PDWearML: Leveraging Daily Activities for Rapid Free-Living Parkinson\u2019s Disease Severity Assessment with Wearable ML

\\textit{Objective:} Achieving effective and robust free-living PD severity assessment with wearable intelligence technologies requires a deep understanding of clinically relevant features, representative activities, and machine learning algorithms. \\textit{Methods:} We designed a unified analytic framework (PDWearML) to optimise wearable ML approaches with simple daily activities for fast assessment of PD severity in free-living settings. It comprises annotation criteria, feature importance analysis, representative activity combination, and PD severity assessment. We conducted a 12-month study, developing a free-living PD wearable dataset containing 100 PD patients and 35 age-matched healthy controls using Huawei smartwatches and Shimmer. \\textit{Results:} The results reveal that through optimising multi-level feature extraction and combining three representative daily activities (WALK, ARISING-FROM-CHAIR, and DRINK), our smartwatch-based machine learning approach can assess PD severity in free-living settings within 2 minutes with an accuracy of up to 84.7\\%. \\textit{Significance:} This work holds significant clinical value, offering a potential auxiliary tool for faster, more tailored interventions in PD healthcare.