ArticleJournal of neuroengineering and rehabilitation2024
Decoding hand and wrist movement intention from chronic stroke survivors with hemiparesis using a user-friendly, wearable EMG-based neural interface.
Article in Journal of neuroengineering and rehabilitation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Wearable neurotechnology systems for upper extremity rehabilitation in children with cerebral palsy: a scoping review.Frontiers in neurology · 2025Pooled it
- Intention-driven functional electrical stimulation therapy using a wearable high-density electrode sleeve in chronic stroke: a case report.Journal of neuroengineering and rehabilitation · 2026Article
- sEMG-Based Muscle Synergy Analysis and Functional Driving Ratio for Quantitative Assessment During Robot-Assisted Upper-Limb Rehabilitation.Sensors (Basel, Switzerland) · 2026Article
- Decoding multi-class motor attempt from the affected unilateral limbs in chronic stroke patients.Journal of neuroengineering and rehabilitation · 2026Article
- Leveraging neural drive to assess hand motor function in individuals with chronic stroke.Journal of neuroengineering and rehabilitation · 2026Article
- Preliminary Investigation of Myoelectric Control of an Assistive Neck Exoskeleton by Individuals With Amyotrophic Lateral Sclerosis.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026Article
- NeuraLoop: a high bandwidth closed-loop human-machine interface.Journal of neuroengineering and rehabilitation · 2025Article
- Improved Prediction of Activities of Daily Living from Wrist Electromyography Using Intermediate Gesture Classification.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025Article
- Simultaneous and Proportional Myoelectric Control of Multiple Degrees of Freedom in Individuals With Chronic Hemiparesis.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- Influence of electrode placement on the recognition of different gesture categories using high-density sEMG.Frontiers in neuroscience · 2025Article
- RETRACTED: Optimizing the impact of time domain segmentation techniques on upper limb EMG decoding using multimodal features.PloS one · 2025Article
- Proportional myoelectric control of a virtual bionic arm in participants with hemiparesis, muscle spasticity, and impaired range of motion.Journal of neuroengineering and rehabilitation · 2024Article
- Wearable high-density EMG sleeve for complex hand gesture classification and continuous joint angle estimation.Scientific reports · 2024Article
- Benchtop Performance of Novel Mixed Ionic-Electronic Conductive Electrode Form Factors for Biopotential Recordings.Sensors (Basel, Switzerland) · 2024Article
- A review on EMG/EEG based control scheme of upper limb rehabilitation robots for stroke patients.Heliyon · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveSeventy-five percent of stroke survivors, caregivers, and health care professionals (HCP) believe current therapy practices are insufficient, specifically calling out the upper extremity as an area where innovation is needed to develop highly usable prosthetics/orthotics for the stroke population. A promising method for controlling upper extremity technologies is to infer movement intention non-invasively from surface electromyography (EMG). However, existing technologies are often limited to research settings and struggle to meet user needs. APPROACH: To address these limitations, we have developed the NeuroLife MAIN
resultsOur decoding algorithm trained on historical- and within-session data produced an overall accuracy of 77.1 ± 5.6% across 12 movements and rest in stroke participants. For individuals with severe hand impairment, we demonstrate the ability to decode a subset of two fundamental movements and rest at 85.4 ± 6.4% accuracy. In online scenarios, two stroke survivors achieved 91.34 ± 1.53% across three movements and rest, highlighting the potential as a control mechanism for assistive technologies. Feedback from stroke survivors who tested the system indicates that the sleeve's design meets various user needs, including being comfortable, portable, and lightweight. The sleeve is in a form factor such that it can be used at home without an expert technician and can be worn for multiple hours without discomfort. SIGNIFICANCE: The NeuroLife EMG System represents a platform technology to record and decode high-resolution EMG for the real-time control of assistive devices in a form factor designed to meet user needs. The NeuroLife EMG System is currently limited by U.S. federal law to investigational use.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.