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Xinpu Chen
Shanghai Jiao Tong University
7Publications
4H-index
149Citations
Publications 7
Newest
#1Xinpu Chen (SJTU: Shanghai Jiao Tong University)H-Index: 4
#2Dingguo Zhang (SJTU: Shanghai Jiao Tong University)H-Index: 17
Last.Xiangyang Zhu (SJTU: Shanghai Jiao Tong University)H-Index: 21
view all 3 authors...
Background The nonstationary property of electromyography (EMG) signals usually makes the pattern recognition (PR) based methods ineffective after some time in practical application for multinational prosthesis. The conventional EMG PR, which is accomplished in two separate steps: training and testing, ignores the mismatch between training and testing conditions and often discards the useful information in testing dataset.
Dec 6, 2011 in ICIRA (International Conference on Intelligent Robotics and Applications)
#1Dingguo Zhang (SJTU: Shanghai Jiao Tong University)H-Index: 17
#2Ying Wang (SJTU: Shanghai Jiao Tong University)H-Index: 1
Last.Fei Xu (SJTU: Shanghai Jiao Tong University)H-Index: 1
view all 4 authors...
This paper proposes a functional electrical stimulation (FES) system based on electromyogram (EMG) classification, which aims to serve for the hemiplegia or incomplete paralyzed patients. This is a hierarchical system and the controller contains three levels. This work focuses on EMG signal processing in order to get the motion intention. Autoregressive (AR) feature, time domain statistics (TDS), and discriminant fourier feature (FC) are adopted as the EMG features. Linear discriminant analysis ...
May 1, 2011 in ICRA (International Conference on Robotics and Automation)
#1Dingguo Zhang (SJTU: Shanghai Jiao Tong University)H-Index: 17
#2Xinpu Chen (SJTU: Shanghai Jiao Tong University)H-Index: 4
Last.Xiangyang Zhu (SJTU: Shanghai Jiao Tong University)H-Index: 21
view all 5 authors...
This paper presents our new progress on research of electromyography (EMG) controlled prosthetic hand. Preliminary clinical study is conducted on amputees. EMG data from the residual muscles of three amputees are used to evaluate the new features, discriminant bispectra (DBS) and discriminant Fourier cepstrum (DFC). The performance is also compared with other traditional features, autoregressive (AR) coefficient, time domain statics (TDS), and power spectral distribution (PSD). Some promising re...
Nov 10, 2010 in ICIRA (International Conference on Intelligent Robotics and Applications)
#1Pinghua Hu (SJTU: Shanghai Jiao Tong University)H-Index: 1
#2Shunchong Li (SJTU: Shanghai Jiao Tong University)H-Index: 3
Last.Xiangyang Zhu (SJTU: Shanghai Jiao Tong University)H-Index: 21
view all 5 authors...
This paper applies real time pattern recognition into the control of robotic hand with surface electromyographic (sEMG) signal. We focus on the hardware system design and the control strategy implementation. Time domain statistic methods are employed to extract the features, which have good effects on the pattern recognition. After the feature dimension reduction by Fisher linear discriminant (FLD), the feature vector is classified by a multi-layer perception (MLP) neural network. At last the da...
#1Xinpu Chen (SJTU: Shanghai Jiao Tong University)H-Index: 4
#2Xiangyang Zhu (SJTU: Shanghai Jiao Tong University)H-Index: 21
Last.Dingguo Zhang (SJTU: Shanghai Jiao Tong University)H-Index: 17
view all 3 authors...
Abstract This paper presents a discriminant bispectrum (DBS) feature extraction approach to surface electromyogram (sEMG) signal classification for prosthetic control. The proposed feature extraction method involves two steps: (1) the bispectrum matrix integration, and (2) the Fisher linear discriminant (FLD) projection. We compare DBS with other conventional features, such as autoregressive coefficients, root mean square, power spectral distribution and time domain statistics. First, the separa...
#1Xinpu Chen (SJTU: Shanghai Jiao Tong University)H-Index: 4
#2Xiangyang Zhu (SJTU: Shanghai Jiao Tong University)H-Index: 21
Last.Dingguo Zhang (SJTU: Shanghai Jiao Tong University)H-Index: 17
view all 3 authors...
Myoelectrical pattern classification is a crucial part in multi-functional prosthesis control. This paper investigates a discriminant Fourier-derived cepstrum (DFC) and feature-level post-processing (FLPP) to discriminate hand and wrist motions using the surface electromyographic signal. The Fourier-derived cepstrum takes advantage of the Fourier magnitude or sub-band power energy of signals directly and provides flexible use of spectral information changing with different motions. Appropriate c...
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