Multi-HMM Classification for Hand Gesture Recognition Using Two Differing Modality Sensors Conference Paper

abstract

• 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.

name of conference

• 2014 IEEE Dallas Circuits and Systems Conference (DCAS)

published proceedings

• 2014 IEEE Dallas Circuits and Systems Conference (DCAS)

author list (cited authors)

• Liu, K., Chen, C., Jafari, R., & Kehtarnavaz, N.

citation count

• 24

complete list of authors

• Liu, Kui||Chen, Chen||Jafari, Roozbeh||Kehtarnavaz, Nasser

publication date

• October 2014

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

Digital Object Identifier (DOI)

• 10.1109/dcas.2014.6965338

International Standard Book Number (ISBN) 13

• 9781479959235

start page

• 1

end page

• 4

URL

• http://dx.doi.org/10.1109/dcas.2014.6965338