Design and Evaluation of a Person-Centric Heart Monitoring System over Fog Computing Infrastructure

Abstract

Heart disease and stroke are becoming the leading cause of death worldwide. Electrocardiography monitoring devices (ECG) are the only tool that helps physicians diagnose cardiac abnormalities. Although the design of ECGs has followed closely the electronics miniaturization evolution over the years, existing wearable ECG have limited accuracy and rely on external resources to analyze the signal and evaluate heart activity. In this paper, we work towards empowering the wearable device with processing capabilities to locally analyze the signal and identify abnormal behavior. The ability to differentiate between normal and abnormal heart activity significantly reduces (a) the need to store the signals, (b) the data transmitted to the cloud and (c) the overall power consumption. Based on this concept, the HEART platform is presented that combines wearable embedded devices, mobile edge devices, and cloud services to provide on-the-spot, reliable, accurate and instant monitoring of the heart. The performance of the system is evaluated concerning the accuracy of detecting abnormal events and the power consumption of the wearable device. Results indicate that a very high percentage of success can be achieved in terms of event detection ratio and the device being operative up to a several days without the need for a recharge. CCS CONCEPTS • Human-centered computing → Mobile devices; • Computing methodologies → Machine learning;

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

“…Rabindra and Rojalina [22] proposed a fogbased machine learning model for smart system big data analytics called FogLearn for application of K-means clustering in Ganga River Basin Management and real-world feature data for detecting diabetes patients suffering from diabetes mellitus. Alvin et al [ [11] ✓ ✓ FogCepCare [12] ✓ ✓ IoT e-health service [13] ✓ ✓ ECGH [14] ✓ ✓ ✓ AMS [16] ✓ ✓ GRAM [17] ✓ [39] proposed a Cloud-based Smart Home Environment (CoSHE) to deliver home healthcare to provide humans contextual information and monitors the vital signs using robot assistant. Initially, CoSHE uses non-invasive wearable sensors to gather the audio, motion and physiological signals and delivers the contextual information in terms of the residents daily activity.…”

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

“…Several research works on wearable ECG monitoring have been developed in the literature [25,29,[125][126][127][128]. These works can be classified into textile-based systems, such as [29,128], and contactless based systems, such as [126].…”

“…Similarly, wearable devices were empowered with processing capabilities to locally (at the edge) analyze the signal and identify abnormal behaviors [144]. However, wearable embedded devices, mobile edge devices, and Cloud services were combined to provide reliable, accurate, and real-time heart monitoring [25,143]. Wearable devices are remotely trained to interpret heart abnormalities and the Fog extends the Cloud by migrating data-processing closer to the production site, thus accelerating the system's responsiveness to events.…”

“…Nowadays, ECG monitoring systems are used in hospitals [14][15][16][17], homes [18][19][20], outpatient ambulatory settings [21][22][23], and in remote contexts [24]. They also employ a wide range of technologies such as IoT [25][26][27], edge computing [28,29], and mobile computing [30][31][32]. In addition, they implement various computational settings in terms of processing frequencies, as well as monitoring schemes.…”

Towards an Architecture to Guarantee Both Data Privacy and Utility in the First Phases of Digital Clinical Trials

“…While some years ago IoT devices were usually constrained in terms of processing power and memory [62], new devices take advantage of the technological advancements in the silicon industry that offer high processing power with little energy consumption. The new capabilities of embedded processors and microcontrollers allow advanced algorithms to be executed within the IoT device [63,64,65,66]. It is important to highlight that, in order to assure confidentiality, some encryption algorithms require the realization of a key exchange before opening a secure communication channel [67,68].…”