RoboPatient - Robot assisted learning of constrained haptic information gain
Lead Research Organisation:
University of Oxford
Department Name: Primary Care Health Sciences
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Organisations
Publications
Hamid E
(2021)
A State-Dependent Damping Method to Reduce Collision Force and Its Variability
in IEEE Robotics and Automation Letters
Leong F
(2022)
A Surrogate Model Based on a Finite Element Model of Abdomen for Real-Time Visualisation of Tissue Stress during Physical Examination Training.
in Bioengineering (Basel, Switzerland)
He L
(2021)
An Abdominal Phantom With Tunable Stiffness Nodules and Force Sensing Capability for Palpation Training
in IEEE Transactions on Robotics
Mayor N
(2022)
Developing a Long COVID Phenotype for Postacute COVID-19 in a National Primary Care Sentinel Cohort: Observational Retrospective Database Analysis.
in JMIR public health and surveillance
Lalitharatne T
(2020)
Facial Expression Rendering in Medical Training Simulators: Current Status and Future Directions
in IEEE Access
Zhou AY
(2022)
Investigating the links between diagnostic uncertainty, emotional exhaustion, and turnover intention in General Practitioners working in the United Kingdom.
in Frontiers in psychiatry
Lalitharatne T
(2021)
MorphFace: A Hybrid Morphable Face for a Robopatient
in IEEE Robotics and Automation Letters
Lalitharatne T
(2021)
MorphFace: A Hybrid Morphable Face for a Robopatient
Mazzolai B
(2022)
Roadmap on soft robotics: multifunctionality, adaptability and growth without borders
in Multifunctional Materials
He L
(2023)
Robotic Simulators for Tissue Examination Training With Multimodal Sensory Feedback.
in IEEE reviews in biomedical engineering
Yu Z
(2023)
Tapered whisker reservoir computing for real-time terrain identification-based navigation.
in Scientific reports
| Description | We have quantified much of the process of a clinician physically examining the abdomen. We have started to construct unique abdominal phantoms to teach health care staff. |
| Exploitation Route | Simulations that will improve teaching healthcare staff and possibly facilitate remote examination of patients. |
| Sectors | Digital/Communication/Information Technologies (including Software) Education Healthcare |
| URL | https://thrish.org/projects/robopatient |
