The field of rehabilitation and assistive robotics has developed numerous upper and lower limb prosthetic devices for people that have lost their limbs. The purpose of these devices is to help users to recover their quality of life by allowing them to interact and manipulate objects in their environment, and perform daily living activities.
Despite the progress achieved in this field, current prosthetic devices still face many challenges to make these devices intelligent, safe, reliable and acceptable for users. Fortunately, significant advances have been observed in recent years in wearable and sensor technology, soft materials, control and machine learning methods. Together, these aspects offer a large repertoire of opportunities to address the current challenges for assistive robotics.
This project will investigate the development of artificial intelligence based sensing and control strategies for prosthetic hands. Specifically, the research and development undertaken in this project will address the following challenges:
• The development of machine learning methods for the reliable understanding of multimodal signals from the human body to perform the correct prosthetic actions.
• The design of control loops that make the prosthetic respond reliably to the intention of human movements.
• The verification and transparency to ensure the correct functioning and safety of the prosthetic device.
• An analysis of user perception of the usability, acceptability and effectiveness of the prosthetic, in order to optimise user engagement.
This project has a strong multidisciplinary nature, with a supervisory team from both engineering and psychology. The student is expected to collaborate with partners from areas of computer science, psychology, electronic and electrical engineering, and mechanical engineering. Furthermore, the student is expected to attend multiple events such as conferences, project meetings, summer schools and workshops.
This research project will be carried out as part of an interdisciplinary integrated PhD in the UKRI Centre for Doctoral Training in Accountable, Responsible and Transparent AI (ART-AI). This project is aligned with the topic focus of ART-AI in three key ways.
Firstly, the development of the machine learning methods for interpreting multi-modal sensor data from the human will require a deep understanding of cutting edge machine learning methods.
Secondly, the human machine interface formed by the prosthetic will require research into how the prosthetic is perceived and accepted by the human as an artificial limb. The project will apply novel psychological theory to identify specific barriers and facilitators (for example, using a cognitive psychology framework to determine whether users view prosthetics as effective, necessary and tolerable). The ‘Person-Based Approach’ will then be used to develop targeted behavioural support to overcome these barriers. This user-centred approach will ensure that users perceive the prosthetic as acceptable as possible, in order to maximise their engagement with the device.
Thirdly, the safety and verification of the prosthetic (including the machine learning sensing) will require research into how medical devices are regulated and how the designers should or should not be held accountable for the devices.
Students will be fully funded for 4 years (stipend, UK/EU tuition fees and research support budget). Further details can be found at: : http://www.bath.ac.uk/centres-for-doctoral-training/ukri-centre-for-doctoral-training-in-accountable-responsible-and-transparent-ai/
Desirable qualities in candidates include intellectual curiosity, a strong background in maths and programming experience.
Candidates are expected to have or be near completion of an MSc or MEng in Electronics, Robotics, Mechanics, Computer Science, Mathematics, Physics or related areas.
Informal enquiries about the project should be directed to Dr Ben Metcalfe: [email protected]
Enquiries about the application process should be sent to [email protected]
Formal applications should be made via the University of Bath’s online application form: https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUCM-FP02&code2=0002
Start date: 28 September 2020.