Transformer-Based Context Informed Incremental Learning with sDTW Alignment Unlocks Fast and Precise Regression-Based Myoelectric Control | JoVE Visualize

Area of Science:

Biomedical Engineering
Neuroscience
Human-Computer Interaction

Background:

Regression-based myoelectric interfaces offer intuitive control but face challenges with calibration, dynamics, and user consistency.
Temporal neural architectures can improve controllers by learning user behavior patterns, but require representative closed-loop training data.
Context-informed incremental learning (CIIL) acquires data online but struggles with temporal deviations between assumed and true user intent.

Purpose of the Study:

To introduce T-sDTW-CIIL, a novel transformer-based incremental learning framework for myoelectric control.
To integrate temporal modeling, closed-loop learning, and soft dynamic time warping (sDTW) for tolerant label alignment.
To evaluate T-sDTW-CIIL's performance against traditional methods in an adaptive cursor-control task.

Main Methods:

Developed T-sDTW-CIIL, a transformer-based incremental learning framework incorporating temporal modeling and sDTW.
Recruited twelve participants for a regression-based cursor-control task using static and CIIL variants of MLP and transformer models.
Assessed performance in a high-precision ISO-Fitts' environment, measuring success rates, throughput, efficiency, and simultaneity.

Main Results:

T-sDTW-CIIL demonstrated significantly higher success rates, throughputs, efficiencies, and simultaneity gains compared to baseline MLP.
Achieved 2.0x, 2.4x, and 3.7x higher throughputs for large, medium, and small targets, respectively, versus static MLP.
Maintained a 98.4% success rate for small targets, while static MLP degraded to 23.4%; reduced contraction intensity by ~10%.

Conclusions:

T-sDTW-CIIL effectively combines temporal learning with context-informed co-adaptation, overcoming limitations of existing myoelectric controllers.
The framework enables robust, low-intensity human-computer interaction through improved real-time adaptation and user intent alignment.
Results highlight the potential of advanced temporal neural architectures for next-generation prosthetic and assistive devices.