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A Tutorial on Support Vector Machines for Pattern Recognition
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State-based Classification of Finger Gestures from Electromyographic Signals
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TalkBack: a conversational answering machine
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PlayAnywhere: a compact interactive tabletop projection-vision system
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Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Gesture-based control and EMG decomposition
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NeuroPad: use cases for a mobile physiological interface
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Previous work has demonstrated the viability of applying offline analysis to interpret forearm electromyography (EMG) and classify finger gestures on a physical surface. We extend those results to bring us closer to using muscle-computer interfaces for always-available input in real-world applications. We leverage existing taxonomies of natural human grips to develop a gesture set covering interaction in free space even when hands are busy with other objects. We present a system that classifies these gestures in real-time and we introduce a bi-manual paradigm that enables use in interactive systems. We report experimental results demonstrating four-finger classification accuracies averaging 79% for pinching, 85% while holding a travel mug, and 88% when carrying a weighted bag. We further show generalizability across different arm postures and explore the tradeoffs of providing real-time visual feedback.