Fusion Learning for sEMG Recognition of Multiple Upper-Limb Rehabilitation Movements
| العنوان: | Fusion Learning for sEMG Recognition of Multiple Upper-Limb Rehabilitation Movements |
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| المؤلفون: | Donglin Li, Tianyang Zhong, Zida An, Yue Zhu, Jiacan Xu, Jianhui Wang |
| المصدر: | Sensors, Vol 21, Iss 5385, p 5385 (2021) Sensors Volume 21 Issue 16 Sensors (Basel, Switzerland) |
| بيانات النشر: | MDPI AG, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Computer science, Generalization, Movement, medicine.medical_treatment, Feature extraction, TP1-1185, Biochemistry, Convolutional neural network, Article, Analytical Chemistry, Upper Extremity, Motion, Deep belief network, medicine, Humans, Electrical and Electronic Engineering, Representation (mathematics), surface electromyogram, Instrumentation, multiple feature fusion network, deep belief network, Rehabilitation, Electromyography, business.industry, multiscale time–frequency information fusion representation, Chemical technology, Process (computing), Pattern recognition, Atomic and Molecular Physics, and Optics, Identification (information), motion intention recognition, Neural Networks, Computer, Artificial intelligence, business |
| الوصف: | 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. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1424-8220 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b41c051b757c220ed36cd687f1fea7ac https://www.mdpi.com/1424-8220/21/16/5385 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....b41c051b757c220ed36cd687f1fea7ac |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14248220 |
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