Low-power, intelligent sensor hardware interface for medical data preprocessing

Authors:
Fei Hu;Shruti Lakdawala;Qi Hao;Meikang Qiu
Affiliations:
Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL;Department of Computer Engineering, The University of Rochester, Rochester, NY;Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL;Department of Electrical and Computer Engineering, University of New Orleans, Orleans, LA
Venue:
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Year:
2009

Citing 4
Cited 1

Wireless integrated network sensors

Communications of the ACM
Low-cost wireless sensor networks for remote cardiac patients monitoring applications

Wireless Communications & Mobile Computing
Ubiquitous computing for remote cardiac patient monitoring: a survey

International Journal of Telemedicine and Applications - Regular issue
Privacy-Preserving Telecardiology Sensor Networks: Toward a Low-Cost Portable Wireless Hardware/Software Codesign

IEEE Transactions on Information Technology in Biomedicine

Design and Optimization of Traffic Balance Broker for Cloud-Based Telehealth Platform

UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This work proposes an interface design of a low-power programmable system on chip for intelligent wireless sensor nodes to reduce the overall power consumption of the heart disease monitoring system, by lending them the capability of processing complex functions and performing rapid computations on a large amount of data at the node. This facilitates the node to intelligently monitor a medical signal for impending events instead of transmitting the signal to the base station constantly. Lowering the transmission data rate decreases the transmission power consumption in a node, thereby lengthening the node life and in turn increasing the reliability of the network. This work also implements a thresholding technique, which controls the data transmission rate depending on the value of the monitored signal, and a cardiac monitoring system that performs computations at the node for the detection of either a skipped heart beat or a reduced heart rate variability, in which event the signal is transmitted to the base station for monitoring/recording or alerting the crew. The performance analysis of the system shows that there are reductions in the system power consumption and data transmission rate, which in turn reduces the network traffic and averts congestion.