Recognizing finger gestures from forearm EMG signals

摘要

A machine learning model is trained by instructing a user to perform various predefined gestures, sampling signals from EMG sensors arranged arbitrarily on the user's forearm with respect to locations of muscles in the forearm, extracting feature samples from the sampled signals, labeling the feature samples according to the corresponding gestures instructed to be performed, and training the machine learning model with the labeled feature samples. Subsequently, gestures may be recognized using the trained machine learning model by sampling signals from the EMG sensors, extracting from the signals unlabeled feature samples of a same type as those extracted during the training, passing the unlabeled feature samples to the machine learning model, and outputting from the machine learning model indicia of a gesture classified by the machine learning model.

主权项

1. A method for identifying individual finger movements, comprising:
arbitrarily arranging a plurality of electromyography (EMG) sensors on a user's forearm; performing one or more predefined finger gestures while using the EMG sensors to sample EMG signals generated by corresponding muscle contractions of the user; concurrent with the sampling of the EMG signals, monitoring the predefined finger gestures using a secondary input mechanism to verify that correct gestures were performed; extracting feature samples from the sampled EMG signals and labeling the feature samples according to corresponding finger gestures that have been verified as correct by the secondary input mechanism; and training a machine learning model with the labeled feature samples for use in identifying arbitrary finger gestures from an evaluation of corresponding EMG signal samples.