Enhanced Detection and Segmentation of Sit Phases in Patients with Parkinson's Disease Using a Single SmartWatch and...

Automatic detection of Sit phases in people with Parkinson's disease (PD) using a single body-worn sensor is crucial for enhancing long-term, home-based monitoring of mobility.

The aim of this study was to enhance the accuracy of detecting and segmenting Sit phases in people with PD using a single SmartWatch worn at the ankle.

Twenty-two patients living with PD performed activities of daily living that incorporate repeated transitions to a seated position in a simulated free-living environment during 3 min, 4 min, and 5 min trials. Tri-axial accelerations and angular velocities of the right or left ankle were recorded at 50 Hz using a SmartWatch. Random forest algorithms were trained using raw and filtered data to automatically detect and segment Sit phases. Sensibility, specificity, and F-scores were calculated based on manual segmentation using the OptiTrack motion capture system.

Sensibility, specificity, and F-score achieved 78.3%, 93.8%, and 84.7% for Sit phase detection of the 3 min trial; 78.8%, 85.5%, and 80.6% for Sit phase detection of the 4 min trial; and 71.6%, 84.8%, and 75.6% for Sit phase detection of the 5 min trial. The median time difference between the manual and automatic segmentation was 0.95s, 0.89s, and 0.84s, respectively, for the 3 min, 4 min, and 5 min trial.

This study demonstrates that a random forest algorithm can accurately detect and segment Sit phases in people with PD using data from a single ankle-worn SmartWatch. The algorithm's performance was comparable to manual segmentation, while substantially reducing the time and effort required. These findings represent a meaningful step forward in enabling efficient, long-term, and home-based monitoring of mobility and symptom progression in people with PD.