ArticleComputational intelligence and neuroscience2020
Lw-CNN-Based Myoelectric Signal Recognition and Real-Time Control of Robotic Arm for Upper-Limb Rehabilitation.
Article in Computational intelligence and neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions.Journal of neuroengineering and rehabilitation · 2025Pooled it
- From Sensors to Care: How Robotic Skin Is Transforming Modern Healthcare-A Mini Review.Sensors (Basel, Switzerland) · 2025Review
- Action recognition in rehabilitation: combining 3D convolution and LSTM with spatiotemporal attention.Frontiers in physiology · 2024Article
- Review of adaptive control for stroke lower limb exoskeleton rehabilitation robot based on motion intention recognition.Frontiers in neurorobotics · 2023Review
- Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features.Computational intelligence and neuroscience · 2022Article
- The Impact of Load Style Variation on Gait Recognition Based on sEMG Images Using a Convolutional Neural Network.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep-learning models can realize the feature extraction and advanced abstraction of raw myoelectric signals without necessitating manual selection. Raw surface myoelectric signals are processed with a deep model in this study to investigate the feasibility of recognizing upper-limb motion intents and real-time control of auxiliary equipment for upper-limb rehabilitation training. Surface myoelectric signals are collected on six motions of eight subjects' upper limbs. A light-weight convolutional neural network (Lw-CNN) and support vector machine (SVM) model are designed for myoelectric signal pattern recognition. The offline and online performance of the two models are then compared. The average accuracy is (90 ± 5)% for the Lw-CNN and (82.5 ± 3.5)% for the SVM in offline testing of all subjects, which prevails over (84 ± 6)% for the online Lw-CNN and (79 ± 4)% for SVM. The robotic arm control accuracy is (88.5 ± 5.5)%. Significance analysis shows no significant correlation (
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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.