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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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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.
Real-time monitoring tool for heart rate and oxygen saturation in young adults Fatimah Abdul Razak, Siti; Jia Wee, Yap; Yogarayan, Sumendra; Noor Masidayu Sayed Ismail, Sharifah; Fikri Azli Abdullah, Mohd
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6371

Abstract

Health monitoring is crucial to maintain optimal well-being, especially for young adults. Wearable sensors have become popular for collecting healthcare data, but there are concerns regarding their reliability and safety, particularly with wireless sensors that use radio-frequency (RF) based devices. Researchers have proposed real-time monitoring systems for measuring heart rate beats per minute (BPM) and blood oxygen saturation (SpO2) saturation levels, but more studies are needed to determine the accuracy and user acceptance of these tools among young adults. To address these concerns, this study proposes a real-time monitoring tool that incorporates MAX 30100 sensors to collect heart rate BPM and SpO2 data. The collected data is then connected to a visualization platform, i.e., InfluxDB and Grafana, to provide valuable insights of the body’s physiological state. By testing the feasibility and usability of the tool, we found motivating differences in resting heart rates and changes in heart rate after activity between male and female participants. By developing this real-time monitoring tool and investigating gender-specific differences in heart rate and activity-induced changes, our study contributes to the advancement of health monitoring technologies for young adults, ultimately promoting personalized healthcare and well-being.
Vehicle Safety Application through the Integration of Flood Detection and Safe Overtaking in Vehicular Communication Seng, Kwang Chee; Abdul Razak, Siti Fatimah; Yogarayan, Sumendra
Civil Engineering Journal Vol 10, No 9 (2024): September
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2024-010-09-015

Abstract

Road safety in Malaysia is a major concern due to frequent floods and accidents caused by overtaking. These issues result in significant injuries and losses. In this paper, we introduce a new system called the Safe Driving Tool (SDT). The SDT integrates a Flood Detection System (FDS) and a Vehicle Overtaking System (VOS) using Long-Range (LoRa) communication technology. The FDS continuously monitors water levels in flood-prone areas. It alerts drivers about potential hazards through vehicle-to-infrastructure (V2I) communication. Simultaneously, the VOS enables safe overtaking maneuvers. It does this by exchanging information with nearby vehicles through vehicle-to-vehicle (V2V) communication. Through testing and experimentation, we have shown that the SDT system effectively reduces accident risks and losses associated with floods and overtaking. The system's performance under various conditions confirms the reliability and effectiveness of LoRa communication technology in enhancing vehicular safety. This study represents a significant advancement in road safety. It combines flood detection and overtaking assistance into a single unified system, addressing two major causes of road accidents in Malaysia. The integration of V2I and V2V communication provides a comprehensive solution that improves driver awareness and decision-making. This ultimately leads to safer driving environments and enhanced driver convenience. Doi: 10.28991/CEJ-2024-010-09-015 Full Text: PDF
Applications of internet of things for monitoring drivers-a comprehensive study Yogarayan, Sumendra; Razak, Siti Fatimah Abdul; Azman, Afizan; Abdullah, Mohd. Fikri Azli
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1599-1606

Abstract

Driving is a complex task that involves interacting adequately with the vehicle and the environmental changes simultaneously. Drivers' health is an essential factor in determining performance outcomes and enhancing road safety. It is a known reality that drivers with sudden health complications are most likely to be involved in road accidents and suffer several injuries. Besides that, drunk driving is another aspect of a significant public health issue, where drivers under the influence of alcohol show a clear vision loss and vehicle control. The internet of things (IoT) is a trendsetting advancement in which all sensor data can be collected in the cloud. In this paper, an active monitoring tool is developed to record the driver's heart rate if these readings reach vital values while on the move. Additionally, the tool monitors the driver's alcohol concentration, and if it rises beyond a certain threshold, an alarm is sent to the designated emergency contact. The tool has been tested and has been found to work satisfactorily.
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.
Driver Drowsiness and Alcohol Detection for Automotive Safety Systems Sivaprakasam, Avenaish; Yogarayan, Sumendra; Mogan, Jashila Nair; Abdul Razak, Siti Fatimah; Azman, Afizan; Raman, Kavilan
Civil Engineering Journal Vol. 11 No. 7 (2025): July
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-07-03

Abstract

Driver drowsiness and alcohol impairment are major causes of traffic accidents, making road safety a main concern. This study highlights the importance of addressing these issues through improved driver monitoring technologies. A prototype combining MQ-3 alcohol sensors, and facial detection was created, integrating with IoT via a Raspberry Pi to monitor and alert on drowsiness and alcohol levels. The developments use the NTHU-DDD dataset, which supports a supervised learning approach to develop a reliable drowsiness detection model. The study explored various machine learning algorithms such as Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), K-nearest neighbors (KNN), Gradient Boosting Classifier, and Gaussian Naive Bayes, with Random Forest and Gradient Boosting emerging as top performers, particularly suited to complex non-linear data. The system effectively used supervised learning techniques to differentiate drowsy and non-drowsy images and exhibited consistent accuracy in detecting drowsiness, especially when the driver’s face was centered. However, accuracy decreased when faces were tilted, highlighting areas for refinement. Moreover, the environmental tests on the MQ-3 sensor demonstrated its sensitivity to alcohol presence, even distinguishing the intensity based on beverage type and concentration. The findings underscore the efficacy of using sensor-based technologies in real-world conditions and provide a foundation for optimizing the system's detection capabilities across various scenarios.
IoT-enabled Edge Impulse approach for heat stress prediction in outdoor settings Ke Yin, Lim; Yogarayan, Sumendra; Abdul Razak, Siti Fatimah; Sayeed, Md. Shohel; Bukar, Umar Ali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3934-3944

Abstract

Several international organizations of public health or countries have predicted the rise of heat-related illness cases due to climate change, which result high environment temperature. Previous studies of heat-related illness prediction using internet of things (IoT) and machine learning (ML) are mainly focusing on early detection or prediction of heat stroke incidence. To overcome the problem of heat stress prediction in outdoor settings, especially for an individual, the objective of this study is to identify a prediction method for heat stress using IoT technology and analyze the accuracy of the identified prediction model. Arduino nano 33 BLE sense with Bluetooth low energy (BLE) connectivity, HTS221 embedded environment temperature and humidity sensor, MLX90614 skin temperature sensor, and MAX30100 heart rate sensor were used to build IoT based wearable device. Besides, Python language is used for data pre-processing and data labelling after getting the sensor data from wearable device. Lastly, model training using neural network algorithms was directed in Edge Impulse. The result shows 94.6% of training accuracy with the loss of 0.27 and 90.22% of accuracy in testing set.
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