(CCFTPCI '19) Monitoring motor symptoms in Parkinson's disease via instrumenting daily artifacts with inertia sensors

Abstract

Daily monitoring of Parkinson’s disease is important since clinical assessments can only provide a brief and limited view of a patient’s condition. However, traditional approaches rely heavily on patients’ self-reports or diaries, which are subjective and often lack of necessary details. In this work, we instrument a handle that can be attached to cutlery with inertial sensors to collect motion data unobtrusively. By analyzing the data of patients and normal people collected in the clinic, we demonstrated that our machine learning based model can not only distinguish between patients and normal people, but also identify the disease levels in a fine-grained manner. To further understand how the self-tracking data is used in clinic, we conducted a semi-structured interview with several clinicians. Through the interpretation from the perspective of both physicians and patients, we found that our handle can help the physicians better understand disease progression and promote patients’ engagement in tackling the disease.