Monitoring motor fluctuations in patients with Parkinson's disease using wearable sensors.

This paper presents the results of a pilot study to assess the feasibility of using accelerometer data to estimate the severity of symptoms and motor complications in patients with Parkinson's disease. A support vector machine (SVM) classifier was implemented to estimate the severity of tremor, bradykinesia and dyskinesia from accelerometer data features. SVM-based estimates were compared with clinical scores derived via visual inspection of video recordings taken while patients performed a series of standardized motor tasks.

Using wearable sensors to predict the severity of symptoms and motor complications in late stage Parkinson's Disease.

This paper is focused on the analysis of data obtained from wearable sensors in patients with Parkinson's Disease. We implemented Support Vector Machines (SVM's) to predict clinical scores of the severity of Parkinsonian symptoms and motor complications. We determined the optimal window length to extract features from the sensor data.

Analysis of feature space for monitoring persons with Parkinson's disease with application to a wireless wearable sensor system.

We present work to develop a wireless wearable sensor system for monitoring patients with Parkinson's disease (PD) in their homes. For monitoring outside the laboratory, a wearable system must not only record data, but also efficiently process data on-board. This manuscript details the analysis of data collected using tethered wearable sensors.