An approximate compressor for wearable biomedical healthcare monitoring systems

Samie, Farzad; Bauer, Lars; Henkel, Jörg

doi:10.1109/codesisss.2015.7331376

Cited by 18 publications

(7 citation statements)

References 31 publications

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“…• Physical sensor: generally any medical device with a wired/wireless interface can be used in eHealth ecosystem to track patients' physical wellness, and digitally monitor their health[49]. This includes ECG/EKG monitor[50],[51],[52],[53],[54],[55],[56],[57],[58],[59],[60],[61], heart rate monitor[62],[63], glucose monitor[64],[65],[66], blood pressure monitor[67],[68],[69], body temperature monitor[70], pulse oximeter[71], hemoglobin monitor[72], activity monitor[73], smart shoes[74], smart garments or e-textiles[75],[76],[77], sleep monitor[78], knee sensor[79], skin…”

mentioning

confidence: 99%

Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare

Farahani

Firouzi

Chang

et al. 2018

Future Generation Computer Systems

796

371

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Internet of Things (IoT) offers a seamless platform to connect people and objects to one another for enriching and making our lives easier. This vision carries us from compute-based centralized schemes to a more distributed environment offering a vast amount of applications such as smart wearables, smart home, smart mobility, and smart cities. In this paper we discuss applicability of IoT in healthcare and medicine by presenting a holistic architecture of IoT eHealth ecosystem. Healthcare is becoming increasingly difficult to manage due to insufficient and less effective healthcare services to meet the increasing demands of rising aging population with chronic diseases. We propose that this requires a transition from the clinic-centric treatment to patient-centric healthcare where each agent such as hospital, patient, and services are seamlessly connected to each other. This patient-centric IoT eHealth ecosystem needs a multi-layer architecture: 1) device, 2) fog computing and 3) cloud to empower handling of complex data in terms of its variety, speed, and latency. This fog-driven IoT architecture is followed by various case examples of services and applications that are implemented on those layers. Those examples range from mobile health, assisted living, e-medicine, implants, early warning systems, to population monitoring in smart cities. We then finally address the challenges of IoT eHealth such as data management, scalability, regulations, interoperability, device-network-human interfaces, security, and privacy.

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mentioning

confidence: 99%

Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare

Farahani

Firouzi

Chang

et al. 2018

Future Generation Computer Systems

796

371

View full text Add to dashboard Cite

show abstract

“…However, in more recent works, researchers also began looking into optimizations. For example, Samie et al [5] used data compression in a wearable ECG monitoring platform to reduce the overall energy consumption and it resulted in a device that could theoretically monitor the ECG of a patient for 10 days using a 400 mAh battery.…”

Section: Related Workmentioning

confidence: 99%

Communication and Security Trade-Offs for Battery-Powered Devices: A Case Study on Wearable Medical Sensor Systems

et al. 2021

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In order to reduce the workload of hospital staff and to provide better services to hospitalized patients, attempts are made to integrate patient monitoring systems directly into hospital networks. Monitoring systems must respond to more and more technological challenges. They are ideally portable and wireless, to free the patient from the hospital bed. At the same time, to enable better patient follow-up, a large amount of information needs to be transmitted and processed in real time. Challenges in the design of such systems include energy-efficient processing and communication, and guaranteeing the security of the measured data. This paper describes a wearable sensor system, integrated into a hospital network, that supports high data rates generated by multiple sensors. With a strongly motivated focus on end-to-end security, we explore trade-offs with respect to security schemes and implementations, and wireless network protocols. The results show that the energy efficiency of the resulting system is comparable to existing systems that support far less sensor data and that compromise on end-to-end security by offloading security operations to a delegation server. To our knowledge, this is the first work that explores the impact of the security scheme and the wireless network protocol on the energy consumption of a wearable device, while providing true end-to-end security.

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“…• approximate computing [26], [27], energy harvesting [28], [29] and non volatile processors [30], [31], which are mainly efficient. The approximate computing paradigm tolerates less accurate results while preserving acceptable output quality.…”

Section: Related Workmentioning

confidence: 99%

Towards Energy-Efficient Heterogeneous Multicore Architectures for Edge Computing

et al. 2019

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In recent years, the edge computing paradigm has been attracting much attention in the Internetof-Things domain. It aims to push the frontier of computing applications, data, and services away from the usually centralized cloud servers to the boundary of the network. The benefits of this paradigm shift include better reactivity and reliability, reduced data transfer costs toward the centralized cloud servers, and enhanced confidentiality. The design of energy-efficient edge compute nodes requires, among others, low power cores such as microprocessors. Heterogeneous architectures are key solutions to address the crucial energy-efficiency demand in modern systems. They combine various processors providing attractive power and performance trade-offs. Unfortunately, no standard heterogeneous microcontroller-based architecture exists for edge computing. This paper deals with the aforementioned issue by exploring typical low power architectures for edge computing. Various heterogeneous multicore designs are developed and prototyped on FPGA for unbiased evaluation. These designs rely on cost-effective and inherently ultra-low power cores commercialized by Cortus SA, a world-leading semiconductor IP company in the embedded ultra-low power microcontroller domain. Some microarchitecture-level design considerations, e.g., floating point and out-of-order computing capabilities, are taken into account for exploring candidate solutions. In addition, a tailored and flexible multi-task programming model is defined for the proposed architecture paradigm. We analyze the behavior of various application programs on available core configurations. This provides valuable insights on the best architecture setups that match program characteristics, so as to enable increased energy-efficiency. Our experiments on multi-benchmark programs show that on average 22% energy gain can be achieved (up to 45%) compared to a reference system design, i.e., a system with the same execution architecture, but agnostic of the task management insights gained from the comprehensive evaluation carried out in this work.INDEX TERMS Edge computing, energy-efficiency, heterogeneous multicore architectures, programming model, embedded systems.

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An approximate compressor for wearable biomedical healthcare monitoring systems

Cited by 18 publications

References 31 publications

Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare

Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare

Communication and Security Trade-Offs for Battery-Powered Devices: A Case Study on Wearable Medical Sensor Systems

Towards Energy-Efficient Heterogeneous Multicore Architectures for Edge Computing

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