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

Abstract

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. The analysis of the video recordings was performed by clinicians trained in the use of scales for the assessment of the severity of Parkinsonian symptoms and motor complications. Results derived from the accelerometer time series were analyzed to assess the effect on the estimation of clinical scores of the duration of the window utilized to derive segments (to eventually compute data features) from the accelerometer data, the use of different SVM kernels and misclassification cost values, and the use of data features derived from different motor tasks. Results were also analyzed to assess which combinations of data features carried enough information to reliably assess the severity of symptoms and motor complications. Combinations of data features were compared taking into consideration the computational cost associated with estimating each data feature on the nodes of a body sensor network and the effect of using such data features on the reliability of SVM-based estimates of the severity of Parkinsonian symptoms and motor complications.

Figures

FIGURE 1

Motor tasks performed by subjects during each trial. The tasks included: A-B) finger to nose (right and left), C-D) finger tapping, E-F) opening/closing the hands, G-H) heel tapping, I) sitting, and J) alternating hand movements.

FIGURE 2

Schematic representation of the position of sensors on the body to gather accelerometer data. The SHIMMER platform we envision to use in future studies is also shown.

FIGURE 3

Effect of the window length on the error affecting the estimates of clinical scores derived via analysis of the accelerometer data. Estimation error values were averaged across all the motor tasks to derive the box plots for tremor (A), bradykinesia (B), and dyskinesia (C). Results were derived on a subject-by-subject basis and are shown as aggregate data.

FIGURE 4

Effect of different SVM kernels on the error affecting the estimates of clinical scores derived via analysis of the accelerometer data. Estimation error values were averaged across all the motor tasks to derive the box plots for tremor (A), bradykinesia (B), and dyskinesia (C). Results were derived on a subject-by-subject basis and are shown as aggregate data.

FIGURE 5

Effect of selecting different motor tasks on the error affecting the estimates of clinical scores derived via analysis of the accelerometer data. Results are shown for tremor (A), bradykinesia (B), and dyskinesia (C). Box plots of estimation errors are shown for the following motor tasks: Task#1) finger to nose – right; Task#2) finger to nose – left; Task#3) finger tapping – right; Task#4) finger tapping – left; Task#5) open/close hand – right; Task#6) open/close hand – left; Task#7) heel tapping – right; Task#8) heel tapping – left; Task#9) sitting; and Task#10) alternating hand movements. Results were derived on a subject-by-subject basis and are shown as aggregate data.