Performance Analysis of an HMM-Based Gesture Recognition Using a Wristwatch Device

Interaction with mobile devices that are intended for everyday use is challenging since such systems are continuously optimized towards small outlines. Watches are a particularly critical as display size, processing capabilities, and weight are tightly constraint. This work presents a watch device with an integrated gesture recognition interface. We report the resource-optimized implementation of our algorithmic solution on the watch and demonstrate that the recognition approach is feasible for such constraint devoices. The system is wearable during everyday activities and was evaluated with eight users to complete questionnaires through intuitive one-hand movements. We developed a procedure to spot and classify input gestures from continuous acceleration data acquired by the watch. The recognition procedure is based on hidden Markov models (HMM) and was fully implemented on a watch. The algorithm achieved an average recall of 79% at 93% precision in recognizing the relevant gestures. The watch implementation of continuous gesture spotting showed a delay below 3 ms for feature computation, Viterbi path processing, and final classification at less than 4 KB memory usage.