###Haptic sensors arrays for augmenting proprioceptive feedback and enabling smart interventions
Project ID: 2228bd1033 (You will need this ID for your application)
Research Theme: Healthcare Technologies
UCL Lead department: Mechanical Engineering
Lead Supervisor: Manish K. Tiwari
Project Summary:
Importance and clinical context: Enhancing clinician’s decision making in time critical and delicate surgical/interventional procedures enables better patient outcomes. In UCL’s Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) we have been investigating a number of haptic sensing, imaging and machine learning technologies to facilitate this. For example, force/pressure sensors integrated on finger-tips and/or clinical instruments are able to augment the proprioceptive feedback and enable quantification of forces during surgeries. This can not only enable safer surgeries that rely on optimal force signatures but may also result in clinical outcomes by improving proprioceptive feedback in time critical procedures where traditional imaging tools are inaccessible. This will also have applications in telemedicine where real-time haptic feedback from a health practitioner in low resource settings could be relayed to a clinicians by combining with haptic displays and/or soft robotics technologies.
Team: Along with supervisors, you will work in a multidisciplinary environment embedded in the WEISS centre and the two lead departments. The work also has direct links with clinicians from fetal medicine in UCLH (see below)
Research to be undertaken: The project will focus on using our high-resolution printing and prototyping capabilities to realise an array of readily integrated haptic and position sensors for in situ haptic imaging which will be combined with machine learning to enable real time material and mechanical property mapping. Finger-tip mounted sensors have recently been combined to demonstrate possibilities of haptic object and/or materials detection. Examples include high-density force sensor arrays integrated on gloves or finger-tip integrated sensors exploiting machine learning. We will also seek to consider the possibilities of classifying materials and stiffness using a combination of haptic sensing and imaging technologies.
Candidate credentials: An engineering/natural sciences background will help. Interest in healthcare/manufacturing technologies with clear potential for patient benefits is most important.