Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
10-2022
Abstract
While the global healthcare market of wearable devices has been growing signi!cantly in recent years and is predicted to reach $60 billion by 2028, many important healthcare applications such as seizure monitoring, drowsiness detection, etc. have not been deployed due to the limited battery lifetime, slow response rate, and inadequate biosignal quality. This study proposes PROS, an e"cient pattern-driven compressive sensing framework for low-power biopotential-based wearables. PROS eliminates the conventional trade-o# between signal quality, response time, and power consumption by introducing tiny pattern recognition primitives and a pattern-driven compressive sensing technique that exploits the sparsity of biosignals. Specifically, we (i) develop tiny machine learning models to eliminate irrelevant biosignal patterns, (ii) e"ciently perform compressive sampling of relevant biosignals with appropriate sparse wavelet domains, and (iii) optimize hardware and OS operations to push processing e"ciency. PROS also provides an abstraction layer, so the application only needs to care about detected relevant biosignal patterns without knowing the optimizations underneath. We have implemented and evaluated PROS on two open biosignal datasets with 120 subjects and six biosignal patterns. The experimental results on unknown subjects of a practical use case such as epileptic seizure monitoring are very encouraging. PROS can reduce the streaming data rate by 24X while maintaining high !delity signal. It boosts the power e"ciency of the wearable device by more than 1200% and enables the ability to react to critical events immediately on the device. The memory and runtime overheads of PROS are minimal, with a few KBs and 10s of milliseconds for each biosignal pattern, respectively. PROS is currently adopted in research projects in multiple universities and hospitals.
Keywords
Biosignal, Compressive Sensing, On-chip Intelligence, Edge-AI, Wearable devices, Cyber-Physical systems
Discipline
Artificial Intelligence and Robotics
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the 28th ACM Annual International Conference on Mobile Computing and Networking, Sydney, 2022 October 17-21
First Page
1
Last Page
15
Identifier
10.1145/3495243.3560533
Publisher
ACM
City or Country
Sydney, Australia
Citation
PHAM, Nhat; JIA, Hong; TRAN, Minh; DINH, Tuan; BUI, Nam; KWON, Young; MA, Dong; NGUYEN, Phuc; MASCOLO, Cecilia; and VU, Tam.
PROS: An efficient pattern-driven compressive sensing framework for low-power biopotential-based wearable with on-chip intelligence. (2022). Proceedings of the 28th ACM Annual International Conference on Mobile Computing and Networking, Sydney, 2022 October 17-21. 1-15.
Available at: https://ink.library.smu.edu.sg/sis_research/7283
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1145/3495243.3560533