Papers for

rehabilitation device developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Reflex informed learning improves muscle driven human walking control

Reflex-Informed Neuromuscular Reinforcement Learning for Muscle-Driven Locomotion

Abstract: Muscle-driven locomotion provides a physically grounded approach to generating realistic human movement. However, achieving both physiological plausibility and adaptability to changes in musculoskeletal capacity and external disturbances remains a fundamental challenge. To address this limitation, we propose a Reflex-Informed Neuromuscular Reinforcement Learning framework for muscle-driven locomotion. Within this framework, a fixed phase-dependent reflex controller serves as the underlying neuromuscular control mechanism, while the reinforcement learning policy produces four biomechanically meaningful residual parameters to modulate key reflex gains and thresholds associated with hip swing, knee support, and ankle propulsion according to the current state. Experimental results demonstrate that the proposed framework generates physiologically plausible locomotion with improved kinematic accuracy and dynamic consistency, as well as better bilateral symmetry and stride-to-stride consistency under nominal walking conditions. The learned policy remains robust under muscle weakness and external perturbations without retraining.

Thu 10 SeptRoboticsGraphicsMachine Learning
The gist
Controlling human-like walking using muscle models is hard because it requires movements to be both realistic and adaptable to changes like muscle weakness or bumps. The authors combine a basic reflex system with reinforcement learning to adjust key muscle-related controls dynamically. This approach helps produce walking patterns that look more natural, stay balanced between legs, and handle disturbances without retraining. The resulting control method mimics how humans might adjust their muscle reflexes when walking under different conditions.
Open → 2609.11733v1

Deep learning predicts finger movements from muscle signals after stroke

Deep Neural Networks for Learning Intent from sEMG Signals to Support Hardware Devices for Post-Stroke Neurorehabilitation

Abstract: Finger-specific motor intent is a clinically meaningful control signal for post-stroke neurorehabilitation, where residual muscle activity may remain measurable despite weak or incomplete movement. We study five-finger multilabel intent decoding from impaired-arm high-density surface electromyography (sEMG) in PhysioMio, a bilateral longitudinal dataset collected from stroke patients. A common processing protocol aligns movement labels, applies 20--450 Hz Butterworth filtering and Symlet-4 wavelet denoising, segments overlapping 200 ms windows, and extracts twelve time- and frequency-domain descriptors per channel. Direct LSTM, CNN, and GNN baselines reveal complementary behavior: the LSTM attains the highest subset accuracy (0.545), whereas the GNN attains the highest macro F1 (0.706) and macro AUPRC (0.776). Architecture search then identifies CNN-Large as the strongest single-split CNN, with 0.593 subset accuracy and 0.714 macro F1, while CNN-Micro provides a compact architecture for embedded inference. To match a four-sensor hardware design, we retrain CNN-Micro using channels associated with ECRB, ECRL, FDS, and FDP and exclude the ground electrode from model input. Across five seeds, cross-channel knowledge distillation improves the four-channel student over direct training, reaching $0.5219 \pm 0.0114$ subset accuracy, $0.7612 \pm 0.0038$ finger accuracy, and $0.6095 \pm 0.0058$ macro F1. The selected 123K-parameter model accepts nine windows of 48 features and has been exported to ONNX. These results establish a reproducible software path from post-stroke sEMG to compact five-finger intent prediction for subsequent hardware-in-the-loop evaluation.

Wed 9 SeptMachine Learning
The gist
After a stroke, people often have weak finger movements but their muscles still send signals. The authors worked on reading these muscle signals to figure out which fingers someone intends to move, even if the movement is weak or incomplete. They tested different types of deep learning models on muscle data from stroke patients and found that certain neural network designs work better at decoding finger movement intent. This approach could help create devices that assist stroke patients by understanding their finger movement commands from muscle signals.
Open → 2609.09971v1