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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Civil Engineering Journal Indonesian Journal of Electrical Engineering and Computer Science
Yogarayan, Sumendra
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : International Journal of Electrical and Computer Engineering

 1 
Driver-centered pervasive application for heart rate measurement Abdul Razak, Siti Fatimah; Jun Tong, Yong; Yogarayan, Sumendra; Sayed Ismail, Sharifah Noor Masidayu; Chia Sui, Ong
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp1176-1184

Abstract

People spend a significant amount of time daily in the driving seat and some health complexity is possible to happen like heart-related problems, and stroke. Driver’s health conditions may also be attributed to fatigue, drowsiness, or stress levels when driving on the road. Drivers’ health is important to make sure that they are vigilant when they are driving on the road. A driver-centered pervasive application is proposed to monitor a driver’s heart rate while driving. The input will be acquired from the interaction between the driver and embedded sensors at the steering wheel, which is tied to a Bluetooth link with an Android smartphone. The driver can view his historical data easily in tabular or graph form with selected filters using the application since the sensor data are transferred to a real-time database for storage and analysis. The application is coupled with the tool to demonstrate an opportunity as an aftermarket service for vehicles that are not equipped with this technology.
Heat stroke prediction: a perspective from the internet of things and machine learning approach Ke Yin, Lim; Yogarayan, Sumendra; Abdul Razak, Siti Fatimah; Ali Bukar, Umar; Sayeed, Md. Shohel
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3427-3433

Abstract

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.
Advancement in driver drowsiness and alcohol detection system using internet of things and machine learning Sivaprakasam, Avenaish; Yogarayan, Sumendra; Mogan, Jashila Nair; Razak, Siti Fatimah Abdul; Abdullah, Mohd. Fikri Azli; Azman, Afizan; Raman, Kavilan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3477-3493

Abstract

Globally traffic accidents are influenced by factors such as drowsiness and alcohol consumption. Consequently, there has been a considerable focus on the development of detection systems as part of ongoing efforts to mitigate these risks. This review paper aims to offer a comprehensive analysis of various drowsiness and alcohol detection methods. The paper particularly emphasizes drowsiness and alcohol detection methods, including those centered on sensor-based approaches, physiological-based techniques, and visual analysis of the eye and mouth state. The aim is to evaluate their method, effectiveness and highlight recent advancements within this domain. Additionally, this review paper evaluates the research gaps of these detection methods, considering factors such as precision, sensitivity, specificity, and adaptability to different environmental conditions.
Co-Authors Abdul Razak, Siti Fatimah Abdullah, Mohd. Fikri Azli Ali Bukar, Umar Azman, Afizan Bukar, Umar Ali Chia Sui, Ong Fatimah Abdul Razak, Siti Fikri Azli Abdullah, Mohd Jia Wee, Yap Jun Tong, Yong Ke Yin, Lim Mogan, Jashila Nair Noor Masidayu Sayed Ismail, Sharifah Raman, Kavilan Sayed Ismail, Sharifah Noor Masidayu Sayeed, Md. Shohel Seng, Kwang Chee Siti Fatimah Abdul Razak Sivaprakasam, Avenaish