Multi-HMM Classification for Hand Gesture Recognition Using Two Differing Modality Sensors
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© 2014 IEEE. This paper presents a multi-Hidden Markov Model (HMM) classification approach for hand gesture recognition by utilizing two differing modality and low-cost sensors. The sensors consist of a Kinect depth camera and a wearable inertial sensor. It is shown that the multi-HMM classification based on nine signals that are simultaneously captured by these two sensors leads to a more robust recognition compared to the situation when only a single HMM classification is used to generate the likelihood probabilities of hand gestures. This approach is applied to the hand gestures of the $1Unistroke Recognizer application and the results obtained indicate a 7% improvement in the overall classification rate over a single HMM classification under realistic conditions.
author list (cited authors)
Liu, K., Chen, C., Jafari, R., & Kehtarnavaz, N.