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.
By Gabriel Parra, Kimia Khoshnami, Daniel S. Brown, Laura A. Hallock
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...
By Shreyasvi Natraj, Mathieu Ruepp, Yanke Li, Robert Riener, Inge Eriks-Hoogland, Diego Paez-Granados
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.
By Lara Pereira, Jo\~ao Ruivo Paulo, Pedro Santos, Paulo Peixoto
arXiv:2608. 15995v1 Announce Type: cross Abstract: Upper extremity motor function recovery is positively linked to Task-Specific Training (TST) and sufficient therapy dosage.
By Jia Quan Loh (Human Robotics Laboratory, Department of Mechanical Engineering, The University of Melbourne), Vincent Crocher (Human Robotics Laboratory, Department of Mechanical Engineering, The University of Melbourne), Marlena Klaic (Melbourne School of Health Sciences, The University of Melbourne), Denny Oetomo (Human Robotics Laboratory, Department of Mechanical Engineering, The University of Melbourne), Ying Tan (Human Robotics Laboratory, Department of Mechanical Engineering, The University of Melbourne)
arXiv:2508. 18066v2 Announce Type: replace-cross Abstract: Controlling high-dimensional and nonlinear musculoskeletal models of the human body is a foundational scientific challenge.
By Boshi An, Alberto Silvio Chiappa, Merkourios Simos, Chengkun Li, Alexander Mathis
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.
By Diwas Lamsal, Juha Carlon, Reinhard Claeys, Maxim Yudayev, Louis Flynn, Tom Verstraten, David Beckw\'ee, Eva Swinnen, Mihai B\^ace, Bart Vanrumste, Benjamin Filtjens
Muscle activity is fundamental to human movement, and understanding its patterns is critical for injury prevention and rehabilitation. Conventional muscle activity monitoring relies on specialized sen...
arXiv:2606. 24960v1 Announce Type: new Abstract: Tailoring stroke rehabilitation requires assessing how movements are organized, not merely if they succeed.
By Tamim Ahmed, Thanassis Rikakis
This scoping review examined 117 studies on video-based markerless motion capture, most published from 2024 onward and focused on healthy adults walking in laboratories. The studies identified five main pipeline architectures, but most reported only raw joint angles without biomechanical refinement, achieving sagittal lower‑limb agreement of about 5–6°, which falls short of clinical acceptability. Validation of out‑of‑plane kinematics, kinetics, and performance in older or pathological populations was rare, and emerging computer‑vision techniques such as foundation‑model mesh recovery and differentiable inverse kinematics were largely absent from validated work.
By Florian Delaplace (LAMHESS, CHU), Elodie Piche (LAMHESS), Fr\'ed\'eric Chorin (IUF, LAMHESS), Raphael Zory (IUF, LAMHESS)
Pose2Muscle is a pose-driven framework that estimates discrete muscle activity states without requiring surface electromyography (sEMG) during inference. It reformulates muscle estimation as a structured prediction problem, using multi-scale spatio-temporal attention and a directed acyclic graph-based decoder to capture motion patterns and maintain multiple candidate hypotheses. The authors introduce the PoseEMG-43 dataset, comprising 2,992 movement instances from 43 daily-life actions performed by 14 participants, and demonstrate that Pose2Muscle outperforms baseline methods with high accuracy and correlation metrics.
By Yuepeng Chen, Jiehong Shi, Kaili Zheng, Boyi Zhang, Chenyi Guo, Ji Wu, Xiangling Fu
arXiv:2610.07713v1 Announce Type: new
Abstract: Surface electromyography (sEMG) provides a wearable, noninvasive interface to neuromuscular activity for movement decoding and human-computer interacti...
By He Wang, Hongyuan Qi, Zhaoxian Zhang, Jinbin Luo, Linyi He, Mehul Motani, Changsheng Wu
arXiv:2607. 01838v1 Announce Type: new Abstract: Counterfactual explanations (CEs) for multivariate time-series classifiers are often difficult to interpret in domains where experts reason in terms of semantic feature groups rather than individual channels.
By Emmanuel C. Chukwu, Rianne M. Schouten, Monique Tabak, Mykola Pechenizkiy