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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

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Performance Measurement of Gesture-Based Human-Machine Interfaces Within eXtended Reality Head-Mounted Displays.

Leopoldo Angrisani¹, Mauro D'Arco¹, Egidio De Benedetto¹

¹Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Napoli, Italy.

Sensors (Basel, Switzerland)

|May 14, 2025

Summary

This study introduces a method to measure Human-Machine Interface (HMI) performance using hand gestures in extended reality (XR) headsets. The approach accounts for user variability, ensuring reliable performance metrics for XR HMI evaluation.

Keywords:

GUM eXtended reality gesture recognition human–machine interaction measurement metrology performance characterization uncertainty

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Area of Science:

Human-Computer Interaction
Virtual Reality
Measurement Science

Background:

Human-Machine Interfaces (HMIs) in eXtended Reality (XR) Head-Mounted Displays (HMDs) increasingly rely on hand-gesture recognition.
Evaluating the performance and reliability of these gesture-based HMIs is crucial for user experience and application success.
Existing methods may not adequately address the inherent variability in human performance and measurement uncertainty.

Purpose of the Study:

To propose and validate a systematic method for measuring the performance of hand-gesture-based HMIs within XR HMDs.
To ensure performance measurements comply with the Guide to the Expression of Uncertainty in Measurement (GUM).
To provide a framework for analyzing intra- and inter-individual variability in gesture recognition performance.

Main Methods:

Development of a testbed with icons presented in the XR HMD's field of view.
Cue-guided selection tasks using specific hand gestures (e.g., finger-tapping).
Conducting multiple selection cycles with diverse participants to collect performance data and measure uncertainty contributions.

Main Results:

The proposed method yields performance metrics that consider gesture parameters and user variability.
Case study with Microsoft HoloLens 2 and finger-tapping demonstrated the method's ability to reveal performance trends.
Statistical analyses confirmed the capacity to differentiate performance improvements due to user familiarity versus accuracy changes.

Conclusions:

The developed framework offers a comprehensive approach to evaluating hand-gesture-based XR HMIs.
The method provides valuable insights into performance across different users and gesture parameters.
It enables the assessment of HMI compliance with performance specifications for specific applications.