Raspberry PI 3B+ Based Smart Remote Health Monitoring System Using IoT Platform

Basu, Samik; Ghosh, Mahasweta; Barman, Soma

doi:10.1007/978-981-15-0829-5_46

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…[13] proposed a Smart HealthCare System. S. Basu et al [14] designed a system to monitor critical parameters and provide reports with a high level of accuracy for heartbeats, peripheral capillary oxygen saturation (SpO2) and the temperature. The system has been designed using Raspberry Pi 3B+ minicomputer.…”

Section: Trending Applicationsmentioning

confidence: 99%

Comparative analysis of different Operating systems for Raspberry Pi in terms of scheduling, synchronization, and memory management

Saeed

Khuhro

Waqas

et al. 2022

murjet

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Deep learning, big data, and the internet of things (IoT) have changed the world entirely. As an embedded computer, Raspberry Pi is playing a dynamic and prominent role in the era of ubiquitous computing. In ubiquitous computing, performance and real-time throughput are still an area of focus for Raspberry Pi. Indeed, process scheduling, page swapping, and process synchronization techniques are essential and crucial parameters of the operating system for Raspberry Pi. The key study objectives were; (i) explore the recent trend of applications of Raspberry Pi and (ii) comparison of process scheduling, page swapping and process synchronization techniques with different Raspberry Pi operating systems. The study concluded that Linux-based operating systems are offering an optimized and efficient computing environment for Raspberry Pi.

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Section: Trending Applicationsmentioning

confidence: 99%

Comparative analysis of different Operating systems for Raspberry Pi in terms of scheduling, synchronization, and memory management

Saeed

Khuhro

Waqas

et al. 2022

murjet

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“…Raspberry Pi 4 (Raspi 4) (Raspberry Pi Limited.) is the latest Raspberry Pi device, which is a popular hardware platform for general purpose IoT applications (Zhao et al, 2015;Basu et al, 2020) and is able to support deep learning applications with specifical framework designs (Google LLC., f;Geiger & Team, 2020). We choose the type of Raspi 4 with a Broadcom BCM2711 SoC and a 4GB LPDDR4 (Raspberry Pi Limited.).…”

Section: D2 Collect Performance On Raspimentioning

confidence: 99%

HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark

Li,

Yu,

et al. 2021

Preprint

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HardWare-aware Neural Architecture Search (HW-NAS) has recently gained tremendous attention by automating the design of deep neural networks deployed in more resource-constrained daily life devices. Despite its promising performance, developing optimal HW-NAS solutions can be prohibitively challenging as it requires cross-disciplinary knowledge in the algorithm, micro-architecture, and device-specific compilation. First, to determine the hardware-cost to be incorporated into the NAS process, existing works mostly adopt either pre-collected hardware-cost look-up tables or device-specific hardware-cost models. The former can be time-consuming due to the required knowledge of the device's compilation method and how to set up the measurement pipeline, while building the latter is often a barrier for non-hardware experts like NAS researchers. Both of them limit the development of HW-NAS innovations and impose a barrier-to-entry to non-hardware experts. Second, similar to generic NAS, it can be notoriously difficult to benchmark HW-NAS algorithms due to their significant required computational resources and the differences in adopted search spaces, hyperparameters, and hardware devices. To this end, we develop HW-NAS-Bench, the first public dataset for HW-NAS research which aims to democratize HW-NAS research to non-hardware experts and make HW-NAS research more reproducible and accessible. To design HW-NAS-Bench, we carefully collected the measured/estimated hardware performance (e.g., energy cost and latency) of all the networks in the search spaces of both NAS-Bench-201 and FBNet, on six hardware devices that fall into three categories (i.e., commercial edge devices, FPGA, and ASIC). Furthermore, we provide a comprehensive analysis of the collected measurements in HW-NAS-Bench to provide insights for HW-NAS research. Finally, we demonstrate exemplary user cases to (1) show that HW-NAS-Bench allows non-hardware experts to perform HW-NAS by simply querying our premeasured dataset and (2) verify that dedicated device-specific HW-NAS can indeed lead to optimal accuracy-cost trade-offs. The codes and all collected data are available at https://github.com/RICE-EIC/HW-NAS-Bench.

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“…The proposed system uses noninvasive sensors to monitor vital parameters such as the heart rate, SpO2, and body temperature accurately. 12 The aim of this work consists in designing a smart and connected steering wheel to detect the physiological signals such as ECG and photoplethysmographic (PPG) signals and in implementing a device dedicated to the monitoring of the heart rate and its variability in order to detect possible tachycardia, bradycardia, or cardiac arrhythmia, as well as to detect possible respiratory decompensation (hypoxemia). When the driver experiences a complication or cardiac or respiratory failure, an alarm alerts him locally and information is transmitted instantly and automatically to a remote tele-vigilance station, which directs a medical team to the person behind the wheel.…”

Section: Articlementioning

confidence: 99%

Monitoring driver health status in real time

Cherif

Chérif

Benabdellah

et al. 2020

Review of Scientific Instruments

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Nowadays, surveillance systems have evolved significantly; hence, in order to meet the specific needs of the health sector and to monitor the patients' health conditions, intelligent systems have been proposed. These innovations represent a primordial role in road safety, which reduce the risk of traffic accidents. This paper describes an intelligent system design for remote monitoring (tele-monitoring) of a driver's health condition in real time. The measurement using new hardware and software devices is made possible through the contact between the driver contact and an intelligent steering wheel, which is coupled either to an integrated monitor or to a bluetooth link with a local Android smartphone. The driver's heart rate is calculated through the continuous collection of the electrocardiographic signal as well as the blood oxygen saturation SpO 2 by using the photoplethysmographic technique. Consequently, it is necessary to monitor the two vital functions of the driver, cardiac and respiratory activity. This information is transmitted to a remote tele-vigilance center in the case of abnormalities in these functions under the transmission control protocol/internet protocol involving a 4G/3G connection. The application is associated with the system that triggers high and low alarms locally and remotely in the events of tachycardia, bradycardia, or cardiac arrhythmia. Furthermore, another alarm is also triggered in the event of respiratory decompensation.

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Raspberry PI 3B+ Based Smart Remote Health Monitoring System Using IoT Platform

Cited by 6 publications

References 8 publications

Comparative analysis of different Operating systems for Raspberry Pi in terms of scheduling, synchronization, and memory management

Comparative analysis of different Operating systems for Raspberry Pi in terms of scheduling, synchronization, and memory management

HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark

Monitoring driver health status in real time

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