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Updated: Jan 9, 2026

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

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

Published on: March 28, 2025

Hand motion prediction for distributed virtual environments.

Addison Chan¹, Rynson W H Lau, Lewis Li

¹Department of Computer Science, University of Durham, South Road, UK. a.s.k.chan@dur.ac.uk

IEEE Transactions on Visualization and Computer Graphics

|November 13, 2007

Summary

This summary is machine-generated.

This study introduces a novel prediction method for human hand motion to overcome network latency issues in distributed virtual environments (DVEs). The approach enhances real-time interaction by reducing prediction errors, improving collaborative experiences over the internet.

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Area of Science:

Computer Science
Human-Computer Interaction
Virtual Reality

Background:

Hands are crucial for object manipulation but underutilized as input in distributed virtual environments (DVEs).
High network latency and the hand's complex degrees of freedom pose significant challenges for real-time interaction and collaboration in DVEs.

Purpose of the Study:

To propose a novel prediction method for human hand motion tailored to address network latency in DVEs.
To improve the accuracy and efficiency of hand tracking and interaction over the internet.

Main Methods:

Developed a constraint-based motion prediction method by analyzing finger motion constraints.
Introduced a revised dead reckoning scheme to minimize average prediction error under high network latency.

Main Results:

The proposed prediction method demonstrated lower prediction error compared to existing popular methods.
The revised dead reckoning scheme significantly reduced average prediction error, especially in high-latency internet conditions.

Conclusions:

The developed hand motion prediction method effectively mitigates network latency issues in DVEs.
The revised dead reckoning scheme offers a more reliable approach for real-time interaction in challenging network environments.