Smoothness is characteristic of coordinated human movements, and stroke patients' movements seem to grow more smooth with recovery. We used a robotic therapy device to analyze five different measures of movement smoothness in the hemiparetic arm of 31 patients recovering from stroke. Four of the five metrics showed general increases in smoothness for the entire patient population. However, according to the fifth metric, the movements of patients with recent stroke grew less smooth over the course of therapy. This pattern was reproduced in a computer simulation of recovery based on submovement blending, suggesting that progressive blending of submovements underlies stroke recovery.

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 support vector machine 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.

This work indicates that noise-based devices could ameliorate diabetic and stroke impairments in balance control.