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A Supervised Pattern Recognition Approach for Human Movement Onset Detection

Applications of robotics and mechatronics to neurorehabilitation are getting more and more consensus in the clinical community thanks to early encouraging results. They enable an objective assessment of patient's motor recovery and the administration of rehabilitation treatments specific for each patient. In particular, isometric force/torque measurements in post-stroke patients were recently used in clinical trials for the functional assessment, with encouraging results. A challenging issue in the processing of such measurements is to detect the initiation of the voluntary contraction of the patient (i.e., onset time). The onset detection is crucial to obtain clinically relevant data. In previous works, different deterministic methods for onset detection were presented. Each of those methods is signal-structure dependant, causing drop of performance when applied to different kind of signals. In this paper, we introduce an innovative technique for the automatic selection of the best onset detection method. To this aim, we adopt a supervised pattern recognition approach that dynamically selects, from a pool of deterministic methods, the one that is best suited for each signal according to the signal structure. The method has been tested on annotated force and torque datasets, showing that such a method improves not only the performance achieved by the single deterministic techniques, but also those attained by a group of clinical experts.