Multimodal, privacy-preserving, low-cost ambient sensing for the remote agentic AI-enabled monitoring of respiratory diseases

About the Project

This doctoral research proposes the development of a novel multimodal, privacy-preserving, low-cost ambient sensing system for the remote monitoring of respiratory diseases in home environments, with a primary focus on chronic obstructive pulmonary disease (COPD) due to its high prevalence and economic burden on healthcare systems. The system aims to enable early detection of health deterioration by analysing behavioural and physiological signals collected passively in everyday living settings.

Project details

This research aims to enable early detection of health deterioration by analysing behavioural and physiological signals collected passively in everyday living settings. The proposed approach integrates two main sensing modalities: ambient audio monitoring and video-based pose estimation. Audio data will be analysed to detect respiratory symptoms such as coughing, wheezing, or wet cough associated with COPD exacerbations. To protect privacy, signal processing techniques such as pitch shifting and band-pass filtering will be applied to remove intelligible speech from recorded audio. Video monitoring will employ pose estimation algorithms that capture only the key joint locations of the human body rather than identifiable images. This allows the system to track physical activity, sedentary behaviour, and deviations from normal routines while preserving user privacy. Reduced movement and increased inactivity are important behavioural indicators of COPD exacerbation.

The research will combine these modalities through artificial intelligence models that analyse cough sounds and activity patterns simultaneously. Multimodal analysis helps reduce false positives, for example by verifying that a detected cough is accompanied by corresponding body movement. Additional safeguards will include training models to recognise cough sounds specific to an individual’s vocal tract and identifying sound direction to distinguish patient coughs from background sources such as television audio.

The system will be implemented as a low-cost embedded platform, potentially using hardware such as a Raspberry Pi with integrated microphone and camera sensors. Additional optional sensing may periodically capture clinical indicators and support monitoring for related conditions such as Alzheimer’s disease or depression, which may also benefit from unobtrusive home monitoring.

To support use in multi-occupant households, the system will employ non-intrusive identification methods such as gait patterns, body dimensions, or personal accessories (e.g., glasses or watches). Face recognition may be used only if the data remains locally processed within the home device. A proof-of-concept prototype will be developed, with initial testing conducted using simulated coughing data from research team members before progressing to real-world studies involving participants.

The project introduces novelty in several areas. First, it explores privacy-preserving in-home video monitoring, which remains under-researched due to privacy concerns. Second, it combines audio and activity visual data to improve the reliability of respiratory symptom detection. Third, the system will integrate agentic AI models, including large language models such as BioMedLM or MedAlpaca, to interpret aggregated patient data and provide explainable health assessments. These AI agents will analyse patterns such as cough frequency and activity levels, generate evidence-based insights, and support early intervention or preventative care.

Overall, the research aims to demonstrate that multimodal, privacy-aware sensing and advanced AI reasoning can enable accurate, unobtrusive health monitoring in domestic environments, improving early detection and management of chronic respiratory conditions.

Person specification

The successful applicant should hold a first-class or upper second-class honours degree, or an equivalent qualification, in a relevant discipline such as chemical engineering, energy engineering, environmental engineering, chemistry, or energy systems. A relevant master’s degree is desirable but not essential.

Strong knowledge of multimodal analysis, machine learning, and advanced AI reasoning especially for medical applications.

Proficient in programming and use of simulation environments.

Well proven technical writing skills and abilities, preferably demonstrated by a track record of past publications in journals and/or conferences.

Submitting an application

We can only consider applications that are complete and have all supporting documents. Applications that do not provide all the relevant documents will be automatically rejected. Your application must include:

1. English language copies of the transcripts and certificates for all your higher education degrees, including any Bachelor degrees.
2. A Research Statement detailing your understanding of the research area, how you would approach the project, and a brief review of relevant literature. Be sure to use the title of the research project you are applying for. There is no set format or word count.
3. A personal statement which outlines any further information which you think is relevant to your application, such as your personal suitability for research, career aspirations, possible future research interests, and further description of relevant employment experience.
4. A Curriculum Vitae (Resume) which details your education and work history.
5. Two academic referees who can discuss your suitability for independent research. References must be on headed paper, signed and dated no more than 2 years old. At least one reference should be from your most recent University. You can submit your references at a later date if necessary.
6. Evidence that you meet the English Language requirements. If you do not currently meet the language requirements, you can submit this at a later stage.
7. A copy of your passport. Where relevant, include evidence of settled or pre-settled status.

If you require further information about the application process, please contact the Postgraduate Admissions team at pgr_admissions@aston.ac.uk.

International Applicants

The opportunity is for HOME students only

Interviews

Interviews will be conducted online via Microsoft Teams. If you are shortlisted, you will be contacted directly with details of the interview.

Funding Notes

This project is open to Home students ONLY, covers all tuition fees and includes a stipend at current UKRI rates. The project also includes a Research Training and Support Grant.

Please note that the successful candidate will be responsible for any expenses related to moving to Birmingham and/or visiting the Aston campus.

Where will I study?

Aston University

Situated in the heart of the Birmingham Innovation Precinct, Aston University stands as a leader in technology, innovation, and entrepreneurship. Its cutting-edge research addresses critical societal, cultural, health, and environmental challenges, guided by three interdisciplinary themes: health, digital innovation, and technology.

Project supervisors

Professor Abdul Sadka