Integrating wearable technology and Artificial Intelligence for enhanced health monitoring and rehabilitation

Summary

Wearable technology has changed how we monitor health by allowing us to track detailed movement data over long periods, in real-time, and from anywhere. Smartwatches can measure things like heart rate, sleep patterns, and physical activity, giving us valuable information about overall health. Other sensors, such as those that measure muscle activity (EMG), stress levels (GSR), and precise body movement (IMU), can provide a complete picture of a person’s wellbeing. EMG sensors help understand muscle function in people with neurological conditions. GSR sensors monitor stress by measuring skin conductivity, and IMUs track movements and posture to assess mobility and balance.
Virtual Reality (VR) technology also has potential for monitoring and treating various diseases. VR can create lifelike environments for assessment and rehabilitation. For example, VR can help improve motor skills in patients with neurological conditions through interactive exercises that mimic daily activities. This approach can be used for stroke rehabilitation by facilitating repetitive, task-specific training that promotes brain recovery. Additionally, VR can assess cognitive functions and mental health by placing patients in virtual environments that test their memory, attention, and problem-solving skills.

Combining VR with wearable sensors could enhance the monitoring of both physical and mental wellbeing. A patient wearing a smartwatch and EMG sensors may take part in a VR-based rehabilitation program that tracks their movements and muscle activity in real-time. This integrated approach using multiple systems provides a comprehensive assessment of the patient’s progress. VR environments can also be designed to reduce stress and anxiety, monitored through GSR sensors, supporting mental health and physical rehabilitation.

Artificial intelligence (AI) plays a crucial role by analysing large volumes of sensor data with high accuracy. AI algorithms filter out noise, correct sensor errors, and integrate data from multiple sensors to create an accurate representation of a patient’s physiology. This combination of data provides a holistic view of a patient’s health, enabling more precise diagnoses and personalised treatment plans.
By integrating data from these diverse sensors, early signs of diseases can be detected. Subtle changes in gait or tremor patterns detected by IMUs and smartwatches could indicate early stages of Parkinson’s disease. Variations in muscle activity captured by EMG sensors might signal the onset of neuromuscular disorders. Elevated stress levels monitored by GSR sensors could be an early warning sign of mental health issues or cardiovascular problems. Continuous and comprehensive monitoring allows for early detection of health issues, potentially leading to earlier interventions and better outcomes.

This PhD proposal aims to use off-the-shelf wearable sensors to monitor and measure the movements of individuals with various diseases, such as Parkinson’s disease, those undergoing stroke rehabilitation, or those with spinal mobility issues.

Essential criteria

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.

• Experience using research methods or other approaches relevant to the subject domain
• A demonstrable interest in the research area associated with the studentship

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%
• For VCRS Awards, Masters at 75%
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• Experience of presentation of research findings

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

• Department for the Economy (DfE)
• Vice Chancellor's Research Scholarship (VCRS)

Our fully funded PhD scholarships will cover tuition fees and provide a maintenance allowance of £19,237 (tbc) per annum for three years (subject to satisfactory academic performance).  A Research Training Support Grant (RTSG) of £900 per annum is also available.

These scholarships, funded via the Department for the Economy (DfE) and the Vice Chancellor’s Research Scholarships (VCRS), are open to applicants worldwide, regardless of residency or domicile.

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.

Due consideration should be given to financing your studies.