A novel gateway-based solution for remote elderly monitoring

Internet of Things (IoT) technologies have been applied to various fields such as manufacturing, automobile industry and healthcare. IoT-based healthcare has a significant impact on real-time remote monitoring of patients' health and consequently improving treatments and reducing healthcare costs. In fact, IoT has made healthcare more reliable, efficient, and accessible. Two major drawbacks which IoT suffers from can be expressed as: first, the limited battery capacity of the sensors is quickly depleted due to the continuous stream of data; second, the dependence of the system on the cloud for computations and processing causes latency in data transmission which is not accepted in real-time monitoring applications. This research is conducted to develop a real-time, secure, and energy-efficient platform which provides a solution for reducing computation load on the cloud and diminishing data transmission delay. In the proposed platform, the sensors utilize a state-of-the-art power saving technique known as Compressive Sensing (CS). CS allows sensors to retrieve the sensed data using fewer measurements by sending a compressed signal. In this framework, the signal reconstruction and processing are computed locally on a Heterogeneous Multicore Platform (HMP) device to decrease the dependency on the cloud. In addition, a framework has been implemented to control the system, set different parameters, display the data as well as send live notifications to medical experts through the cloud in order to alert them of any eventual hazardous event or abnormality and allow quick interventions. Finally, a case study of the system is presented demonstrating the acquisition and monitoring of the data for a given subject in real-time. The obtained results reveal that the proposed solution reduces 15.4% of energy consumption in sensors, that makes this prototype a good candidate for IoT employment in healthcare. 2020 Elsevier Inc.This paper was made possible by the National Priorities Research Program (NPRP) grant No. 9-114-2-055 from the Qatar National Research Fund (a member of Qatar Foundation ). The statements made herein are solely the responsibility of the authors.Scopu

The accuracy and efficacy of real-time compressed ECG signal reconstruction on a heterogeneous multicore edge-device

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.Scopu