Learning Enabled Human Activity Recognition and Tracking for Home and Social Care


The project will develop novel techniques, based on Artificial Intelligence/Machine Learning (AI/ML) and new sensors such as millimetrewave radars, for the non-invasive diagnosis for home and social care. A millimetrewave sensor can wirelessly and non-invasively detect vital signs including pulse rate and respiration rate of the human body. Routinely and continuously monitoring such signs is crucial and essential for prolonging independence and maintaining general wellness for elder people and those who live alone. While AI/ML has been successful in tasks such as image processing, new methods will be needed to deal with millimetrewave sensory input which contains significant noises and outliers. This is compounded by the challenges from the detection of minor – or even invisible -- human activities (such as heartbeat and respiration) and the medical context where the connection between human activities and illnesses needs to be established and clearly articulated.


The objectives of this PhD project are to (1) develop novel automated techniques for non-invasive and unobtrusive diagnosis, based on AI/ML and sensing techniques, (2) develop the student into a future leader of this interdisciplinary area (AI/ML, sensing, and healthcare), and (3) foster the collaboration, and enable the two-way exchange of knowledge, between clinical and academic partners, in order to support the NHS and increase societal and economic impact.

Supervisory Arrangement

The supervisory team provides ideal support to this project. Dr Huang will provide supervision on AI/ML. Dr Zhou will provide supervision on sensing and signal processing technology, including the millimetrewave techniques. The project will be supported by medical professionals in two local hospitals. 

Working Environment

The student will be placed at the Digital Innovation Facility, where the primary supervisor Dr Huang is the director of the Home and Social Care Lab. The lab has all the technical facilities needed for this project, including 3 GPU servers for computational needs, 10+ millimetrewave radars with data acquisition panels for sensing and data capturing, and 10+ mobile robots that can be used for this project. The co-supervisor Dr Zhou has a dedicated lab in EEE with radio frequency devices. The generous technical support from both sides will be sufficient for a PhD project from day one. The clinical experimental environment will be provided by our clinical partner in two local hospitals.

The scholarship is funded by University of Liverpool Doctoral Network in Technologies for Healthy Ageing (https://www.liverpool.ac.uk/study/postgraduate-research/doctoral-training-programmes/technologies-for-healthy-ageing/ ).

The PhD commences in October 2021 and is fully funded for 3.5 years (with UKRI level stipend, currently £15,609pa).

The student is expected to have an undergraduate degree in either Computer Science or Electronic Engineering. Given this position will be competitive, a 1st class degree might be needed. 

Due to a recent change in UKRI policy, this is now available for Home, EU or international students to apply. However, please be aware there is a limit on the number of international students we can appoint to these studentships per year.

Enquiries to: Dr Xiaowei Huang () and Dr Jiafeng Zhou ( )

To apply:  please send your CV and a covering letter to  please put Technologies for Healthy Ageing in the subject line

Expected interviews in April 2021


Open to students worldwide

Funding information

Funded studentship

This studentship is funded by the EPSRC DTP scheme and is offered for 3.5years in total. It provides full tuition fees and a stipend of approx. £15,609 tax free per year for living costs. The stipend costs quoted are for students starting from 1st October 2021 and will rise slightly each year with inflation.
The funding for this studentship also comes with a budget for research and training expenses of £1000 per year, and for those that are eligible, a disabled students allowance to cover the costs of any additional support that is required.



Huang, X., et al. A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability. https://doi.org/10.1016/j.cosrev.2020.100270
Jussi Kuutti , et al. Evaluation of a Doppler radar sensor system for vital signs detection and activity monitoring in a radio-frequency shielded room, Measurement.