Rehabilitation

Image-Derived Skeletal Muscle Activation Metric Map: a Forearm Study Using Ultra-Fast Ultrasound Imaging and High-Density Electromyography
Image-Derived Skeletal Muscle Activation Metric Map: a Forearm Study Using Ultra-Fast Ultrasound Imaging and High-Density Electromyography 640 360 IEEE Transactions on Biomedical Engineering (TBME)
We derived muscle activation metrics using ultra-fast ultrasound imaging, which can be alternative or complementary to surface electromyography for investigating skeletal muscle function, and diagnosis or prognosis of associated diseases. read more
Accurate Real-Time Joint Torque Estimation for Dynamic Prediction of Human Locomotion

Accurate Real-Time Joint Torque Estimation for Dynamic Prediction of Human Locomotion
Accurate Real-Time Joint Torque Estimation for Dynamic Prediction of Human Locomotion 789 444 IEEE Transactions on Biomedical Engineering (TBME)
A real-time human torque estimator is proposed and evaluated across various walking conditions. This estimator consists of a neural network and a dynamical model and only requires kinematic measurements. read more

Design a Novel BCI for Neurorehabilitation Using Concurrent LFP and EEG Features: A Case Study
Author(s)3: Yi Sun, Yu Qi, Yueming Wang, Cuntai Guan, Yu Sun
Design a Novel BCI for Neurorehabilitation Using Concurrent LFP and EEG Features: A Case Study IEEE Transactions on Biomedical Engineering (TBME)
This work introduced for the first time a novel BCI that incorporate both intracortical LFP and scalp EEG (named, LFP-EEG-BCI) for motor intention decoding during neurorehabilitation. Concurrent intracortical and scalp signals were collected from a paraplegic patient undergoing motor imagery (MI) neurorehabilitation training. A common spatial filter approach was adopted for feature extraction and a decision fusion strategy was further introduced to obtain the decoding results. Transfer learning approach was also utilized to reduce the calibration. The proposed novel LFP-EEG-BCI may lead to new directions for developing practical neurorehabilitation systems in clinical applications. read more

A Hybrid Robotic System for Arm Training of Stroke Survivors: Concept and First Evaluation
Author(s)3: Emilia Ambrosini, Johannes Zajc, Simona Ferrante, Giancarlo Ferrigno, Stefano Dalla Gasperina, Maria Bulgheroni, Walter Baccinelli, Thomas Schauer, Constantin Wiesener, Michael Russold, Margit Gfohler, Markus Puchinger, Matthias Weber, Sebastian Becker, Karsten Krakow, Nancy Immick, Andreas Augsten, Mauro Rossini, Davide Proserpio, Giulio Gasperini, Franco Molteni, Alessandra Pedrocchi
A Hybrid Robotic System for Arm Training of Stroke Survivors: Concept and First Evaluation 170 177 IEEE Transactions on Biomedical Engineering (TBME)

                      This work describes a hybrid robotic system for arm recovery after stroke, combining EMG-triggered Functional Electrical Stimulation (FES) with a passive exoskeleton for upper limb suspension. The system was…

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