Chetty, Kevin;
Tang, Chong;
Lok, Lai Bun;
Brennan, Paul;
Shi, Fangzhan;
(2025)
Addressing privacy and cost challenges in remote patient monitoring with streamlined 60 GHz radar and edge processing.
In:
Radar Sensor Technology XXIX.
SPIE
Preview |
Text
Brennan_134710H.pdf Download (2MB) | Preview |
Abstract
Remote patient monitoring is critical in elderly care, particularly for detecting incidents like falls and abnormal gait, but current systems face high deployment costs, privacy concerns, and the complexities of continuous data processing. To overcome these challenges, we developed a 60GHz millimetre-wave radar system that processes data on-device, eliminating the need for constant internet access and mitigating privacy risks. This system is optimized for real-time healthcare monitoring in residential and clinical environments, with a radar detection range of 0.6m to 11m. Data captured by the radar are processed on an ATmega328 microcontroller using a quantized Long Short-Term Memory model. The quantization ensures efficient operation under tight resource constraints, enabling accurate classification of movement patterns. The system achieves an inference latency of approximately 300 microseconds, suitable for real-time response. A key innovation is our global confidence mechanism, which reduces false alarms by aggregating predictions over multiple detection frames. This mechanism assigns weighted confidence scores to predictions based on their consistency across time, triggering alerts only when the accumulated score exceeds a predefined threshold. This significantly improves reliability in detecting critical events like falls, reducing false positives. The system was tested on five distinct activities: falls, normal walking, irregular walking, standing, and painful standing, achieving over 91% accuracy overall, with fall detection being the most accurate. Compared to conventional solutions, it provides a cost-effective, privacy-preserving alternative suitable for scalable deployment across healthcare settings. By leveraging on-device machine learning, our approach reduces computational demands and enhances real-time performance without relying on cloud-based processing.
| Type: | Proceedings paper |
|---|---|
| Title: | Addressing privacy and cost challenges in remote patient monitoring with streamlined 60 GHz radar and edge processing |
| Event: | SPIE Defense + Commercial Sensing |
| Location: | Orlando, Florida |
| Dates: | 13 Apr 2025 - 17 Apr 2025 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://doi.org/10.1117/12.3052476 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Remote Patient Monitoring, 60GHz mm-wave Radar, Real-time Edge Processing, Privacy-preserving Healthcare, Embedded Machine Learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10209434 |
Archive Staff Only
 |
View Item |
