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Predictive Modeling of Heat Stress Patterns and Hotspot

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Reg. ID : 17521
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Huraian

In recent years, heat-related illnesses have become a significant public health concern, particularly during periods of hot weather. The alarming increase in heat-related cases calls for effective measures to mitigate the risks associated with heat stress. This study presents the development of a predictive model using machine learning techniques to analyze heat stress patterns. The detection tool comprises of Arduino Nano 33 BLE Sense, equipped with a heartrate sensor, a skin temperature sensor, and an environment temperature and humidity sensor. These sensors provide real-time data on physiological parameters and environmental conditions, enabling the tool to accurately assess heat stress levels. The machine learning algorithms employed in the predictive model analyze the collected data to identify patterns and correlations between physiological responses and heat stress. The tool aims to support public health authorities in implementing proactive interventions to prevent heat stress. By providing timely and accurate information on heat stress patterns, this tool enables the identification of high-risk areas and populations, allowing for targeted interventions, public awareness campaigns, and resource allocation.

Highlights

The tool incorporates several features that contribute to its effectiveness and potential impact. Here are the highlights of its important features: Integration of IoT Technology: The tool leverages IoT technology to collect real-time data from sensors, including heart rate, skin temperature, and environmental conditions. This integration enables continuous monitoring of physiological parameters and environmental factors, providing a comprehensive understanding of heat stress levels. Machine Learning Algorithms: The tool utilizes machine learning algorithms to analyze the collected data and identify patterns and correlations associated with heat stress. By training the model on historical data and incorporating relevant features, the system can accurately predict heat stress levels and potential risks of heat-related illnesses. High Accuracy and Precision: Through the combination of IoT technology and machine learning algorithms, the tool aims to achieve high accuracy and precision in heat stress predictions. By considering multiple physiological and environmental parameters, it provides a more comprehensive and reliable assessment compared to traditional methods, enhancing decision-making processes. Customizable and Scalable Solution: The tool is designed to be customizable and scalable, allowing for adaptation to various settings and user requirements. It can be tailored to specific geographical locations, occupational environments, or vulnerable populations, making it a versatile solution that can address a wide range of heat stress scenarios. Decision Support System: The tool serves as a decision support system, assisting public health authorities, urban planners, and employers in making informed decisions related to heat stress management. By providing actionable insights and recommendations, it enables the implementation of targeted interventions, resource allocation, and policy development.

Contact Person/Inventor

Name Email Contact Phone
Ooi Shih Yin syooi@mmu.edu.my 0124290731

Award

Award Title Award Achievement Award Year Received
Malaysian Technology Expo MTE 2023 2023

Video

Additional Document

Attachment Saiz
file-1711009261.pdf (255.54 KB) 255.54 KB
file-1711009261.pdf (297.96 KB) 297.96 KB
file-1711009261.pdf (159.68 KB) 159.68 KB
file-1711009261.pdf (354.97 KB) 354.97 KB

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