Recognition of Fighting Motion using a 3D-Chain Code and HMM

Han, Chang-ho; Oh, Cheol; Choi, Byung-Wook

doi:10.5302/j.icros.2010.16.8.756

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…The method HMM for gesture recognition Kazakh dance movement, based on the research results obtained by the recognition rate of 90.82%. The method HMM and 3D chain code (27 direction) to recognize fighting motion result 85 % [20].…”

Section: Related Workmentioning

confidence: 99%

Gesture Recognition of Dance using Chain Code and Hidden Markov Model

Sutopo¹

2019

IJATCSE

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Dance one culture consists of motion. This paper seeks to recognize Golek Menak Dance movement to be easily studied from Indonesia, where the dance of the dancers (actor) is performed by using the motion capture Kinect sensor which then produces motion data format with Biovision Hierarchy (BVH), where data is a tensor which has position x, y, z. This research use test data Jogetan and Sabetan movement carried out featuring by Chain Code 15 (CC-15), which is a combination of 15 directions with forward motion (1), backward (-1) and fixed (0) to obtain vector quantization which is then carried by the gesture recognition using Hidden Markov Model (HMM). The novelty in this paper use Chain Code 15 (CC-15) to conduct the introduction featuring Dance with HMM classification, which produced an accuracy of 90% of ten (10) test data movement.

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Section: Related Workmentioning

confidence: 99%

Gesture Recognition of Dance using Chain Code and Hidden Markov Model

Sutopo¹

2019

IJATCSE

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HSA-based HMM Optimization Method for Analyzing EEG Pattern of Motor Imagery

Ko¹,

Sim²

2011

Journal of Institute of Control, Robotics and Systems

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HMMs (Hidden Markov Models) are widely used for biological signal, such as EEG (electroencephalogram) sequence, analysis because of their ability to incorporate sequential information in their structure. A recent trends of research are going after the biological interpretable HMMs, and we need to control the complexity of the HMM so that it has good generalization performance. So, an automatic means of optimizing the structure of HMMs would be highly desirable. In this paper, we described a procedure of classification of motor imagery EEG signals using HMM. The motor imagery related EEG signals recorded from subjects performing left, right hand and foots motor imagery. And the proposed a method that was focus on the validation of the HSA (Harmony Search Algorithm) based optimization for HMM. Harmony search algorithm is sufficiently adaptable to allow incorporation of other techniques. A HMM training strategy using HSA is proposed, and it is tested on finding optimized structure for the pattern recognition of EEG sequence. The proposed HSA-HMM can performs global searching without initial parameter setting, local optima, and solution divergence.

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HMM-based Intent Recognition System using 3D Image Reconstruction Data

Ko¹,

Park²,

Kim³

et al. 2012

Journal of Korean Institute of Intelligent Systems

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The mirror neuron system in the cerebrum, which are handled by visual information-based imitative learning. When we observe the observer's range of mirror neuron system, we can assume intention of performance through progress of neural activation as specific range, in include of partially hidden range. It is goal of our paper that imitative learning is applied to 3D vision-based intelligent system. We have experiment as stereo camera-based restoration about acquired 3D image our previous research Using Optical flow, unscented Kalman filter. At this point, 3D input image is sequential continuous image as including of partially hidden range. We used Hidden Markov Model to perform the intention recognition about performance as result of restoration-based hidden range. The dynamic inference function about sequential input data have compatible properties such as hand gesture recognition include of hidden range. In this paper, for proposed intention recognition, we already had a simulation about object outline and feature extraction in the previous research, we generated temporal continuous feature vector about feature extraction and when we apply to Hidden Markov Model, make a result of simulation about hand gesture classification according to intention pattern. We got the result of hand gesture classification as value of posterior probability, and proved the accuracy outstandingness through the result.

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Recognition of Fighting Motion using a 3D-Chain Code and HMM

Cited by 3 publications

References 3 publications

Gesture Recognition of Dance using Chain Code and Hidden Markov Model

Gesture Recognition of Dance using Chain Code and Hidden Markov Model

HSA-based HMM Optimization Method for Analyzing EEG Pattern of Motor Imagery

HMM-based Intent Recognition System using 3D Image Reconstruction Data

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