ECG Signal Reconstruction on the IoT-Gateway and Efficacy of Compressive Sensing Under Real-Time Constraints

Disi, Mohammed Al; Djelouat, Hamza; Kotroni, Christos; Politis, Elena; Amira, Abbes; Bensaali, Fayçal; Dimitrakopoulos, George; Alinier, Guillaume

doi:10.1109/access.2018.2877679

Cited by 37 publications

(30 citation statements)

References 42 publications

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“…The medical area will grow fivefold, the fastest, from 144 million connections in 2015 to 729 million in 2020 [1]. There are many applications of IoT in medicine, including in electrocardiography [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17].…”

Section: Introductionmentioning

confidence: 99%

IoT Based Heart Activity Monitoring Using Inductive Sensors

Brezulianu

Geman

Zbancioc

et al. 2019

Sensors

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This paper presents a system dedicated to monitoring the heart activity parameters using Electrocardiography (ECG) mobile devices and a Wearable Heart Monitoring Inductive Sensor (WHMIS) that represents a new method and device, developed by us as an experimental model, used to assess the mechanical activity of the hearth using inductive sensors that are inserted in the fabric of the clothes. Only one inductive sensor is incorporated in the clothes in front of the apex area and it is able to assess the cardiorespiratory activity while in the prior of the art are presented methods that predict sensors arrays which are distributed in more places of the body. The parameters that are assessed are heart data-rate and respiration. The results are considered preliminary in order to prove the feasibility of this method. The main goal of the study is to extract the respiration and the data-rate parameters from the same output signal generated by the inductance-to-number convertor using a proper algorithm. The conceived device is meant to be part of the “wear and forget” equipment dedicated to monitoring the vital signs continuously.

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Section: Introductionmentioning

confidence: 99%

IoT Based Heart Activity Monitoring Using Inductive Sensors

Brezulianu

Geman

Zbancioc

et al. 2019

Sensors

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“…Tested on a single subject, limited gesture recognition supported [54] A mobile real-time health monitoring architecture based on a heterogeneous multicore platform for ECG signal processing Enhanced battery life, low latency, low power device design Sub-optimal performance for clinical-grade signals due to frequent transmissions [55] A dictionary-based lossy signal compression technique for enhancing energy efficiency of wearables…”

Section: B Activity Recognition Applicationsmentioning

confidence: 99%

“…Content may change prior to final publication. Embedded signal processing to avoid unnecessary data transmission by carrying out local processing [54], [57], [58], [87] Compressive sensing to avoid generating unnecessary redundant data [53], [84], [92], [99] Embedded ML to carry out complex computation tasks on the device to reduce latency Wireless technologyrelated issues A, HW [45] Duty cycling to periodically power off communication modules when not in use to conserve energy [49], [61] Adaptive transmission power control of transceiver based on distance to remote node [59] Use of low power wireless communication protcols such as 6LoWPAN [61] A multi channel TDMA approach to allocate slots for simultaneous transmission [109] An adaptive connection interval selection in dynamic channel environments for improved connectivity [98], [148] Low power wireless communication protocols…”

Section: Challenges and Future Research Directionsmentioning

confidence: 99%

“…A, N, SW [46], [111] Energy awareness regarding neighboring nodes to select the optimal route [50] Multi parameter cost function for the next hop selection [60] Selective data routing based on the data priority Securityrelated aspects HW, DP, SW [110] Content agnostic privacy and encryption protocol eliminating the need for asymmetric encryption [180], [181] Integration of lightweight cryptography solutions including more appropriate elliptic curve types or algorithm implementations [186] More efficient utilization of manufacturer-provide SoCs accelerated for cryptographic primitives execution [187] Finding trade-offs between the primitive and required level of the provided security Processing limitations HW, DP, SW [54] The use of heterogeneous multicore processor gateway as compared to little cores gateway working as a router [64], [86], [104], [105] Task offloading to leverage high computing resources of nearby devices for improved performance [106] Edge/fog/cloud computing techniques for optimal performance [107] Seamless resource sharing between heterogeneous mobile devices Storage limitations HW [47], [55] Data compression to reduce the size of the dataset for efficient data processing and storage [106] Edge/Fog/Cloud computing techniques for better performance [173] Data summarization and aggregation Lack of hardware acceleration HW, SW [47], [55] Data compression to reduce the size of the data set for more efficient data processing and storage [64], [86], [104], [105] Task offloading to leverage high computing resources of the nearby devices for the improved performance [186] Identifying and use of present hardware acceleration, which may not be accessible by the default Inefficient use of energy consuming modules HW, SW [62] Configurable data acquisition modules [88] Replacing high power consumption modules with low power alternates, e.g., using two accelerometers instead of a gyroscope as...…”

Section: Inefficient Routingmentioning

confidence: 99%

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Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

et al. 2020

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Personal mobile devices such as smartwatches, smart jewelry, and smart clothes have launched a new trend in the Internet of Things (IoT) era, namely the Internet of Wearable Things (IoWT). These wearables are small IoT devices capable of sensing, storing, processing, and exchanging data to assist users by improving their everyday life tasks through various applications. However, the IoWT has also brought new challenges for the research community to address such as increasing demand for enhanced computational power, better communication capabilities, improved security and privacy features, reduced form factor, minimal weight, and better comfort. Most wearables are battery-powered devices that need to be recharged-therefore, the limited battery life remains the bottleneck leading to the need to enhance the energy efficiency of wearables, thus, becoming an active research area. This paper presents a survey of energy-efficient solutions proposed for diverse IoWT applications by following the systematic literature review method. The available techniques published from 2010 to 2020 are scrutinized, and the taxonomy of the available solutions is presented based on the targeted application area. Moreover, a comprehensive qualitative analysis compares the proposed studies in each application area in terms of their advantages, disadvantages, and main contributions. Furthermore, a list of the most significant performance parameters is provided. A more in-depth discussion of the main techniques to enhance wearables' energy efficiency is presented by highlighting the trade-offs involved. Finally, some potential future research directions are highlighted.

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“…This paper presents some of the results obtained as part of the ongoing project "Embedded multi-core systems for multi-critical applications on the Internet of Things Era (EMBIoT)" [6,7,8,9,10,11]. The paper is dedicated to addressing some of the aforementioned limitations by exploring mutlticore processors in the design of real-time power-efficeint gateway-centric connected 25 health applications.…”

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confidence: 99%

Real-time ECG monitoring using compressive sensing on a heterogeneous multicore edge-device

Djelouat

Disi

Boukhenoufa

et al. 2020

Microprocessors and Microsystems

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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.little TM 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.

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ECG Signal Reconstruction on the IoT-Gateway and Efficacy of Compressive Sensing Under Real-Time Constraints

Cited by 37 publications

References 42 publications

IoT Based Heart Activity Monitoring Using Inductive Sensors

IoT Based Heart Activity Monitoring Using Inductive Sensors

Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

Real-time ECG monitoring using compressive sensing on a heterogeneous multicore edge-device

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