casa 18(14): e1

Research Article

Hidden Markov Model for recognition of skeletal databased hand movement gestures

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  • @ARTICLE{10.4108/eai.18-6-2018.154819,
        author={Bui Cong Giao and Trinh Hoai An and Nguyen  Thi Hong Anh and Ho  Nhut Minh},
        title={Hidden Markov Model for recognition of skeletal databased hand movement gestures},
        journal={EAI Endorsed Transactions on Context-aware Systems and Applications},
        keywords={Skeletal data, Hand movement recognition, PCA algorithm, HMM.},
  • Bui Cong Giao
    Trinh Hoai An
    Nguyen Thi Hong Anh
    Ho Nhut Minh
    Year: 2018
    Hidden Markov Model for recognition of skeletal databased hand movement gestures
    DOI: 10.4108/eai.18-6-2018.154819
Bui Cong Giao1,*, Trinh Hoai An1, Nguyen Thi Hong Anh1, Ho Nhut Minh2
  • 1: Faculty of Electronics and Telecommunications, Saigon University, Vietnam
  • 2: Faculty of Electronics Engineering II, Posts and Telecommunications Institute of Technology, Ho Chi Minh Campus, Vietnam
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The development of computing technology provides more and more methods for human-computer interaction applications. The gesture or motion of a human hand is considered as one of the most basic communications for interacting between people and computers. Recently, the release of 3D cameras such as Microsoft Kinect and Leap Motion has provided many advantage tools to explore computer vision and virtual reality based on RGB-Depth images. The paper focuses on improving approach for detecting, training, and recognizing the state sequences of hand motions automatically. The hand movements of three persons are recorded as the input of a recognition system. These hand movements correspond to five actions: sweeping right to left, sweeping top to bottom, circle motion, square motion, and triangle motion. The skeletal data of hand joint are collected to build an observation database. Desired features of each hand action are extracted from skeleton video frames by using the Principle Component Analysis (PCA) algorithm for training and recognition. A hidden Markov model (HMM) is applied to train the feature data and recognize various states of hand movements. The experimental results showed that the proposed method achieved the average accuracy nearly 95.66% and 91.00% for offline and online recognition, respectively.