transfer in-hospital data to free-living environments without the need for manual labeling of the free-living data. Additionally, this paper designs a multi-head attention mechanism that focuses the disease classifier on sensors with strong feature discrimination and good distribution alignment. This experiment relies on wearable sensor data from 60 PD patients and 12 healthy controls, achieving an impressive accuracy of 90.46%, a precision of 88.28%, a recall of 88.09%, and an F1-score of 88.14%.