Background: The advancement in smart sensors, the Internet of Things (IoT) have augmented the healthcare systems (Pramanik, et al., 2019) and offered new opportunities for patients care strategies. The inertial sensing system has been widely used in human motion monitoring and neuro-motor rehabilitation monitoring. To obtain more precision and stable monitoring, multi-sensor data fusion based on Kalman filters have been widely studied (Bai, et al., 2020).
Recent emergence in AI (Artificial Intelligence) technology has become a consensus tool which has been successfully applied in various field (Shukla, et al, 2018). Using of AI technology will enhance the inertial sensing for human motion monitoring on a variety of aspects including sensor error estimation, sensor fusion, calibration, and motion tracking algorithms.
Project aim: The proposed system will help to address the limitation of current nonlinear state estimation method for multi-sensor data fusion by applying the AI technology to enhance motion tracking algorithms. There are two major aims of this project. One is to enhance the sensor calibration and use of AI in sensor fusion. Secondly is to apply AI technology to enhance the motion tracking (e.g. position, velocity, and orientation).
Furthermore, the system is expected to provide an effective means to automatically and quantitatively assess human motion performance. AI based methods will be developed to improve the motion tracking accuracy and applying to applications in human movement analysis and rehabilitation assessment. This proposed research project aligns with the school research focus in the areas of healthcare and interdisciplinary research.
Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.
We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.
In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
The University offers the following levels of support:
The following scholarship options are available to applicants worldwide:
These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.
Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.
Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).
This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Due consideration should be given to financing your studies. Further information on cost of living
References:
Pramanik, P.K.D., Upadhyaya, B.K., Pal, S. and Pal, T., 2019. Internet of things, smart sensors, and pervasive systems: Enabling connected and pervasive healthcare. In Healthcare data analytics and management (pp. 1-58). Academic Press.
Bai, L., Pepper, M.G., Yan, Y., Phillips, M. and Sakel, M., 2020. Low Cost Inertial Sensors for the Motion Tracking and Orientation Estimation of Human Upper Limbs in Neurological Rehabilitation. IEEE Access, 8, pp.54254-54268.
Shukla, A.K., Janmaijaya, M., Abraham, A. and Muhuri, P.K., 2019. Engineering applications of artificial intelligence: A bibliometric analysis of 30 years (1988–2018). Engineering Applications of Artificial Intelligence, 85, pp.517-532.
Submission deadline
Friday 5 February 2021
12:00AM
Interview Date
Week Beginning 22nd March 2021
Preferred student start date
mid September 2021