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Privacy-aware home activity monitoring and automated analysis system

About This PhD Project

Project Description


This project aims to develop a smart distributed environmental system able to sense and learn activity patterns from the users at their homes, then find deviations from the usual patterns that could highlight a number of potential issues.

On average people spend 90% or their lifetimes indoors, most of this time in their own home. This is especially relevant for elderly people or people suffering from a number of permanent or temporal conditions that restrict their time outside home, for instance in need of rehabilitation.

On a general context people at their homes tend to unconsciously follow regular patterns of behaviour and activity on a daily and weekly basis. Alterations of these patterns can point to issues related to health, or even imply safety risks. A distributed environmental monitoring system that, avoiding privacy issues, can learn to track and model someone’s activity patterns, and then find significant deviations on these patters, could have a huge impact in people’s lives by raising early warnings either to the user or to a designated person or carer. The system will not require that the user wears any kind of device, avoiding issues related to discomfort, forgetfulness and battery-life.

There’re a number of potential applications of this type of system, to set some examples: reminders about basic daily activities to people that can potentially forget them, rehab patterns not correctly followed, intrusion detection, early detection of health issues or worsening of current ones. Patients in need of rehab are often prescribed a certain activity or training patterns by health specialists; the proposed system could monitor the actual activity carried out by the patient. People living alone and people depending on care from relatives, friends or professional services would benefit from a system that could produce early diagnostics from just their daily activity.


This PhD focus on developing a privacy-respectful data activity gathering system based on remote distributed sensors avoiding the need of the user to wear any device, using a machine learning core that can exploit the activity data to:
- model regular patterns and find out alterations on them that can highlight health/safety issues and trigger early warnings or take other appropriate actions
- compare these patterns with recommended ones for either rehabilitation of just a healthy life style


The successful applicant will hold a UK honours degree (or equivalent) on Electronic Engineering, Data Science or Computer Science on a first degree (2:1 or above). Equivalent professional qualifications and any appropriate research experience may be considered. A minimum English language level of IELTS score of 6.5 (or equivalent) with no element below 6.0 is required.

Candidates are requested to submit a more detailed research proposal (of a maximum of 2000 words) on the project area as part of their application.

Research Strategy and Research Profile

Glasgow Caledonian University’s research is framed around the United Nations Sustainable Development Goals, We address the Goals via three societal challenge areas of Inclusive Societies, Healthy Lives and Sustainable Environments (more). This project is part of the research activity of the Research Group – Smart Connectivity and Sensing (SCaS).

How to Apply

Applicants will normally hold a UK honours degree 2:1 (or equivalent); or a Masters degree in a subject relevant to the research project. Equivalent professional qualifications and any appropriate research experience may be considered. A minimum English language level of IELTS score of 6.5 (or equivalent) with no element below 6.0 is required. Some research disciplines may require higher levels.

Candidates are encouraged to contact the research supervisors for the project before applying. Applicants should complete the online GCU Research Application Form, stating the Project Title and Reference Number (listed above).

Please also attach to the online application, copies of academic qualifications (including IELTS if required), 2 references and any other relevant documentation.

Please send any enquiries regarding your application to: Applicants shortlisted for the PhD project will be contacted for an interview.

For more information on How to apply and the online application form please go to

Dr Mario Mata (ERC)

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