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.

Figures

Figure 1

Reach to grasp test schematic: Trial begins with hand at rest, placed in initial preset position. At cue, (1) subject reaches for the shape (centered), (2) places it on a 7.5 cm high target platform, (3) return to initial position. Trials are run for both hemiparetic and unimpaired hand.

Figure 2

Index PIJ joint angle sensor from CyberGlove® plotted with velocity profile from wrist sensor. Seven phases of movement are involved in the reach to grasp test

Figure 3

Kinematic trajectories for subject’s impaired and unimpaired hand (joint PIJ of index finger). Impaired hand shows more variability in movement and instability.

Figure 4

Subject 4’s Kinematic data obtained prior to training. Data is segmented at onset 1 and offset 1 to correspond to time when movement begins to time when hand touches object (preshaping phase).

Figure 5

Subject 4’s Kinematic data obtained after training. Data is segmented at onset 1 and offset 1 to correspond to time when movement begins to time when hand touches object (preshaping phase).

Figure 6

Subject 4’s Classification Error for Figures 4 and 5 to discriminate hand postures for shapes ‘huge circle’ and ‘small cube’. A decrease in classification error for post treatment (Figure 5) illustrates the the subject’s ability to effectively shape the hand during reaching.

Figure 7

Subject 6's classification errors decrease as movement progresses. Classification error of unimpaired hand is less than impaired hand. Hand posture is distinguishable earliest in time for the unimpaired hand. After training, impaired hand more closely resembles unimpaired hand and reaches a minimum error earlier in time.