Transformer-Based Trajectory Prescription for sEMG-Optimizing Robot-Mediated Rehabilitation
Overview
This research introduces a personalizable transformer model that maps muscle activations measured via surface electromyography to prescribed contact force trajectories for rehabilitation robots. Using data from 16 healthy participants, the model achieves mean trajectory prediction errors of less than 8% on held-out tasks and users, with effectively instantaneous deterministic inference and sub-second sampling of trajectory distributions. An initial test during live rehabilitation tasks shows participants reaching target neuromotor activations with an average error of just 15%. The work provides a proof of concept for data-driven, activation-targeted trajectory prescription in robot-mediated rehabilitation and establishes a foundation for future validation in clinical populations. Data and code are open-sourced as part of the OpenRobotRehab project on SimTK.
Generated from attributed reports · Updated 2 days ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Data origin: robot trajectories
“itation robot behavior, we present a personalizable transformer-based model mapping individuals' muscle activations (as measured via surface electromyography) to prescribed contact force trajectories when interacting with an end-effector rehabilitation robot. The model uses a transformer encoder-decoder leveraging rotary positional embeddings (RoPE) to generate a latent representation of trajectory-relevant and user-”
Exact source · revision 1Source owner not reported
Reported quantity · trajectory tracking tasks: 15 other · reported trials
“specific neuromotor factors, and augments trajectory prediction with flow matching to produce a distribution over plausible prescriptions biased toward the training distribution. Using data from 16 healthy participants completing 15 isometric trajectory tracking tasks, we evaluate generalization across held-out tasks and users, as well as the feasibility of user-specific fine-tuning within a therapy-session timescale”
Exact source · revision 1Source owner not reported
Artifact availability · code: reported pending
“de a proof of concept for data-driven, activation-targeted trajectory prescription in robot-mediated rehabilitation and establish a foundation for future validation in clinical populations. Data and code have been open-sourced as part of the OpenRobotRehab project on SimTK.”
Exact source · revision 1Source owner not reported
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalTransformer-Based Trajectory Prescription for sEMG-Optimizing Robot-Mediated Rehabilitation
This research introduces a personalizable transformer model that maps muscle activations measured via surface electromyography to prescribed contact force trajectories for rehabilitation robots. Using data from 16 healthy participants, the model achieves mean trajectory prediction errors of less than 8% on held-out tasks and users, with effectively instantaneous deterministic inference and sub-second sampling of trajectory distributions. An initial test during live rehabilitation tasks shows participants reaching target neuromotor activations with an average error of just 15%. The work provides a proof of concept for data-driven, activation-targeted trajectory prescription in robot-mediated rehabilitation and establishes a foundation for future validation in clinical populations. Data and code are open-sourced as part of the OpenRobotRehab project on SimTK.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).