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Intelligent sensing and control of a novel rehabilitation robot


   School of Computing

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Applications are invited for a self-funded, 3-year full-time or 6-year part time PhD project.

The PhD will be based in the School of Computing and will be supervised by Professor Zhaojie Ju.

The work on this project could involve:

  • To investigate effective sensing techniques to capture human motion features, such as contact or non-contact sensors, and collect real-world datasets. 
  • To design efficient machine learning algorithms to analyze and recognize the motion features/intention and dynamically link such results with the rehabilitation robot. 
  • To investigate the control models to drive the rehabilitation robot in response to the motion recognition results in real time. 

Project description

In human-robot interaction/collaboration, the robot is supposed to be able to detect, perceive and understand corresponding human motions in the environment to interact, co-operate, imitate or learn in an intelligent manner. Robot’s perception intelligence through sensory systems plays a key role to make the robot interact with human in a more natural and efficient way. This project aims to design effective and efficient methods to sense human/patient motion and recognize the intention, and to real-time control a novel rehabilitation exoskeleton robot. They are expected to timely and correctly respond to user’s motion intention to assist the daily life and rehabilitation. 

This project goes well along with and contributes to our on-going EU funded project, “AiBle” project (totalling €5 million), which brings together cutting-edge technology in artificial intelligence, virtual reality, cloud computing and exoskeleton control. This robot-enhanced rehabilitation will help stroke patients make the best recovery possible and re-learn skills for everyday life. University of Portsmouth is the lead partner of this project and will be responsible for the motion analysis and recognition using the multi-sensory information from the patients. 

Dr Zhaojie Ju, Professor in Machine Learning and Robotics, is the Principle Investigator of the "AiBle” project and leading the consortium of 9 EU partners. He has published over 200 publications in journals, book chapters, and conference proceedings, and received 5 best paper awards, 1 book award, 1 best competition paper award, and 1 best Associate Editor award. He is an Associate Editor of top journals, such as IEEE Trans. Cybernetics, IEEE Trans. Cognitive and Developmental Systems, and Neurocomputing. He has successfully supervised over 10 PhD students, who found good jobs after graduation in either academic or industrial sectors. He is the Chair of IEEE SMC Portsmouth Chapter and an IEEE Senior Member. 

General admissions criteria

You'll need a good first degree from an internationally recognised university or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

Specific candidate requirements

You will need a first or upper second class honours degree or a good Masters degree in related area (such as: Engineering and Computer Science), and be able to demonstrate good problem solving and analytical skills as well as knowledge of artificial intelligence and robotics. Evidence of programming with Matlab and/or C/C++ is essential and experience in machine learning or robotics would be a significant advantage.

How to Apply

We encourage you to contact Professor Zhaojie Ju () to discuss your interest before you apply, quoting the project code below.

When you are ready to apply, please follow the 'Apply now' link on the Computing PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process. 

When applying please quote project code:COMP5891023


Funding Notes

Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK students only).

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