Preliminary Studies of SEMG-Based Finger Gesture Classification for Smart Watch Application Using Deep Learning

To control smart watches available in the market, both hands should be used. A surface electromyography (SEMG)-based interface will enable controlling the watch with the hand where it is worn. However, fewer studies have been conducted on SEMG-interface for healthy subjects for smart watch applications. This study developed an algorithm to classify 3–5 finger gestures from SEMG signals recorded on the upper wrist. Also, we compared the classification accuracies between intra-subject models and an inter-subject model. We concluded that when there is a low number of gestures, factory calibration for matching SEMGs to gestures is sufficient. However, when there are a high number of gestures, individual calibration is required just after purchasing the watch.

[1]  Changmok Choi,et al.  An SEMG computer interface using three myoelectric sites for proportional two-dimensional cursor motion control and clicking for individuals with spinal cord injuries. , 2013, Medical engineering & physics.

[2]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[3]  C. Nicol,et al.  Classification of Phantom Finger, Hand, Wrist, and Elbow Voluntary Gestures in Transhumeral Amputees With sEMG , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[4]  Annette Hagengruber,et al.  An sEMG-based Interface to give People with Severe Muscular Atrophy control over Assistive Devices , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).