Comparing online wrist and forearm EMG-based control using a rhythm game-inspired evaluation environment.

Area of Science:

• Biomedical Engineering
• Human-Computer Interaction
• Neuroscience

Background:

• Electromyogram (EMG) signals from the wrist are a growing area for human-machine interaction (HMI).
• Prior research on wrist-based EMG control is limited, especially in real-time, user-interactive studies.
• Existing evaluation frameworks, often from prosthetics, may not fully capture the potential of wrist-based EMG for diverse HMI applications.

Purpose of the Study:

• To compare the online usability of wrist-based versus forearm-based EMG control.
• To introduce a new evaluation environment inspired by rhythm games and speed-accuracy tradeoffs.
• To assess EMG control performance under varying difficulty levels and stress.

Main Methods:

• Development of a novel, temporally constrained evaluation environment with linearly increasing difficulty.
• Comparison of wrist and forearm EMG control using this new environment.
• Analysis of online and offline usability metrics, including speed, accuracy, and stress impact.

Main Results:

• Wrist EMG control demonstrated comparable performance to forearm EMG for traditional prosthetic gestures.
• Wrist EMG control outperformed forearm EMG for fine finger gestures.
• Online performance metrics showed decreased correlation with offline metrics as environmental difficulty increased.

Conclusions:

• Wrist-based EMG is a viable and potentially superior control modality for specific HMI applications.
• Real-time, dynamic evaluation environments are crucial for accurately assessing myoelectric control systems.
• The proposed evaluation framework offers deeper insights into EMG control usability beyond traditional methods.