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Professor Honghai Liu

Professor of Human Machine Systems

Honghai Liu
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Biography

I recieved a PhD from King's College, University of London. I am the Chair Professor in Human Machine Systems at the University of Portsmouth. I previously worked in industry on large-scale industrial control and system integration projects, and held appointments at the University of London and University of Aberdeen.

My research focuses on motion sensing and understanding and its applications to human machine systems, particularly those approaches which could make contributions to the intelligent connection of perception to action. I truly appreciate financial support from the Royal Society, EPSRC, EU 7th Framework Programme, SEEDA, Japan Society for the Promotion of Science and the British Council, as well as national and international industrial and academic partners.

Research Interests

Sensing Techniques for Monitoring Morphological Changes of Forearm Muscles, 2012-2013

This project is to develop novel sensing prototypes and techniques to dynamically monitoring morphological changes of forearm muscles for controlling powered prostheses. The project outcome will be compared with EMG-based sensing techniques and be implemented to control i-Limb hand. The project is funded by HEIF5.

Exploring Human Hand Capabilities into Multifingered Robot Manipulation, 2007-2013

The proposed work aims to investigate artificial intelligence (AI) methodologies and practical solutions which will allow robotic hands to automatically adapt to human environments and thus to enable them to autonomously perform useful manipulation tasks involved in daily living, potentially for health care and rehabilitation applications. The project is funded by EPSRC and The Royal Society.

Sea Border Surveillance, 2010-2012

The SeaBILLA project aims to define the architecture for cost-effective European sea border surveillance systems, integrating space, land, sea and air assets, including legacy systems, apply advanced technological solutions to increase performances of surveillance functions, develop and demonstrate significant improvements in detection, tracking, identification and automated behavior analysis of all vessels, including hard to detect vessels, in open waters as well as close to coast. The project is funded by Call FP7-SEC-2009-1 Security Call.

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