SASE-TCN: A Multitask Learning Network for MDS-UPDRS III Task Classification and Parkinson’s Disease Recognition
Yiyuan Zhang, Long Meng, Chen Chen, Wei Chen · IEEE Sensors Journal · 2025
As wearable technology and machine learning (ML) algorithms have developed by leaps and bounds, many studies have focused on automatically monitoring Parkinson’s disease (PD). The procedure includes two steps: first, classifying activities, usually the Movement Disorder Society-sponsored revision of the unified PD rating scale (MDS-UPDRS) part III tasks; second, evaluating the performance of these activities and recognizing PD. To improve the efficiency and precision of the procedure, this study proposes a self-attention squeeze-and-excitation temporal convolutional network (SASE-TCN). Based on the multistage temporal convolutional network (MS-TCN) and attention mechanisms, SASE-TCN can effectively extract the distant sequential and channelwise features. In addition, based on acceleration signals, SASE-TCN can classify MDS-UPDRS III tasks and recognize patients with PD simultaneously with the multitask learning mechanism. The model was tested with two public datasets, PD-BioStampRC21 and PD-motion. The SASE-TCN model obtained an average${F}1$score of 0.7802–0.9160 for activity classification and an average accuracy of 0.7366–0.8578 for PD recognition. The results demonstrated the feasibility of the SASE-TCN in classifying PD-related activities and recognizing PD with accelerometers. This study will support the diagnosis and treatment of PD.