2002 · 15 citations · 8 references
EngineeringBiomedical EngineeringComputer ScienceAssistive TechnologyBiomedical Signal ProcessingHuman-computer InteractionAssistive DevicesAssistive Technology DesignElectromyogram AnalysisHuman-machine InterfacesMovement Pattern AnalysisMuscle Signal ProcessingCepstral CoefficientsCepstral FeaturesElectromyography Feature ExtractionElectromyography Signal ClassificationFeature RecognitionHuman-computer Interface DesignMuscle ContractionMuscle Contraction Detection
The purpose of this study is to develop a human-computer interface (HCI) application based on a real-time EMG discrimination system. A personal computer with a plug-in data acquisition and processing board containing a floating-point digital signal processor are used to attain real-time EMG classification. The integrated EMG is employed to detect the onset of muscle contraction. The cepstral coefficients derived from AR coefficients and estimated by a recursive least square algorithm, are used as the recognition feature. These features are then discriminated using a modified maximum likelihood distance classifier. The identified commands control the mouse cursor. It is fully compatible with a Microsoft serial mouse. This system can move the cursor in four directions, and double-click the icon in GUI operating systems.
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Björn Wittenmark · Automatica · 1993 · 3.1K citations
IEEE Acoustics Speech and Signal Processing Newsletter · 1974 · 1.3K citations
Wen-Juh Kang, Jiue-Rou Shiu, Cheng‐Kung Cheng et al. · IEEE Transactions on Biomedical Engineering · 1995 · 120 citations
Engineering, Rehabilitation Medicine, Biomedical Signal Processing +13