A Novel Energy-Based Composite Index for Assessing Motor State in Parkinson's Disease by Means of IMU-Based Digital Health Technology

Parkinson's disease (PD) is a neurodegenerative disorder in which dopaminergic medications, such as levodopa, are typically used to improve motor symptoms and the overall level of people's mobility. To enhance and personalise clinical management, it is important to assess the adherence and impact of pharmacological treatments on motor states (such as ON/OFF/DISKYNESIA, to cite a few). In this context, in addition to clinical assessments performed by PD specialists, it becomes crucial to leverage digital health technologies (e.g., wearable devices) that can collect motor symptoms objectively, continuously and remotely, so as to monitor participants even in an uncontrolled environment. This work aims to implement and validate an automatic motor state identification algorithm based on a novel energy-based composite index capturing mobility and motor symptom fluctuations occurring during the day. This work aims to identify and validate an energy-based composite index, whose evaluation and comparison with a suitab