Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Zheng-Yang Bi"'
Autor:
Hai-Peng Wang, Zheng-Yang Bi, Wen-Jie Fan, Yi-Xin Zhou, Yu-Xuan Zhou, Fei Li, Keping Wang, Xiao-Ying Lu, Zhi-Gong Wang
Publikováno v:
IEEE Access, Vol 9, Pp 68320-68331 (2021)
In this paper, three easily implemented hardware algorithms, including the adaptive prediction error filter based on the Gram-Schmidt algorithm (GS-APEF), the least mean square adaptive filter and the comb filter, are extensively investigated for art
Externí odkaz:
https://doaj.org/article/810f47e3c12a44ad965104583208459e
Autor:
Yu-Xuan Zhou, Yang Xia, Jia Huang, Hai-Peng Wang, Xue-Liang Bao, Zheng-Yang Bi, Xiao-Bing Chen, Yu-Jie Gao, Xiao-Ying Lü, Zhi-Gong Wang
Publikováno v:
Journal of Rehabilitation Medicine, Vol 49, Iss 8, Pp 629-636 (2017)
Objective: The electromyographic bridge (EMGB) detects surface electromyographic signals from a non-paretic limb. It then generates electric pulse trains according to the electromyographic time domain features, which can be used to stimulate a paraly
Externí odkaz:
https://doaj.org/article/8c5f4fe9da934b6d8b89b7fc519a46ec
Publikováno v:
Neural Regeneration Research, Vol 12, Iss 1, Pp 133-142 (2017)
Voluntary participation of hemiplegic patients is crucial for functional electrical stimulation therapy. A wearable functional electrical stimulation system has been proposed for real-time volitional hand motor function control using the electromyogr
Externí odkaz:
https://doaj.org/article/033324d42aa74614981cf01c730c0c7c
Autor:
Wen-Jie Fan, Yixin Zhou, Yu-Xuan Zhou, Hai-Peng Wang, Zhigong Wang, Zheng-Yang Bi, Fei Li, Keping Wang, Xiaoying Lü
Publikováno v:
IEEE Access, Vol 9, Pp 68320-68331 (2021)
In this paper, three easily implemented hardware algorithms, including the adaptive prediction error filter based on the Gram-Schmidt algorithm (GS-APEF), the least mean square adaptive filter and the comb filter, are extensively investigated for art
Autor:
Xiaoying Lü, Jia Huang, Lisen Zhu, Zheng-Yang Bi, Yu-Xuan Zhou, Hai-Peng Wang, Yunlong Wang, Hong-Xing Wang, Chen-Xi Xie, Bi-Lei Wang, Zhigong Wang
Publikováno v:
IEEE Access. 8:137330-137341
In this study, a wearable prototype system was developed for multiple-gesture rehabilitation using electrical stimulation controlled by a volitional surface electromyography (sEMG) scan of a healthy forearm. The purpose of the prototype system is to
Publikováno v:
EMBC
A surface electromyography (sEMG) detector, that not only removes stimulation artifacts entirely but also increases the recording time, has been developed in this paper. The sEMG detector consists of an sEMG detection circuit and a stimulation isolat
Autor:
Benhui Hu, Zhigong Wang, Keping Wang, Yu-Xuan Zhou, Xiaoying Lü, Wei Wang, Zheng-Yang Bi, Shisheng Chen, Minjie Ji
Publikováno v:
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. 28(5)
Objectives: The goal of this study is to design a novel approach for extracting volitional electromyography (vEMG) contaminated by functional electrical stimulation (FES) with time variant amplitudes and frequencies. Methods: A selective interpolatio
Publikováno v:
2019 IEEE 4th International Conference on Integrated Circuits and Microsystems (ICICM).
This paper presented an embedded control system based on Samsung Exynos4412 processor. It is designed for multi-channel EMG-Bridge prototype. The advanced processor Exynos4412 is used in the embedded control system with a 7-inch TFT-LCD touch screen
Publikováno v:
2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB).
The voluntary participation of paralyzed patients is crucial for the neuromuscular electrical stimulation (NMES) therapy. In this paper, an NMES technique based on surface electromyogram (sEMG) communication between healthy side and affected side of
Autor:
Yu-Xuan Zhou, Chen-Xi Xie, Jia Huang, Bi-Lei Wang, Zhigong Wang, Zheng-Yang Bi, Hong-Xing Wang, Hai-Peng Wang, Xiaoying Lü
Publikováno v:
Journal of Neural Engineering. 18:046028
Objective. In this study, a hybrid method combining hardware and software architecture is proposed to remove stimulation artefacts (SAs) and extract the volitional surface electromyography (sEMG) in real time during functional electrical stimulations