The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
This study introduces a transformer-based model that personalizes robot-mediated rehabilitation by mapping surface electromyography (sEMG) signals to contact force trajectories. Using a transformer encoder-decoder with rotary positional embeddings and flow matching, the model predicts trajectories with less than 8% error on unseen tasks and users, and can be fine-tuned within a single therapy session. Live tests show participants achieve target neuromotor activations with only 15% error, demonstrating the feasibility of activation-targeted trajectory prescription.
arXiv:2610.05552v2 Announce Type: replace Abstract: Three-dimensional gait analysis guides rehabilitation after spinal cord injury but depends on marker-based motion capture and force plates, which f...
arXiv:2608. 12145v1 Announce Type: cross Abstract: Autonomous rehabilitation systems must not only recognize human motion but also provide structured feedback to support users without continuous therapist supervision.
arXiv:2608. 15995v1 Announce Type: cross Abstract: Upper extremity motor function recovery is positively linked to Task-Specific Training (TST) and sufficient therapy dosage.
arXiv:2508. 18066v2 Announce Type: replace-cross Abstract: Controlling high-dimensional and nonlinear musculoskeletal models of the human body is a foundational scientific challenge.
RevalExo is a new benchmark for locomotion mode recognition that focuses on functional daily activities performed by older adults and clinical cohorts. It includes 27 participants from three groups—healthy older adults, stroke survivors, and older adults with probable sarcopenia—recorded with lower-body IMUs and, for a subset, synchronized egocentric video. The dataset offers 10.1 hours of frame‑level annotations across 11 locomotion modes, and the authors evaluate unimodal, multimodal, cross‑population, and cross‑modal recognition challenges, finding that sensor fusion improves performance but transitions and generalization remain difficult.