Prof. Huosheng Hu
School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, UK
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Academic Career Length
37Years (1990-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 October 2007 in Biomedical Signal Processing and Control
Journal Article
MOMohammadreza Asghari Oskoei
Mohammadreza Asghari OskoeiHuosheng Hu5 March 2008 in IEEE Transactions on Biomedical Engineering
Journal Article
MOMohammadreza Asghari Oskoei
Mohammadreza Asghari OskoeiHuosheng HuThis paper proposes and evaluates the application of support vector machine (SVM) to classify upper limb motions using myoelectric signals. It explores the optimum configuration of SVM-based myoelectric control, by suggesting an advantageous data segmentation technique, feature set, model selection approach for SVM, and postprocessing methods. This work presents a method to adjust SVM parameters before classification, and examines overlapped segmentation and majority voting as two techniques to improve controller performance. A SVM, as the core of classification in myoelectric control, is compared with two commonly used classifiers: linear discriminant analysis (LDA) and multilayer perceptron (MLP) neural networks. It demonstrates exceptional accuracy, robust performance, and low computational load. The entropy of the output of the classifier is also examined as an online index to evaluate the correctness of classification; this can be used by online training for long-term myoelectric control operations.
1 January 2008 in Biomedical Signal Processing and Control
Journal Article
HZHuiyu Zhou
Huiyu ZhouHuosheng Hu1 March 2010 in Journal of Bionic Engineering
Journal Article
JLJindong Liu
Jindong Liu
Huosheng HuJournal Article
ZZZuyuan Zhu
Zuyuan ZhuHuosheng HuLearning from demonstration (LfD) has been used to help robots to implement manipulation tasks autonomously, in particular, to learn manipulation behaviors from observing the motion executed by human demonstrators. This paper reviews recent research and development in the field of LfD. The main focus is placed on how to demonstrate the example behaviors to the robot in assembly operations, and how to extract the manipulation features for robot learning and generating imitative behaviors. Diverse metrics are analyzed to evaluate the performance of robot imitation learning. Specifically, the application of LfD in robotic assembly is a focal point in this paper.
9 December 2008 in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
Journal Article
Nicola BellottoHuosheng HuOne of fundamental issues for service robots is human-robot interaction. In order to perform such a task and provide the desired services, these robots need to detect and track people in the surroundings. In this paper, we propose a solution for human tracking with a mobile robot that implements multisensor data fusion techniques. The system utilizes a new algorithm for laser-based leg detection using the onboard laser range finder (LRF). The approach is based on the recognition of typical leg patterns extracted from laser scans, which are shown to also be very discriminative in cluttered environments. These patterns can be used to localize both static and walking persons, even when the robot moves. Furthermore, faces are detected using the robot's camera, and the information is fused to the legs' position using a sequential implementation of unscented Kalman filter. The proposed solution is feasible for service robots with a similar device configuration and has been successfully implemented on two different mobile platforms. Several experiments illustrate the effectiveness of our approach, showing that robust human tracking can be performed within complex indoor environments.
About Me
Huosheng Hu received his MSc degree in Industrial Automation from Central South University, China, and his PhD degree in Robotics from the University of Oxford, U.K. After conducting robotics research at Oxford for 9 years, he joined Essex University as Senior Lectures in 1998, and then Reader in 2001 and Professor in 2002. He is an Emeritus Professor in the School of Computer Science & Electronic Engineering at the University of Essex, United Kingdom since May 2025. His research interests span artificial intelligence, autonomous robots, human–robot interaction, embedded systems, mechatronics, data fusion, learning algorithms, and industrial automation. He has published more than 600 papers in journals, books, and international conferences, earning several Best Paper Awards. His publications have been cited over 22,712 times, with an H-index of 71 (Google Scholar), and he has been recognized among the world’s top 2% most cited scholars (2020–2025). Prof. Hu is a Fellow of the Institute of Engineering & Technology, a Fellow of the Institute of Measurement & Control, a Life Senior Member of IEEE, and a founding member of the IEEE Robotics & Automation Society Technical Committee on Networked Robots. He has served as Program Chair and as a member of Advisory and Organizing Committees for numerous international conferences, including IEEE IROS, ICRA, ICMA, and ROBIO. He is currently Executive Editor of the International Journal of Mechatronics and Automation and has previously