Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Improvement of Gesture Recognition using 2-stage HMM

2단계 히든마코프 모델을 이용한 제스쳐의 성능향상 연구

  • Jung, Hwon-Jae (Electronics and Radio Engineering, Kyung-hee University) ;
  • Park, Hyeonjun (Electronics and Radio Engineering, Kyung-hee University) ;
  • Kim, Donghan (Electronics and Radio Engineering, Kyung-hee University)
  • 정훤재 (경희대학교 전자전파공학) ;
  • 박현준 (경희대학교 전자전파공학) ;
  • 김동한 (경희대학교 전자전파공학)
  • Received : 2015.06.03
  • Accepted : 2015.10.14
  • Published : 2015.11.01
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Abstract

In recent years in the field of robotics, various methods have been developed to create an intimate relationship between people and robots. These methods include speech, vision, and biometrics recognition as well as gesture-based interaction. These recognition technologies are used in various wearable devices, smartphones and other electric devices for convenience. Among these technologies, gesture recognition is the most commonly used and appropriate technology for wearable devices. Gesture recognition can be classified as contact or noncontact gesture recognition. This paper proposes contact gesture recognition with IMU and EMG sensors by using the hidden Markov model (HMM) twice. Several simple behaviors make main gestures through the one-stage HMM. It is equal to the Hidden Markov model process, which is well known for pattern recognition. Additionally, the sequence of the main gestures, which comes from the one-stage HMM, creates some higher-order gestures through the two-stage HMM. In this way, more natural and intelligent gestures can be implemented through simple gestures. This advanced process can play a larger role in gesture recognition-based UX for many wearable and smart devices.

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References

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