Real-time ECG Monitoring using Compressive sensing on a Heterogeneous Multicore Edge-Device
In a typical ambulatory health monitoring systems, wearable medical sensors are deployed on the human body to continuously collect and transmit physiological signals to a nearby gateway that forward the measured data to the cloud-based healthcare platform. However, this model often fails to respect the strict requirements of healthcare systems. Wearable medical sensors are very limited in terms of battery lifetime, in addition, the system reliance on a cloud makes it vulnerable to connectivity and latency issues. Compressive sensing (CS) theory has been widely deployed in electrocardiogramme ECG monitoring application to optimize the wearable sensors power consumption. The proposed solution in this paper aims to tackle these limitations by empowering a gatewaycentric connected health solution, where the most power consuming tasks are performed locally on a multicore processor. This paper explores the efficiency of real-time CS-based recovery of ECG signals on an IoT-gateway embedded with ARM’s big.littleTM multicore for different signal dimension and allocated computational resources. Experimental results show that the gateway is able to reconstruct ECG signals in real-time. Moreover, it demonstrates that using a high number of cores speeds up the execution time and it further optimizes energy consumption. The paper identifies the best configurations of resource allocation that provides the optimal performance. The paper concludes that multicore processors have the computational capacity and energy efficiency to promote gateway-centric solution rather than cloud-centric platforms.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Citation : Djelouat, H., Amira, A., Bensaali, F., Kotronis, C., Politi, E., Nikolaidou, M., Dimitrakopoulos, G. (2019) Real-time ECG Monitoring using Compressive sensing on a Heterogeneous Multicore Edge-Device. Microprocessors and Microsystems,
ISSN : 0141-9331
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes