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144 results on '"Chen, Dongyi"'

5. Study on Static Biomechanical Model of Whole Body Based on Virtual Human.

Author

Cheng, Zheng, Luo, Bin, Chen, Chuan, Guo, Huajun, Wu, Jiaju, and Chen, Dongyi

Abstract

Material handling tasks often lead to skeletal injury of workers. The whole-body static biomechanical modeling method based on virtual humans is the theoretical basis for analyzing the human factor index in the lifting process. This paper focuses on the study of humans' body static biomechanical model for virtual human ergonomics analysis: First, the whole-body static biomechanical model is constructed, which calculates the biomechanical data such as force and moment, average strength, and maximum hand load at human joints. Secondly, the prototype model test system is developed, and the real experiment environment is set up with the inertial motion capture system. Finally, the model reliability verification experiment and application simulation experiment are designed. The comparison results with the industrial ergonomic software show that the model is consistent with the output of the industrial ergonomic software, which proves the reliability of the model. The simulation results show that under the same load, the maximum joint load and the maximum hand load are strongly related to the working posture, and the working posture should be adjusted to adapt to the load. Upright or bent legs have less influence on the maximum load capacity of the hand. Lower hand load capacity is due to forearm extension, and the upper arm extension greatly reduces the load capacity of the hand. Compared with a one-handed load, the two-handed load has a greater load capacity. [ABSTRACT FROM AUTHOR]

Published
2024
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9. A Piezoelectric Heart Sound Sensor for Wearable Healthcare Monitoring Devices

Author

Chen, Zhenghao, Chen, Dongyi, Xue, Liuhui, Chen, Liang, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Mucchi, Lorenzo, editor, Hämäläinen, Matti, editor, Jayousi, Sara, editor, and Morosi, Simone, editor

Published
2019
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16. Smart Textiles and Smart Personnel Protective Equipment

Author

Chen, Dongyi, Lawo, Michael, Barbosa, Simone, Editorial board, Carroll, John M., Editorial board, Cockton, Gilbert, Editorial board, Jacob, Robert J. K., Editorial board, Murray, Dianne, Editorial board, Palanque, Philippe, Editorial board, Pastor, Óscar, Editorial board, Balakrishnan, Ravin, Editorial board, Zhai, Shumin, Editorial board, Bernhaupt, Regina, Editorial board, Cheok, Adrian David, Editorial board, Duh, Henry Been-Lirn, Editorial board, Markopoulos, Panos, Editorial board, Myers, Brad A, Editorial board, Plimmer, Beryl, Editorial board, Pribeanu, Costin, Editorial board, Wesson, Janet, Editorial board, Calvary, Gaëlle, Editorial board, Cao, Xiang, Editorial board, Gonzalez Calleros, Juan, Editorial board, Feiner, Steven, Editorial board, Vetere, Frank, Editorial board, Schnädelbach, Holger, Editorial board, Schneegass, Stefan, editor, and Amft, Oliver, editor

Published
2017
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22. A Wearable Head Mounted Display Bio-Signals Pad System for Emotion Recognition

Author

Wan, Chunting, Chen, Dongyi, Huang, Zhiqi, and Luo, Xi

Subjects
Chemical technology, Emotions, bio-signals, Electroencephalography, TP1-1185, Biochemistry, Article, Atomic and Molecular Physics, and Optics, Analytical Chemistry, HCI interface, User-Computer Interface, emotion recognition, Head-Mounted Displays, virtual reality, Humans, Smart Glasses, Electrical and Electronic Engineering, Instrumentation
Abstract

Multimodal bio-signals acquisition based on wearable devices and using virtual reality (VR) as stimulus source are promising techniques in emotion recognition research field. Numerous studies have shown that emotional states can be better evoked through Immersive Virtual Environments (IVE). The main goal of this paper is to provide researchers with a system for emotion recognition in VR environments. In this paper, we present a wearable forehead bio-signals acquisition pad which is attached to Head-Mounted Displays (HMD), termed HMD Bio Pad. This system can simultaneously record emotion-related two-channel electroencephalography (EEG), one-channel electrodermal activity (EDA), photoplethysmograph (PPG) and skin temperature (SKT) signals. In addition, we develop a human-computer interaction (HCI) interface which researchers can carry out emotion recognition research using VR HMD as stimulus presentation device. To evaluate the performance of the proposed system, we conducted different experiments to validate the multimodal bio-signals quality, respectively. To validate EEG signal, we have assessed the performance in terms of EEG eyes-blink task and eyes-open and eyes-closed task. The EEG eyes-blink task indicates that the proposed system can achieve comparable EEG signal quality in comparison to the dedicated bio-signals measuring device. The eyes-open and eyes-closed task proves that the proposed system can efficiently record alpha rhythm. Then we used signal-to-noise ratio (SNR) and Skin Conductance Reaction (SCR) signal to validate the performance for EDA acquisition system. A filtered EDA signal, with a high mean SNR of 28.52 dB, is plotted on HCI interface. Moreover, the SCR signal related to stimulus response can be correctly extracted from EDA signal. The SKT acquisition system has been validated effectively by the temperature change experiment when subjects are in unpleasant emotion. The pulse rate (PR) estimated from PPG signal achieved the low mean average absolute error (AAE), which is 1.12 beats per minute (BPM) over 8 recordings. In summary, the proposed HMD Bio Pad offers a portable, comfortable and easy-to-wear device for recording bio-signals. The proposed system could contribute to emotion recognition research in VR environments.

Published
2022

23. A Simple Integration of Social Relationship and Text Data for Identifying Potential Customers in Microblogging

Author

Pang, Guansong, Jiang, Shengyi, Chen, Dongyi, 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, Motoda, Hiroshi, editor, Wu, Zhaohui, editor, Cao, Longbing, editor, Zaiane, Osmar, editor, Yao, Min, editor, and Wang, Wei, editor

Published
2013
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26. CEEMDAN-IMFx-PCA-CICA: an improved single-channel blind source separation in multimedia environment for motion artifact reduction in ambulatory ECG.

Author

Xiong, Fan and Chen, Dongyi

Subjects
BLIND source separation, HILBERT-Huang transform, AMBULATORY electrocardiography, WEARABLE technology, SIGNAL processing, VENTRICULAR arrhythmia, MOTION, HEART diseases
Abstract

Long-term monitoring of ECG via wearable monitoring systems has already been widely adopted to detect and prevent heart diseases. However, one of the main issues faced by wearable ECG monitoring systems is that motion artifacts significantly affect the systems' stability and reliability. Therefore, motion artifact reduction is a very challenging task in filtering and processing physiological signals. Based on the existing algorithms and ECG prior knowledge, in this paper, we propose an algorithm, CEEMDAN-IMFx-PCA-CICA, for motion artifact reduction in ambulatory ECG signals using single-channel blind source separation technique. Our algorithm first utilizes CEEMDAN to decompose the mixed signals into IMFs (intrinsic mode function) containing different source signal features, thereby forming new multi-dimensional signals. Using the correlation between IMFx (IMF component with the most ECG features) and each IMF, and PCA are then applied to reduce the dimension of each IMF. Finally, the blind separation of the source ECG signals is achieved by using CICA with IMFx as the constraint reference component. The results of our experiments indicate that our algorithm outperformed CEEMDAN-CICA, CEEMDAN-PCA-CICA, and improved CEEMDAN-PCA-CICA. Besides, the number of iterations of the CICA is significantly reduced; the separated source signal is better; the obtained result is stable. Furthermore, the separated ECG signal has a higher correlation with the source ECG signal and a lower RRMSE, especially in the case of high noise-to-signal ratios. [ABSTRACT FROM AUTHOR]

Published
2023
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27. A Wearable Learning and Support System for Manufacture Application

Author

Xiahou, Shiji, Chen, Dongyi, Huang, Zhiqi, 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, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Pan, Zhigeng, editor, Aylett, Ruth, editor, Diener, Holger, editor, Jin, Xiaogang, editor, Göbel, Stefan, editor, and Li, Li, editor

Published
2006
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28. An Augmented Reality-Based Application for Equipment Maintenance

Author

Ke, Changzhi, Kang, Bo, Chen, Dongyi, Li, Xinyu, 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, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Tao, Jianhua, editor, Tan, Tieniu, editor, and Picard, Rosalind W., editor

Published
2005
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29. Human Vibration Environment of Wearable Computer

Author

Huang, Zhiqi, Chen, Dongyi, Xiahou, Shiji, 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, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Tao, Jianhua, editor, Tan, Tieniu, editor, and Picard, Rosalind W., editor

Published
2005
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47. Design of fractional-order iterative learning controller for cross-coupled contour motion systems

Author

Wang Limei and Chen Dongyi

Subjects
0209 industrial biotechnology, Computer science, 020208 electrical & electronic engineering, Iterative learning control, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Linear motor, Differential operator, Fractional calculus, 020901 industrial engineering & automation, Integer, Control theory, 0202 electrical engineering, electronic engineering, information engineering, Algorithm
Abstract

For the contouring errors produced by repetitive tracking motion of single-axis linear motor on XY platform, a combined method of fractional iterative learning and cross coupled is proposed. Firstly, a positional controller of the fractional PD-type iterative learning is designed, and the integer differential operator is replaced by the differential operator defined by Grunwald-Letnikov fractional calculus to improve the traditional integer order iterative learning law. The convergence condition of the fractional iterative learning controller is given. Then, the cross-coupled control is adopted between two axes, and the contour error is estimated by using the real-time contour error estimation model to reduce the contour error. The compensation amount of X and Y axis is assigned according to the relationship of cross-coupled gain, and the real-time compensation. The simulation shows that compared with integral iterative learning, fractional iterative learning has faster convergent speed and higher accuracy of contouring process. Fractional iterative learning is an effective way to improve the contour error.

Published
2018

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