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Sessionand person-independent recognition of hand and finger gestures is of utmost importance for the practicality of gesture based interfaces. In this paper we evaluate the performance of a wearable gesture recognition system that captures arm, hand, and finger motions by measuring movements of, and muscle activity at the forearm. We fuse the signals of an Inertial Measurement Unit (IMU) worn at the wrist, and the Electromyogram (EMG) of muscles in the forearm to infer hand and finger movements. A set of 12 gestures was defined, motivated by their similarity to actual physical manipulations and to gestures known from the interaction with mobile devices. We recorded performances of our gesture set by five subjects in multiple sessions. The resulting datacorpus will be made publicly available to build a common ground for future evaluations and benchmarks. Hidden Markov Models (HMMs) are used as classifiers to discriminate between the defined gesture classes. We achieve a recognition rate of 97.8% in session-independent, and of 74.3% in person-independent recognition. Additionally, we give a detailed analysis of error characteristics and of the influence of each modality to the results to underline the benefits of using both modalities together.

Recognizing Hand and Finger Gestures with IMU based Motion and EMG based Muscle Activity Sensing