Temporal Posed and Spontaneous Gesture Recognition from Electromyography in the Rock-Paper-Scissors Game
arXiv:2606.29423v1 Announce Type: new Abstract: The importance of gesture recognition has been acknowledged in many domains requiring real-time recognition systems. Two requirements for these are fast recognition in multiuser contexts. Therefore, we explored the temporal characteristics of electromyography (EMG) and its accuracy in recognizing gestures in a Rock-Paper-Scissors (RPS) game. Twenty-four participants played RPS in dyads, while a two-channel EMG was recorded from the forearm. We found out that EMG onsets could be detected at least 800 ms before the gesture's visible onset, and that
Advances in machine learning and sensor technology are enabling more sophisticated real-time human-computer interaction methods.
Early and accurate gesture recognition using non-visual cues has implications for control systems, prosthetics, and ambient computing, potentially creating more intuitive interfaces.
This research suggests a potential for predictive interfaces that anticipate human actions rather than merely reacting to them, particularly in settings requiring quick decision-making.
- · Human-computer interaction companies
- · Gaming industry
- · Prosthetics and assistive technology developers
- · Wearable tech manufacturers
- · Traditional gesture recognition methods dependent on visual input
- · Systems with high latency in human-computer interaction
- · Manufacturers of clunky or slow control interfaces
Gesture recognition systems could become more seamless and proactive, improving user experience and efficiency.
This could lead to new applications in areas like remote surgery, manufacturing, or even advanced robotics where early intent detection is crucial.
Ethical considerations around predictive interfaces and potential misinterpretations of intent could become more prominent, requiring new regulatory frameworks.
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