• Title/Summary/Keyword: EMG Decoding

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Implementation of four-subject four-channel optical telemetry system with enforced synchronization (강제 동기식 4생체 4채널 광펠레미트리시스템 구현)

  • ;;;M.Ishida
    • Journal of the Korean Institute of Telematics and Electronics D
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    • v.35D no.7
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    • pp.40-47
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    • 1998
  • This paper presents the physiological signal processing CMOS one chip for transmitting human bodys small electrical signals such as electrocardiogram(EKG) or electromyogram(EMG) and the external system for receiving signals was implemented by the commercial ICs. For simultaneous four-subject four-channel telemetry, a new enfored synchronization techniqeu using infrared bi-directional communication has been proposed. The telemeter IC with the size of 5.1*5.1mm$^{2}$ has the following functions: receiving of command signal, initialization of internal state of all functional blocks, decoding of subject-selection signal, time multiplexing of 4-channel modulated physiological signals, transmitting of telemetry signal to external system and auto power down control. The newly designed synchronized oscillator with low supply voltage dependence in the telemeter IC operates at a supply voltage from 4.6~6.0V and the nonlinearity error of PIM modulator was less than 1.2%F.S(full scale). The power saving block operates at the period of 2.5ms even if the telemetry IC does not receive command signal from external system for a constant time.

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Smart HCI Based on the Informations Fusion of Biosignal and Vision (생체 신호와 비전 정보의 융합을 통한 스마트 휴먼-컴퓨터 인터페이스)

  • Kang, Hee-Su;Shin, Hyun-Chool
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.47 no.4
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    • pp.47-54
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    • 2010
  • We propose a smart human-computer interface replacing conventional mouse interface. The interface is able to control cursor and command action with only hand performing without object. Four finger motions(left click, right click, hold, drag) for command action are enough to express all mouse function. Also we materialize cursor movement control using image processing. The measure what we use for inference is entropy of EMG signal, gaussian modeling and maximum likelihood estimation. In image processing for cursor control, we use color recognition to get the center point of finger tip from marker, and map the point onto cursor. Accuracy of finger movement inference is over 95% and cursor control works naturally without delay. we materialize whole system to check its performance and utility.