Towards Self-Aware Heatstroke Early-Warning System Based on Healthcare IoT

Karmani, Vivekanand; Chandio, Aftab Ahmed; Karmani, Priyanka; Chandio, Moiz; Korejo, Imtiaz Ali

doi:10.1109/worlds4.2019.8904006

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…In addition, a warning will be displayed through light-emitting diode (LED) and buzzer based on the risk of getting heatstroke. Apart from that, Karmani et al [44] developed hand glove as an IoT device to create a heatstroke early-warning system. Arduino board with Wi-Fi module is used as a microcontroller.…”

Section: Related Workmentioning

confidence: 99%

Heat stroke prediction: a perspective from the internet of things and machine learning approach

Ke Yin,

Yogarayan,

Abdul Razak

et al. 2024

IJECE

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With the increasing occurrence of heat-related illnesses due to rising temperatures worldwide, there is a need for effective detection and prediction systems to mitigate the risks. Heat stroke, a life-threatening condition occurs when the body’s temperature exceeds 104 degrees Fahrenheit (40 degrees Celsius). It can happen due to prolonged exposure to temperatures. When the body struggles to cool itself down adequately. The internet of things (IoT) and machine learning (ML) are two advancing technologies that have the potential to revolutionize industries and enhance our lives in numerous ways. Currently, monitoring devices are primarily used to diagnose when individuals suffering from heatstroke are at the location. This paper delves into the exploration of utilizing the IoT and ML algorithms to predict heat strokes. It reviews existing studies in this field, focusing on how IoT has been deployed and the application of machine learning techniques. The research aims to define the integration of IoT devices and ML algorithms that has a great potential to detect and predict heat-related illnesses such as heat stroke at an early stage.

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Section: Related Workmentioning

confidence: 99%

Heat stroke prediction: a perspective from the internet of things and machine learning approach

Ke Yin,

Yogarayan,

Abdul Razak

et al. 2024

IJECE

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“…Our literature survey revealed that self-awareness was considered as a preliminary requirement in building secure and optimized systems (Gelenbe et al, 2020), fog and mist computing systems (Preden et al, 2015), self-adaptive and trustworthy computing systems (Habib and Marimuthu, 2020), and healthcare IoT (Karmani et al, 2019). In this work, we have derived a self-aware model, which would be aware of the defined uncertainties and their impacts on WSN data transmission.…”

Section: Related Workmentioning

confidence: 99%

A Fuzzy Framework for Self-Aware Wireless Sensor Networks

Habib¹,

Marimuthu²

2022

JER is an international, peer-reviewed journal that publishes f

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We have developed a fuzzy logic framework to simulate self-awareness in wireless sensor networks (WSNs) that envisages data transmission losses due to abnormalities in the operating environment. Fuzzy logic is selected to model the awareness, because it requires less data to encode, and occupies less memory, making it suitable for sensor nodes with limited resources. We view WSN as a multi-layered infrastructure, where a central-server in the top-layer manages the gateways in the intermediate-layer, and the gateways are aggregating the raw sensed data from the bottom layer’s sensors. The self-awareness is simulated using Mamdani Fuzzy inference system (FIS) that utilizes a custom-defined membership functions of the selected parameters, such as temperature, humidity, wind speed, and battery residual-level to guide WSN through its data transmissions. The input and output parameters are defined with three linguistic variables (low, medium and high) and a predetermined set of rules is applied to the selected FIS to determine the impact level. The impact is mapped into packet error rate and the gateway then decides whether to carry out the data transmissions while being aware of the selected uncertainties and their impact. The experimental results on a multivariate dataset integrating real-time meteorological data with randomly generated battery voltage revealed that the proposed FIS framework demonstrated 38% energy savings by non-executing unworthy communications against normal transmissions.

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“…There are many popular DT algorithms C4.5, ID3, CART, CHAID, J48 and MARS [46][47]. DT algorithms are widely being applied in the Healthcare Informatics [109]. Tayefi…”

Section: Machine Learning Healthcare Informatics (Ml-hi) Algorithmsmentioning

confidence: 99%

Taxonomy on Healthcare System Based on Machine Learning Approaches: Tuberculosis Disease Diagnosis

Karmani¹,

Chandio²,

Karmani³

et al. 2020

IJCDS

Self Cite

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Towards Self-Aware Heatstroke Early-Warning System Based on Healthcare IoT

Cited by 5 publications

References 14 publications

Heat stroke prediction: a perspective from the internet of things and machine learning approach

Heat stroke prediction: a perspective from the internet of things and machine learning approach

A Fuzzy Framework for Self-Aware Wireless Sensor Networks

Taxonomy on Healthcare System Based on Machine Learning Approaches: Tuberculosis Disease Diagnosis

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