Parkinson’s Disease Classification and Medication Adherence Monitoring Using Smartphone-based Gait Assessment and Deep Reinforcement Learning Algorithm

Abstract

For the diagnosis and classification of Parkinson’s patients, the unified scale of Parkinson’s patients (Unified Parkinson’s Disease Rating Scale – UPDRS) is used, which requires the patient to perform a series of tests among which the biomarkers of speech, the facial expression, the hand movement and walking analysis are considered, after which the doctor diagnoses the patient whether or not he has Parkinson’s disease according to the score obtained. The work proposes a system for monitoring patients with the use of cell phones and their automatic classification according to the data collected by them. The system starts from the budget that Parkinson’s patients have different abnormalities when walking if they do not follow the required medication. The cell phone collects the data passively while the patient has his cell phone in his pocket. After that, the data preprocessor helps to extract the walking cycles that this Parkinson-related biomarker contains. The algorithm proposed for classification and Medication Adherence Monitoring is the Deep Reinforcement Learning. With this work we demonstrate the feasibility of using cell phones to monitor the biomarker walking in Parkinson’s patients and the possibility of Passive Medication Adherence Monitoring and Dynamic Treatment Regimes.