Development and Performance Evaluation of an IoT-Integrated Breath Analyzer

Abd Alghani Khamis, Aida Idris, Abdallah Abdellatif, Noor Ashikin Mohd Rom, Taha Khamis, Mohd Sayuti Ab Karim (), Shamini Janasekaran and Rusdi Bin Abd Rashid
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Abd Alghani Khamis: Department of Mechanical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Aida Idris: Department of Management, Faculty of Business and Economics, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Abdallah Abdellatif: Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Noor Ashikin Mohd Rom: Faculty of Management, Multimedia University, Cyberjaya 63100, Malaysia
Taha Khamis: Center for Applied Biomechanics (CAB), Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
Mohd Sayuti Ab Karim: Centre of Advanced Manufacturing and Material Processing (AMMP), Department of Mechanical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Shamini Janasekaran: Centre for Advanced Materials and Intelligent Manufacturing, Faculty of Engineering, Built Environment & IT, SEGi University Sdn Bhd, Petaling Jaya 47810, Malaysia
Rusdi Bin Abd Rashid: Department of Psychological Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur 50603, Malaysia

IJERPH, 2023, vol. 20, issue 2, 1-26

Abstract: Although alcohol consumption may produce effects that can be beneficial or harmful, alcohol consumption prevails among communities around the globe. Additionally, alcohol consumption patterns may be associated with several factors among communities and individuals. Numerous technologies and methods are implemented to enhance the detection and tracking of alcohol consumption, such as vehicle-integrated and wearable devices. In this paper, we present a cellular-based Internet of Things (IoT) implementation in a breath analyzer to enable data collection from multiple users via a single device. Cellular technology using hypertext transfer protocol (HTTP) was implemented as an IoT gateway. IoT integration enabled the direct retrieval of information from a database relative to the device and direct upload of data from the device onto the database. A manually developed threshold algorithm was implemented to quantify alcohol concentrations within a range from 0 to 200 mcg/100 mL breath alcohol content using electrochemical reactions in a fuel-cell sensor. Two data collections were performed: one was used for the development of the model and was split into two sets for model development and on-machine validation, and another was used as an experimental verification test. An overall accuracy of 98.16% was achieved, and relative standard deviations within the range from 1.41% to 2.69% were achieved, indicating the reliable repeatability of the results. The implication of this paper is that the developed device (an IoT-integrated breath analyzer) may provide practical assistance for healthcare representatives and researchers when conducting studies involving the detection and data collection of alcohol consumption patterns.

Keywords: alcohol detection; breath analyzer; Internet of Things; HTTP; GSM; cellular IoT; linear regression; fuel-cell sensors; alcohol in breath (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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