Application of an LDA Classifier for Determining User-Intent in Multi-DOF Quasi-Static Shoulder Tasks in Individuals with Chronic Stroke: Preliminary Analysis.

Abstract

Abnormal synergies commonly present after stroke, limiting function and accomplishment of ADL's. They cause co-activation of sets of muscles spanning multiple joints across the affected upper-extremity. These synergies present proportionally to the amount of shoulder effort, thus the effects of the synergy reduce with reduced effort of shoulder muscles. A promising solution may be the application of a wearable exoskeletal robotic device to support the paretic shoulder in hopes to maximize function. To date, control strategies for such a device remain unknown. This work examines the feasibility of using two different linear discriminant analysis classifiers to control shoulder abduction and adduction as well as external and internal rotation simultaneously, two primary degrees of freedom that have gone largely unstudied in hemiparetic stroke. Forces, moments, and muscle activity were recorded during single and dual-tasks involving these degrees of freedom. A classifier that classified all tasks was able to determine user-intent in 14 of the 15 tasks above 90% accuracy. A classifier using force and moment data provided an average 94.3% accuracy, EMG 79%, and data sets combined, 94.9% accuracy. Parallel classifiers identifying user-intent in either abduction and adduction or internal and external rotation were 95.4%, 92.6%, and 97.3% accurate for the respective data sets. These preliminary results indicate that it seems possible to classify user-intent of the paretic shoulder in these degrees of freedom to an adequate accuracy using load cell data or load cell and EMG data combined that would enable control of a powered exoskeletal device.