Privacy-Preserving Multi-Modal Edge Sensor Fusion for Dementia and Alzheimer’s Health Monitoring

Summary

This PhD will develop and evaluate a privacy-preserving, multi-modal edge sensing platform to support safe and independent living for people with dementia and Alzheimer’s disease. The platform will combine mmWave radar, LiDAR, depth sensing from RGB-D cameras, and wearable devices to identify clinically relevant changes in behaviour without relying on continuous conventional video surveillance or cloud-based processing.

The sensing modalities provide complementary information. mmWave radar can detect presence, movement, gait, velocity and micro-motion, including in darkness.

LiDAR provides three-dimensional location and posture information, supporting fall detection and recognition of a person remaining on the floor. RGB-D cameras will be used primarily in depth-only mode.

Their RGB stream will be disabled where possible, or used only transiently for local inference and then immediately discarded. No RGB images or video will be stored, transmitted or accessible outside the edge device.

The retained information will be privacy-aware depth-derived features, such as silhouettes, pose keypoints, skeletal trajectories and activity features. Wearable sensors, including inertial, activity, sleep and physiological sensors, may provide acceleration, orientation, heart-rate and activity information.

The research will develop adaptive and uncertainty-aware sensor-fusion algorithms for detecting falls, wandering, prolonged inactivity, routine disruption, gait deterioration and potential distress events. The system will be designed to remain useful when a wearable is not worn, a sensor is occluded or an individual sensor stream is unreliable.

Rather than pre-selecting a hardware solution, the project will benchmark FPGA platforms, NVIDIA Jetson-class GPU edge devices and other low-power embedded-AI hardware. It will identify the most suitable single or heterogeneous architecture for sensing, privacy transformation, sensor fusion and AI inference, considering accuracy, latency, energy, cost, robustness and deployability.

Differential privacy will protect activity summaries, aggregate reports and model updates that leave the edge device. Federated learning may also be explored to improve models across homes or care settings without centralising raw personal data.

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.

• A comprehensive and articulate personal statement demonstrating interest in the subject area
• Experience using research methods or other approaches relevant to the subject domain

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.

• Applicants should hold, or expect to hold a First Class Honours Degree in a relevant subject area
• International applicants should hold, or expect to hold a relevant Masters Degree at a level equivalent to a distinction at Ulster
• Experience or knowledge of research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Track record of peer reviewed publications

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)

PhD Scholarships are available for this project and will cover:

• Tuition fees.
• A maintenance allowance of approximately £22,000 (exact rate is to be confirmed) per annum for three years, subject to satisfactory academic performance.
• A Research Training Support Grant (RTSG) of approximately £900 per annum.

To be eligible for these scholarships applicants must: be a UK or Irish national; or have pre-settled or settled status; or have indefinite leave to remain or enter.

Applicants should also meet the residency criteria which requires that they have lived in the EEA, Switzerland, the UK or Gibraltar for at least the three years preceding the start date of the research degree programme i.e. September 2027.

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, funded from any source, including self-financing students, are NOT eligible to apply for an award.

Students in full-time employment are not eligible for an award.

Due consideration should be given to financing your studies.

Part-time PhD scholarships may be available at a 0.5 pro rata rate of the full-time scholarship, which would require a six-year registration period.

The University will award a successful candidate one of the following scholarships for the project, depending on the funding available at the time an admissions offer is made:

1. Department for the Economy (DfE) PhD Scholarships
2. Vice Chancellor’s Research Scholarships (VCRS)

Please note that the Terms and Conditions for scholarships are subject to change for 2027/28.