ArticleSensors (Basel, Switzerland)2021
Fusion Learning for sEMG Recognition of Multiple Upper-Limb Rehabilitation Movements.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Bibliometric analysis of surface electromyography trends in stroke rehabilitation research.Frontiers in neurology · 2025Pooled it
- Mitigating the Impact of Electrode Shift on Classification Performance in Electromyography Applications Using Sliding-Window Normalization.Sensors (Basel, Switzerland) · 2025Article
- sEMG-based gesture recognition using multi-stream adaptive CNNs with integrated residual modules.Frontiers in bioengineering and biotechnology · 2025Article
- Lower Limb Motion Recognition Based on sEMG and CNN-TL Fusion Model.Sensors (Basel, Switzerland) · 2024Article
- A Hand Gesture Recognition Strategy Based on Virtual-Dimension Increase of EMG.Cyborg and bionic systems (Washington, D.C.) · 2024Article
- Recognition of Upper Limb Action Intention Based on IMU.Sensors (Basel, Switzerland) · 2022Article
- Research Hotspots of the Rehabilitation Medicine Use of sEMG in Recent 12 Years: A Bibliometric Analysis.Journal of pain research · 2022Article
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Authors and funding
6 authors.
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Abstract
Surface electromyogram (sEMG) signals have been used in human motion intention recognition, which has significant application prospects in the fields of rehabilitation medicine and cognitive science. However, some valuable dynamic information on upper-limb motions is lost in the process of feature extraction for sEMG signals, and there exists the fact that only a small variety of rehabilitation movements can be distinguished, and the classification accuracy is easily affected. To solve these dilemmas, first, a multiscale time-frequency information fusion representation method (MTFIFR) is proposed to obtain the time-frequency features of multichannel sEMG signals. Then, this paper designs the multiple feature fusion network (MFFN), which aims at strengthening the ability of feature extraction. Finally, a deep belief network (DBN) was introduced as the classification model of the MFFN to boost the generalization performance for more types of upper-limb movements. In the experiments, 12 kinds of upper-limb rehabilitation actions were recognized utilizing four sEMG sensors. The maximum identification accuracy was 86.10% and the average classification accuracy of the proposed MFFN was 73.49%, indicating that the time-frequency representation approach combined with the MFFN is superior to the traditional machine learning and convolutional neural network.
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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.