Motor Symptom Classification of Parkinson’s Disease using a Comprehensive Home-Based Sensor System
Project Members: Angela Botros, Prabitha Urwyler, Martin Camenzind, Daniel Bolliger, Tim Vanbellingen, Rolf Kistler, Stephan Bohlhalter, Tobias Nef
Project Start: 01.09.2017
Project Funding: Hasler Foundation
There is a strong clinical need for objective and continuous monitoring of motor performance in PD for improving therapeutic regiments and for assessment in clinical trials. In recent years, there has been a lot of progress in the area of symptom identification in PD patients. Most of the developed systems are based on a set of wearable sensors which classify the symptoms using a variety of machine learning algorithms and signal processing techniques. The majority of the approaches focus on one symptom only, e.g. Freezing of gait or Bradykinesia, in a clinical setting.
And while these approaches are able to identify the symptoms with high sensitivity and specificity, usually the underlying hardware is not designed for home- and long-term usage. The aim of this project is to develop a new system for the monitoring of PD motor symptoms, intended for long-term usage in a non-clinical environment, i.e. at the peoples homes. Key values are the simplicity, patient acceptance and robustness of the system.
Keywords: Parkinson's Disease, motor symptoms, wearable sensors, monitoring