Start date: October 2020
Duration: 3 years (full time)
Location: Colchester Campus
Based in: School of Computer Science and Electronic Engineering (in collaboration with School of Sport, Rehabilitation and Exercise Sciences)
This interdisciplinary studentship brings together the expertise of AI and machine learning with rehabilitation science also leveraging practical support from the Paediatric Physiotherapy Unit, Anglian Community Enterprise Community Interest Company, which could in turn enable a significant scientific impact potential for this timely research endeavour.
Therefore, we are looking for a highly motivated and interdisciplinary-minded student, who has an excellent computer science, electronics/biomedical/mechatronics engineering or other related UG/MSc degree and is keen to work on relevant research in the area of machine learning algorithms for rehabilitation of children with disability. More details about this project can be found as follows.
The project is to develop a portable, marker-less motion capture system consisting of accelerometers and a video camera, and a gait assessment method using deep learning and data fusion techniques to automatically quantify the degree of walking impairment in children with Cerebral Palsy (CP).
The validity and reliability assessment. Walking biomechanics in children with/without CP will be captured using the developed system and the sensors’ data will be processed using deep learning to derive Lower limb kinematics.
Multivariate statistics will be used for dimension reduction of the high-dimensional kinematics.
A metric of impairment between the components loading of individuals with/without CP (e.g. Euclidean distance) will be calculated and compared for both methods. This will justify if walking impairment quantified using the new method is comparable to 3DGA.
The new gait analysis method will be used in children with CP to quantify the magnitude of walking improvement before and after intervention (e.g. orthosis), and this will be correlated with routine clinical outcome measures (e.g. subjective reports of improvement, walking speed).
A low-cost, accurate and automatic gait analysis platform will better enable clinicians to objectively quantify impairments, monitor improvements, and individualising treatment decision making.
How to apply
You can apply for this postgraduate research opportunity online (https://www1.essex.ac.uk/pgapply/login.aspx
Please include your CV, covering letter, personal statement, and transcripts of UG and Masters degrees in your application.
The University has moved to requiring only one reference for PhD applications and these can be received after a conditional offer has been made so the absence of these will not hold up the recruitment process.
Find out more about this studentship and information on how to apply on our website (https://www.essex.ac.uk/postgraduate-research-degrees/opportunities/supervised-At-Home-Gait-Assessment-for-children-with-Cerebral-Palsy