The Accuracy and Efficacy of Real-Time Compressed ECG Signal Reconstruction on a Heterogeneous Multicore Edge-Device

Abstract

Typical real-time remote health monitoring architectures consist of wearable medical devices continuously transmitting physiological signals to a nearby gateway which routes the data to an remote internet of things (IoT)-platform. Unfortunately, this model falls-short under the strict requirements of healthcare systems. Wearable medical devices have short battery lifespans, the system reliance on a cloud makes it vulnerable to connectivity and latency issues, and there are privacy concerns related to streaming sensitive medical data to remote servers. The compressive sensing (CS) scheme has been explored in the context of bio-signals to reduce the energy consumption of wearable sensors. However, CS does not address the other limitations caused by the model's reliance on cloud-computing but exacerbates the associated computing latency by requiring a computationally complex reconstruction process. In our remote elderly monitoring system, we attempt to address this weakness by developing a gateway-centric connected health system, where most signal processing and analysis occurs locally on heterogeneous multicore edge-devices. This paper explores the efficacy of real-time reconstruction of ECG signals, compressed under the CS scheme, on an IoT-gateway powered by ARM's big. LITTLE multicore solution at different signal dimension and allocated computational resources. Experimental results show the gateway's capability to reconstruct ECG signals in real-time, even when considering dimensionally large windows and minimum computational resources. Moreover, they demonstrate that utilizing more cores for the reconstruction process has a higher impact on execution time and is more energy efficient than increasing the cores' frequency. The optimal resource allocation for the majority of cases is a single big (A15) core at minimum frequency as it provides extreme fast reconstruction while consuming less or slightly more energy than its LITTLE (A7) counterpart. Heterogeneous multicore devices have the computational capacity and energy efficiency to elevate some of the limitations of a cloud-based remote health monitoring and can help create a more sustainable IoT-based connected health.