Abstract
Accidental opioid overdose and Sudden Unexpected Death in Epilepsy (SUDEP) represent major forms of preventable mortality, often involving sudden-onset catastrophic events that could be survivable with rapid detection and intervention. The current physiological monitoring technologies are potentially applicable, but face challenges, including complex setups, poor patient compliance, high costs, and uncertainty about community-based use. Paradoxically, simple clinical observation in supervised injection facilities has proven highly effective, suggesting observable changes in central body motion may be sufficient to detect life-threatening events. We describe a novel wearable biosensor for continuous central body motion monitoring, offering a potential early warning system for life-threatening events. The biosensor incorporates a low-power, triaxial MEMS accelerometer within a discreet, chest-worn device, enabling long-term monitoring with minimal user burden. Two system architectures are described: stored data for retrospective analysis/research, and an in-development system for real-time overdose detection and response. Early user research highlights the importance of accuracy, discretion, and trust for adoption among people who use opioids. The initial clinical data collection, including the OD-SEEN study, demonstrates feasibility for capturing motion data during real-world opioid use. This technology represents a promising advancement in non-invasive monitoring, with potential to improve the outcomes for at-risk populations with multiple health conditions.
| Original language | English |
|---|---|
| Article number | 11027 |
| Number of pages | 14 |
| Journal | Applied Sciences (Switzerland) |
| Volume | 15 |
| Issue number | 20 |
| DOIs | |
| Publication status | Published - 14 Oct 2025 |
Keywords
- actigraphy
- drug overdose
- emergency detection
- motion sensors
- wearable technology
ASJC Scopus subject areas
- General Materials Science
- Instrumentation
- General Engineering
- Process Chemistry and Technology
- Computer Science Applications
- Fluid Flow and Transfer Processes