A Novel Algorithm For Real-time Detection Of Freezing Of Gait Using Wrist-based Accelerometer Sensor Data

The rapid advancements in medical technology throughout the past two decades have greatly enhanced life expectancy, ushering in an era where ageing populations are thriving. However, this demographic shift has also brought attention to age-related issues, particularly neurological disorders. This paper addresses the challenges posed by one such neurological disorder Parkinson's disease (PD), with a focus on the debilitating symptom of Freezing of Gait (FoG). We propose a novel algorithm utilizing wrist-worn accelerometer sensors for real-time FoG detection. The algorithm incorporates Variable Mode Decomposition (VMD) to enhance noise resilience in motion data. At its core, our emphasis has been on developing a subject-independent model and harnessing the capabilities of a wrist-based sensor, enhancing the feasibility and usability of our approach. Evaluation on a public dataset showcases the model's significant potential, with an achieved average Sensitivity of 0.80 and Specificity of 0.81, underlining its effectiveness.