A Low-Cost Edge Computing Device for Real-Time Detection of Motor Symptoms in Neurodegenerative Diseases Using Machine Learning

Diseases like Parkinson's affect a non-negligible percentage of elder people worldwide. Affected people life quality strictly depends on the disease progression and counteractions that medical doctors decide to apply, according to periodical clinical evaluations. The possibility to have continuous monitoring of people with the help of electronic devices is a desirable opportunity for two main reasons: i) to reduce queues for medical doctors for in-person clinical assessment, ii) provide them with real-time and continuous monitoring data produced by assisted people in their home environment. To this end, the paper proposes the development and physical realization of an all-in-one low-cost device, able to both measure and process movement data in real-time, adopting miniaturized inertial sensors and machine learning capabilities. To accomplish the task, a pre-validated movement simulator is adopted to generate data concerning different kinds of movement disorders, specifically focusing on tremors related to Par