Available
Project number:
2025_110
Start date:
October 2025
Project themes:
Main supervisor:
Clinical Academic in the Department of Addiction Sciences, at the Institute of Psychiatry, Psychology & Neuroscience.
Co-supervisor:
Professor Richard Dobson
Industry partner:
Detecting heroin/opioid overdose through analysis of data from wearables: developing a new emergency alert capability.
1. Background
Opioid overdose is the leading cause of accidental death in many countries, with over 80,000 fatalities reported in the UK and US in 2022. Opioids depress respiratory rhythm generation, leading to respiratory failure and death if untreated. Despite naloxone's life-saving potential, delays in detection and intervention often result in fatalities. Existing wearable devices lack the capability to detect respiratory depression accurately or function as regulated medical devices. PneumoWave’s chest biosensor, leveraging accelerometer-based respiratory monitoring, presents an innovative solution for real-time detection of Substance-Induced Respiratory Depression (SIRD) and automated emergency response.
2. Novelty & Importance
This research advances wearable sensor technology and machine learning for overdose detection by identifying digital biomarkers, refining personalised algorithms, and automating longitudinal reporting. Current devices lack precise respiratory depression detection and scalability. This project will overcome dataset limitations, improving algorithm validation. Key objectives include developing machine learning-based alert algorithms, improving validation tools, and positioning the UK as a leader in overdose prevention technology. This approach enhances real-time monitoring, enabling rapid emergency response.
3. Aims & Objectives
Key aims include secondary analysis of data to refine digital biomarkers, conducting the RESCU-3 study to collect real-world data, and developing machine learning algorithms to detect respiratory patterns indicative of SIRD. Objectives involve automating data integration, validating detection algorithms, and iteratively refining alert systems to optimise performance in diverse, real-world settings

We are now accepting applications for 1 October 2025
How to apply
Candidates should possess or be expected to achieve a 1st or upper 2nd class degree in a relevant subject including the biosciences, computer science, mathematics, statistics, data science, chemistry, physics, and be enthusiastic about combining their expertise with other disciplines in the field of healthcare.
Important information for International Students:
It is the responsibility of the student to apply for their Student Visa. Please note that the EPSRC DRIVE-Health studentship does not cover the visa application fees or the Immigration Health Surcharge (IHS) required for access to the National Health Service. The IHS is mandatory for anyone entering the UK on a Student Visa and is currently £776 per year for each year of study. Further detail can be found under the International Students tab below.
Next Steps
- Applications submitted by the closing date of Thursday 6 February 2025 will be considered by the CDT. We will contact shortlisted applicants with information about this part of the recruitment process.
- Candidates will be invited to attend an interview. Interviews are projected to take place in April 2025.
- Project selection will be through a panel interview chaired by either Professor Richard Dobson and Professor Vasa Curcin (CDT Directors) followed by informal discussion with prospective supervisors.
- If you have any questions related to the specific project you are applying for, please contact the main supervisor of the project directly.
For any other questions about the recruitment process, please email us at