Energy-Efficient Long-term Continuous Personal Health Monitoring
Document Type
Article
Publication Date
6-1-2015
Keywords
body area networks, compressive sensing, continuous health monitoring, implantable and wearable medical devices, secure wireless sensor network
Digital Object Identifier (DOI)
https://doi.org/10.1109/TMSCS.2015.2494021
Abstract
Continuous health monitoring using wireless body area networks of implantable and wearable medical devices (IWMDs) is envisioned as a transformative approach to healthcare. Rapid advances in biomedical sensors, low-power electronics, and wireless communications have brought this vision to the verge of reality. However, key challenges still remain to be addressed. The constrained sizes of IWMDs imply that they are designed with very limited processing, storage, and battery capacities. Therefore, there is a very strong need for efficiency in data collection, analysis, storage, and communication. In this paper, we first quantify the energy and storage requirements of a continuous personal health monitoring system that uses eight biomedical sensors: (1) heart rate, (2) blood pressure, (3) oxygen saturation, (4) body temperature, (5) blood glucose, (6) accelerometer, (7) electrocardiogram (ECG), and (8) electroencephalogram (EEG). Our analysis suggests that there exists a significant gap between the energy and storage requirements for long-term continuous monitoring and the capabilities of current devices. To enable energy-efficient continuous health monitoring, we propose schemes for sample aggregation, anomaly-driven transmission, and compressive sensing to reduce the overheads of wirelessly transmitting, storing, and encrypting/authenticating the data. We evaluate these techniques and demonstrate that they result in two to three orders-of-magnitude improvements in energy and storage requirements, and can help realize the potential of long-term continuous health monitoring.
Was this content written or created while at USF?
No
Citation / Publisher Attribution
IEEE Transactions on Multi-Scale Computing Systems, v. 1, issue 2, p. 85-98
Scholar Commons Citation
Mohsen Nia, Arsalan; Mozaffari Kermani, Mehran; Sur-Kolay, Susmita; Raghunathan, Anand; and Jha, Niraj K., "Energy-Efficient Long-term Continuous Personal Health Monitoring" (2015). Computer Science and Engineering Faculty Publications. 21.
https://digitalcommons.usf.edu/esb_facpub/21
