Classification of hand preshaping in persons with stroke using Linear Discriminant Analysis

Abstract

Objective This study describes the analysis of hand preshaping using Linear Discriminant Analysis (LDA) to predict hand formation during reaching and grasping tasks of the hemiparetic hand, following a series of upper extremity motor intervention treatments. The purpose of this study is to use classification of hand posture as an additional tool for evaluating the effectiveness of therapies for upper extremity rehabilitation such as virtual reality (VR) therapy and conventional physical therapy. Classification error for discriminating between two objects during hand preshaping is obtained for the hemiparetic and unimpaired hands pre and post training. Methods Eight subjects post stroke participated in a two-week training session consisting of upper extremity motor training. Four subjects trained with interactive VR computer games and four subjects trained with clinical physical therapy procedures of similar intensity. Subjects’ finger joint angles were measured during a kinematic reach to grasp test using CyberGlove® and arm joint angles were measured using the trackSTAR™ system prior to training and after training. Results The unimpaired hand of subjects preshape into the target object with greater accuracy than the hemiparetic hand as indicated by lower classification errors. Hemiparetic hand improved in preshaping accuracy and time to reach minimum error. Conclusion Classification of hand preshaping may provide insight into improvements in motor performance elicited by robotically facilitated virtually simulated training sessions or conventional physical therapy.

Neural co-processors for restoring brain function: results from a cortical model of grasping

“…This is likely due to object shape information not being fully conveyed to the primary motor cortex (M1) module. Such a form of hand muscle spasticity is also a common symptom in certain strokes where the subject is able to position their hand, but is unable to form the appropriate grasp [23,37].…”

Differentiation of hand posture to object shape in children with unilateral spastic cerebral palsy

“…Discriminant analysis reduced the multivariate data contributing to hand posture using a custom MATLAB (Mathworks, Natick, MA) program. At each 5% interval of reach, the hand posture was classified into a shape category (straight, concave, or convex) by calculating the statistical (Mahalanobis) distance between the cumulative data per shape (Puthenveettil, Fluet, Qui, & Adamovich, 2012). To determine when during reach the relative ability to discriminate hand posture to object shape emerges, the shape category at each interval was entered into a matrix that summarized the extent to which hand posture during each trial correctly predicted object shape (see Santello & Soechting, 1998 for details).…”

Grasps Recognition and Evaluation of Stroke Patients for Supporting Rehabilitation Therapy

“…Tavakolan et al [ 14 ] used SVM for pattern recognition of surface electromyography signals of four forearm muscles in order to classify eight hand gestures. On the other hand, Puthenveettil et al [ 15 ] used linear discriminant analysis to classify hand preshapes in poststroke patients using data from the CyberGlove. In this study, the SVM approach was selected to perform the gesture recognition.…”