Stress and Emotion Detection System Using IoT and Machine Learning with a Fuzzy Inference-Based Mental Health Risk Assessment Module - Research

Aug 5, 2026

A stress and emotion detection system (SEDS) is presented, combining machine learning, the Internet of Medical Things (IoMT), and fuzzy logic for mental health risk assessment. Particle Photon microcontrollers collect data from non-invasive biosensing devices, behavioral/emotional/cognitive stress indicators, and a facial identification sub-module to classify emotions such as happy, sad, angry, and nervous. Parameters are stored via IoT cloud services and accessed through a smartphone application using facial-expression analysis. Fuzzy logic categorizes stress threat levels from very low to extremely high and triggers referral notifications to mental health professionals when risk is high.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

Stress in educational settings

Impact of stress on physical and cognitive health

COVID-19 related stress

System overview

Purpose and goals

Core technologies and expected improvements

Stress and Emotion Detection System Using IoT and Machine Learning with a Fuzzy Inference-Based Mental Health Risk Assessment Module Analene Montesines Nagayo Engineering Department, University of Technology and Applied Sciences, Al Musanna, Al Muladdah, Sultanate of Oman. e-mail: [analene@act.edu.om](analene@act.edu.om) Mahmood Zayid Al Ajmi Engineering Department, University of Technology and Applied Sciences, Al Musanna, Al Muladdah, Sultanate of Oman. [e-mail: mahmood@act.edu.om](e-mail: mahmood@act.edu.om) Mai Mubarak Al Saadi Engineering Department, University of Technology and Applied Sciences, Al Musanna, Al Muladdah, Sultanate of Oman. [e-mail: maimubarak@act.edu.om](e-mail: maimubarak@act.edu.om) Fatma Saleh Al Buradai Engineering Department, University of Technology and Applied Sciences, Al Musanna, Al Muladdah, Sultanate of Oman. e-mail: [fatmaalburadai@act.edu.om](fatmaalburadai@act.edu.om) Abstract— This research paper describes the conceptualization, deployment, and application of a stress and emotion detection system (SEDS) that utilizes emerging technologies including machine learning, the Internet of Medical Things (IoMT), and fuzzy logic. Particle Photon microcontrollers were used to collect and analyze data from non-invasive biosensing devices, input switches for behavioral, emotional, and cognitive stress indicators, and input signals generated by the machine learning-based facial identification sub-module, which detects an individual's emotional states (happy, sad/upset, angry/irritable, or nervous/scared) . The acquired parameters were stored and made readily accessible using IoT cloud services and dedicated mobile phone applications. The emotional condition of an individual was assessed by the utilization of the Personal Image Classifier tool within the MIT App Inventor, which involved the analysis of facial expressions. Furthermore, the SEDS was integrated with a module for mental health risk assessment which employs fuzzy logic to categorize the user's stress-related psychological health threat level as very low, low, moderate, high, or extremely high based on the data collected. The customized smartphone application provided users specific recommendations for effectively managing their mental health, based on stress level assessments. When the SEDS determined that the level of mental health risk posed by stress was high, it automatically generated a referral notification and transmitted it via text message to the mental health care professional, facilitating the provision of appropriate psychological counseling. Based on the collected data, the stress and emotion detection system produced results that were comparable to those from the DASS21 stress scale. The system demonstrated an improved accuracy of 90% in a test involving thirty individuals who volunteered to participate. The machine learning-based emotion detection system achieved a classification accuracy of 86.67% in correctly detecting happy, neutral, sad, angry, or nervous feelings through the analysis of facial expressions. This research is designed to provide mental health care professionals such as guidance counselors, psychologists, and psychiatrists with the resources essential to facilitate the evaluation and treatment of mental health issues. It also aims to raise people's understanding and detection of psychological conditions through enhanced awareness initiatives. Keywords-machine learning, internet of medical things, fuzzy logic, mental health risk. I. INTRODUCTION Stress can manifest within the educational setting for both teachers and students due to a range of factors. These factors encompass heightened academic demands, the burden of overwhelming workloads, impending assessments, and the pervasive culture of peer competition [1] . Teachers encounter stress as a result of various reasons, including the demanding nature of their workload, the uncertainty surrounding their employm

FAQs

What technologies does the proposed SEDS use for detection and assessment?

The system integrates machine learning, the Internet of Medical Things (IoMT), and fuzzy logic to detect stress and emotional states and to assess mental health risk.

How are users’ emotions and stress indicators captured in the system?

Data are collected through non-invasive biosensing devices, input switches for behavioral, emotional, and cognitive stress indicators, and a facial identification sub-module that analyzes facial expressions.

How does the system determine mental health risk level and what happens when risk is high?

Fuzzy logic categorizes stress-related psychological threat levels from very low to extremely high. When risk is high, the application generates a referral notification and sends it to a mental health care professional via text message.

Aug 5, 2026

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