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141 results on '"Fang, Yinfeng"'

3. Examining the Impact of Muscle-Electrode Distance in sEMG Based Hand Motion Recognition

Author

Shi, Jinwei, Liu, Mingchun, Fang, Yinfeng, Yu, Jiahui, Gao, Hongwei, Ju, Zhaojie, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yang, Huayong, editor, Liu, Honghai, editor, Zou, Jun, editor, Yin, Zhouping, editor, Liu, Lianqing, editor, Yang, Geng, editor, Ouyang, Xiaoping, editor, and Wang, Zhiyong, editor

Published
2023
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4. SW-YOLO: Improved YOLOv5s Algorithm for Blood Cell Detection

Author

Wu, Yonglin, Fang, Yinfeng, Gao, Dongxu, Gao, Hongwei, Ju, Zhaojie, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yang, Huayong, editor, Liu, Honghai, editor, Zou, Jun, editor, Yin, Zhouping, editor, Liu, Lianqing, editor, Yang, Geng, editor, Ouyang, Xiaoping, editor, and Wang, Zhiyong, editor

Published
2023
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9. Desktop-Sized Lithium Battery Protection Printed Circuit Board Detection System Based on Visual Feedback Manipulator

Author

Xia, Huang, Pu, Chenghong, Wang, Bangyu, Liu, Ziyang, Fang, Yinfeng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liu, Honghai, editor, Yin, Zhouping, editor, Liu, Lianqing, editor, Jiang, Li, editor, Gu, Guoying, editor, Wu, Xinyu, editor, and Ren, Weihong, editor

Published
2022
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10. Assistive Robot Design for Handwriting Skill Therapy of Children with Autism

Author

Huang, Xiansheng, Fang, Yinfeng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liu, Honghai, editor, Yin, Zhouping, editor, Liu, Lianqing, editor, Jiang, Li, editor, Gu, Guoying, editor, Wu, Xinyu, editor, and Ren, Weihong, editor

Published
2022
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18. Towards Active Muscle Pattern Analysis for Dynamic Hand Motions via sEMG

Author

Li, Jiahan, Fang, Yinfeng, Huang, Yongan, Li, Gongfa, Ju, Zhaojie, Liu, Honghai, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Lotfi, Ahmad, editor, Bouchachia, Hamid, editor, Gegov, Alexander, editor, Langensiepen, Caroline, editor, and McGinnity, Martin, editor

Published
2019
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19. Ultrasound Feature Evaluation for Robustness to Sensor Shift in Ultrasound Sensor Based Hand Motion Recognition

Author

Boyd, Peter, Fang, Yinfeng, Liu, Honghai, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Althoefer, Kaspar, editor, Konstantinova, Jelizaveta, editor, and Zhang, Ketao, editor

Published
2019
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21. Interacting with prosthetic hands via electromyography signals

Author

Fang, Yinfeng, Liu, Honghai, Stevens, Brett, and Ju, Zhaojie

Subjects
616.7, Computing
Abstract

It is a challenge to provide robust electromyographic signals or patterns for prosthetic hand systems. This thesis proposes a comprehensive methodology to address the challenge with respect to surface electromyographic signal acquisition, electrode layouts, electromyographic features and user training strategies. A multi-channel surface electromyography acquisition platform is customised to conduct researches throughout this thesis. First of all, a zig electrode layout is proposed to provide more repeatable electromyographic signals. This electrode layout is instantiated into an electrode sleeve, which is specially presented to fix the electrodes on the forearm and acquire forearm muscular activities. Our experiments prove that zig electrode layout has better electromyographic signal repeatability than conventional parallel electrode layout in different tests. Secondly, this thesis establishes a bridge connecting forearm muscles’ functions and multi-channel electromyographic signals by means of electromyographic map and magnitude-angle feature. The electromyographic map is proposed to explore how channels of electromyographic signals correspond to individual forearm muscles. In order to understand hand motion physiological principles, magnitude-angle feature is presented to identify the most active muscles during hand motions. Thirdly, to enhance patients’ ability in generating intuitive prosthetic control commands, a training strategy based on visual trajectory feedback is proposed. In the training procedure, users are able to adjust themselves according to classifier feedback. This training procedure can significantly improve patients’s ability in generating repeatable electromyographic pattern, no matter the feedback information is able or disable.

Published
2015

23. Multi-length Windowed Feature Selection for Surface EMG Based Hand Motion Recognition

Author

Zhou, Dalin, Fang, Yinfeng, Ju, Zhaojie, Liu, Honghai, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Chen, Zhiyong, editor, Mendes, Alexandre, editor, Yan, Yamin, editor, and Chen, Shifeng, editor

Published
2018
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24. A Multi-channel EMG-Driven FES Solution for Stroke Rehabilitation

Author

Zhou, Yu, Fang, Yinfeng, Zeng, Jia, Li, Kairu, Liu, Honghai, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Chen, Zhiyong, editor, Mendes, Alexandre, editor, Yan, Yamin, editor, and Chen, Shifeng, editor

Published
2018
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25. Preliminary Results of EMG-Based Hand Gestures for Long Term Use

Author

Boyd, Peter, Fang, Yinfeng, Liu, Honghai, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, YongAn, editor, Wu, Hao, editor, Liu, Honghai, editor, and Yin, Zhouping, editor

Published
2017
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27. How to Build a Supervised Autonomous System for Robot-Enhanced Therapy for Children with Autism Spectrum Disorder

Author

Esteban Pablo G., Baxter Paul, Belpaeme Tony, Billing Erik, Cai Haibin, Cao Hoang-Long, Coeckelbergh Mark, Costescu Cristina, David Daniel, De Beir Albert, Fang Yinfeng, Ju Zhaojie, Kennedy James, Liu Honghai, Mazel Alexandre, Pandey Amit, Richardson Kathleen, Senft Emmanuel, Thill Serge, Van de Perre Greet, Vanderborght Bram, Vernon David, Yu Hui, and Ziemke Tom

Subjects
robot-enhanced therapy, autism spectrum disorders, supervised autonomy, multi-sensory data, cognitive controller, Technology
Abstract

Robot-Assisted Therapy (RAT) has successfully been used to improve social skills in children with autism spectrum disorders (ASD) through remote control of the robot in so-called Wizard of Oz (WoZ) paradigms.However, there is a need to increase the autonomy of the robot both to lighten the burden on human therapists (who have to remain in control and, importantly, supervise the robot) and to provide a consistent therapeutic experience. This paper seeks to provide insight into increasing the autonomy level of social robots in therapy to move beyond WoZ. With the final aim of improved human-human social interaction for the children, this multidisciplinary research seeks to facilitate the use of social robots as tools in clinical situations by addressing the challenge of increasing robot autonomy.We introduce the clinical framework in which the developments are tested, alongside initial data obtained from patients in a first phase of the project using a WoZ set-up mimicking the targeted supervised-autonomy behaviour. We further describe the implemented system architecture capable of providing the robot with supervised autonomy.

Published
2017
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34. Development of a Surface EMG Acquisition System with Novel Electrodes Configuration and Signal Representation

Author

Fang, Yinfeng, Zhu, Xiangyang, Liu, Honghai, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Lee, Jangmyung, editor, Lee, Min Cheol, editor, Liu, Honghai, editor, and Ryu, Jee-Hwan, editor

Published
2013
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35. Deep Convolutional Generative Adversarial Network-Based EMG Data Enhancement for Hand Motion Classification

Author

Chen, Zihan, Qian, Yaojia, Wang, Yuxi, and Fang, Yinfeng

Subjects
Histology, Biomedical Engineering, Bioengineering, Biotechnology
Abstract

The acquisition of bio-signal from the human body requires a strict experimental setup and ethical approvements, which leads to limited data for the training of classifiers in the era of big data. It will change the situation if synthetic data can be generated based on real data. This article proposes such a kind of multiple channel electromyography (EMG) data enhancement method using a deep convolutional generative adversarial network (DCGAN). The generation procedure is as follows: First, the multiple channels of EMG signals within sliding windows are converted to grayscale images through matrix transformation, normalization, and histogram equalization. Second, the grayscale images of each class are used to train DCGAN so that synthetic grayscale images of each class can be generated with the input of random noises. To evaluate whether the synthetic data own the similarity and diversity with the real data, the classification accuracy index is adopted in this article. A public EMG dataset (that is, ISR Myo-I) for hand motion recognition is used to prove the usability of the proposed method. The experimental results show that adding synthetic data to the training data has little effect on the classification performance, indicating the similarity between real data and synthetic data. Moreover, it is also noted that the average accuracy (five classes) is slightly increased by 1%–2% for support vector machine (SVM) and random forest (RF), respectively, with additional synthetic data for training. Although the improvement is not statistically significant, it implies that the generated data by DCGAN own its new characteristics, and it is possible to enrich the diversity of the training dataset. In addition, cross-validation analysis shows that the synthetic samples have large inter-class distance, reflected by higher cross-validation accuracy of pure synthetic sample classification. Furthermore, this article also demonstrates that histogram equalization can significantly improve the performance of EMG-based hand motion recognition.

Published
2022

36. The Mathematical Model and Control Scheme of a Four-Legged Robot Based on GZ-I and Note Module

Author

Fang, Yinfeng, Zhang, Houxiang, Li, Xiuli, Chen, S. Y., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Liu, Honghai, editor, Ding, Han, editor, Xiong, Zhenhua, editor, and Zhu, Xiangyang, editor

Published
2010
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38. Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture Recognition

Author

Shenyilang Zhang, Xuguang Zhang, Guangtai Lei, Fang Yinfeng, and Wang Yuxi

Subjects
0209 industrial biotechnology, Computer science, 0206 medical engineering, force myography, 02 engineering and technology, TP1-1185, Biochemistry, Signal, Analytical Chemistry, sampling frequency, Reduction (complexity), 020901 industrial engineering & automation, Sampling (signal processing), Position (vector), Humans, Computer vision, Electrical and Electronic Engineering, channel number, Instrumentation, Mechanical Phenomena, Gestures, Electromyography, business.industry, gesture recognition, Chemical technology, Communication, Myography, Hand, 020601 biomedical engineering, Pressure sensor, Atomic and Molecular Physics, and Optics, Gesture recognition, Artificial intelligence, business, Muscle Contraction, Communication channel, Gesture
Abstract

Force myography (FMG) is a method that uses pressure sensors to measure muscle contraction indirectly. Compared with the conventional approach utilizing myoelectric signals in hand gesture recognition, it is a valuable substitute. To achieve the aim of gesture recognition at minimum cost, it is necessary to study the minimum sampling frequency and the minimal number of channels. For purpose of investigating the effect of sampling frequency and the number of channels on the accuracy of gesture recognition, a hardware system that has 16 channels has been designed for capturing forearm FMG signals with a maximum sampling frequency of 1 kHz. Using this acquisition equipment, a force myography database containing 10 subjects’ data has been created. In this paper, gesture accuracies under different sampling frequencies and channel’s number are obtained. Under 1 kHz sampling rate and 16 channels, four of five tested classifiers reach an accuracy up to about 99%. Other experimental results indicate that: (1) the sampling frequency of the FMG signal can be as low as 5 Hz for the recognition of static movements; (2) the reduction of channel number has a large impact on the accuracy, and the suggested channel number for gesture recognition is eight; and (3) the distribution of the sensors on the forearm would affect the recognition accuracy, and it is possible to improve the accuracy via optimizing the sensor position.

Published
2021

50. Detection of salient crowd motion based on repulsive force network and direction entropy

Author

Juan Zheng, Xianghong Tang, Xuguang Zhang, Fang Yinfeng, Hui Yu, and Dujun Lin

Subjects
node strength, Computer science, Feature matrix, General Physics and Astronomy, lcsh:Astrophysics, 02 engineering and technology, 01 natural sciences, Instability, Article, salient crowd motion detection, 010305 fluids & plasmas, lcsh:QB460-466, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, crowd behavior analysis, Entropy (information theory), Statistical physics, repulsive force, lcsh:Science, Feature parameter, Motion detection, lcsh:QC1-999, direction entropy, Salient, lcsh:Q, 020201 artificial intelligence & image processing, Weighted network, Vector field, lcsh:Physics
Abstract

This paper proposes a method for salient crowd motion detection based on direction entropy and a repulsive force network. This work focuses on how to effectively detect salient regions in crowd movement through calculating the crowd vector field and constructing the weighted network using the repulsive force. The interaction force between two particles calculated by the repulsive force formula is used to determine the relationship between these two particles. The network node strength is used as a feature parameter to construct a two-dimensional feature matrix. Furthermore, the entropy of the velocity vector direction is calculated to describe the instability of the crowd movement. Finally, the feature matrix of the repulsive force network and direction entropy are integrated together to detect the salient crowd motion. Experimental results and comparison show that the proposed method can efficiently detect the salient crowd motion.

Published
2019

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