Abstract
ABSTRACTIn order to achieve high-level control for an active postural support brace, it is important for a wearable robot to be capable of recognizing human motion intentions. An inertial sensors-based torso motion mode recognition method is proposed in this study. The experiments are conducted to define range of torso motion, capture human motion signals by using four inertial sensors on seven healthy subjects, and utilize a classification method to achieve torso motion recognition based on human intent. Up to sixteen modes for torso motion recognition are investigated, and cascaded classification methods combining a quadratic discriminant analysis (QDA) classifier and a support vector machine (SVM) classifier are deployed. With selected cascaded classification strategies, cross-validation yielded classification accuracies of 95.18% (QDA) and 96.24% (SVM). The obtained results of the study show that inertial sensors based motion recognition is viable to achieve in high recognition accuracy which is promising for future robotic applications.
Published Version
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