PhD in Tactile Biomechanics and Neuroscience: How Do Humans Feel Friction?
Background: Prosthetic and robotic hands demonstrate poor dexterity during object manipulation, often dropping objects. Humans rarely allow objects to slip because we can sense if an object is slippery and adjust our grip. In recent years, while we have learned more about the biomechanics and neuroscience underpinning our ability to sense friction, there is still much to learn. Perhaps unsurprisingly, given how poorly we understand human friction sensing, very little research has been directed at replicating this ability to sense friction or slipperiness in artificial sensors. This research program has three work packages: (WP1) To advance our understanding of how humans sense friction; (WP2) To demonstrate, using a number of friction-based tactile sensor prototypes currently under development by our research group, that friction sensing leads to improved dexterity in robotic manipulation;
(WP3) To use advanced design, manufacturing, and instrumentation methods to miniaturise the proposed sensors to a scale similar to a human finger pad. The outcomes of this research, which would endow artificial hands with the ability to feel the slipperiness and/or impending loss of grip of a grasped object, could significantly advance the fields of prosthetics, telesurgery, and service, agricultural, and manufacturing robotics.
PhD scope: The candidate will work on WP1 of this project, performing biomechanical and microneurographic studies of the human sense of touch to discover the mechanisms by which frictional information about the contact interface between the finger pad and a manipulated object is transduced by the skin of the finger pad, and subsequently encoded and signalled to the brain by tactile afferents. The biomechanical studies will involve video processing of relative movement between the skin and the object surface, and this movement (and associated forces) related to friction. Microneurography, using microelectrodes placed in the median nerve at the level of the wrist, will be used to record the responses of single tactile afferents in response to localised biomechanical events, which in turn are influenced by frictional properties – this demonstrates whether friction-related biomechanical events (such as localised slips) can be detected by our mechanoreceptors. Robotic actuators will be used (with support from additional technical and research staff) to manipulate the skin of the finger pad. Advance statistical methods, including machine learning techniques, will be used to decode the ensemble of neural recordings, highlighting which biomechanical events are important to our sensation of friction.
Supervision and research environment: The candidate will be supervised by A/Prof Stephen Redmond (School of Electrical and Electronic Engineering: https://www.ucd.ie/eleceng/) and Prof James Jones (School of Medicine: https://www.ucd.ie/medicine/). The candidate will also spend time training with one of our international collaborators to develop skills in the technique of microneurography. Neurological recordings will be performed in Dublin under the guidance of Prof Jones, and in collaboration with Neurology Research Group at St Vincent’s University Hospital, Dublin. The candidate will interact with the other work packages or the project. The candidate will also have the opportunity to contribute to the SFI Insight Centre for Data Analytics (https://www.insight-centre.org/) based at UCD.
Funding: A stipend of €18,500 per annum plus tuition fee is available for a maximum of four years. The project is generously funded by Science Foundation Ireland’s President of Ireland Future Research Leaders Award, held by A/Prof Redmond, which includes an extensive budget for laboratory apparatus, consumables, and travel. The tactile sensor design work packages are also partly supported by US Office of Naval Research Global funding held by A/Prof Redmond and Dr Heba Khamis at UNSW (Sydney, Australia).
Academic requirements: The minimum academic qualification is a first- or upper-second-class honours degree (or an equivalent international degree) in electrical engineering, software engineering, biomedical engineering, neuroscience, neurophysiology, or a sufficiently related field. Software programming skills are essential.
Contact: Please send the following documents to Stephen Redmond (firstname.lastname@example.org):
(1) Cover letter;
(2) Curriculum vitae;
(3) All academic transcripts;
(4) Currently valid IELTS, TOEFL or other English language qualification.