Enhanced detection of early Parkinson's disease through multi-sensor fusion on IoMT

To date, Parkinson's disease (PD) is an incurable neurological disorder, and the time of quality life can only be extended through early detection and timely intervention. However, the symptoms of early PD are both heterogeneous and subtle. To cope with these challenges, we develop a two-level fusion framework for smart healthcare, leveraging smartphones interconnected with the Internet of Medical Things and exploring the contribution of multi-sensor and multi-activity data. Rotation rate and acceleration during walking activity are recorded with the gyroscope and accelerometer, while location coordinates and acceleration during tapping activity are collected via the touch screen and accelerometer, and voice signals are captured by the microphone. The main scientific contribution is the enhanced fusion of multi-sensor information to cope with the heterogeneous and subtle nature of early PD symptoms, achieved by a first-level component that fuses features within a single activity using an attention mechanism and a second-level component that dynamically allocates weights across activities. Compared with related works, the proposed framework explores the potential of fusing multi-sensor data within a single activity, and mines the importance of different activities that correspond to early PD symptoms. The proposed two-level fusion framework achieves an AUC of 0.891 (95 % CI, 0.860-0.921) and a sensitivity of 0.950 (95 % CI, 0.888-1.000) in early PD detection, demonstrating that it efficiently fuses information from different sensor data for various activities and has a strong fault tolerance for data.