The field of rehabilitation and assistive robotics has developed numerous prosthetic devices for people that have lost limbs. These devices help users to recover their quality of life by allowing them to interact with and manipulate objects. Despite progress, current prosthetic devices still face many challenges to make them 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 challenges for assistive robotics. This project will investigate the development of AI- based sensing and control strategies for prosthetic hands. Specifically, the project will address:
-development of machine learning methods for the reliable understanding of multimodal signals from the human body to perform correct prosthetic actions, which will require a deep understanding of cutting edge machine learning methods.
-design of control loops that make the prosthetic respond reliably to the intention of human movements.
-verification and transparency to ensure the correct functioning and safety of the prosthetic device, which will require research into how medical devices are regulated and how the designers should or should not be held accountable for devices.
-analysis of user perception of the usability, acceptability and effectiveness of the prosthetic, in order to optimise user engagement.
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.
Engineering applications of machine learning, with a particular focus on medical devices.
MEng in Electronic and Electrical Engineering at Bath, with a final year project focusing on machine learning for the classification of post-anoxic cardiac arrest coma patient’s EEG signals.
Dr Ben Metcalfe
Dr Ben Ainsworth
Dr Dingguo Zhang
Dr Uriel Martinez Hernandez