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@article{180619,
        author = {Dr Soumya M Anakal and Divya Davanagere and Nishita N Madamshetty and Mahek Sultana Banghban},
        title = {Stress Monitoring System Using IOT},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {2376-2385},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180619},
        abstract = {This paper presents a comprehensive IoT
based stress monitoring system that integrates 
environmental and physiological sensing with an 
Adaptive Neuro-Fuzzy Inference System (ANFIS) 
algorithm 
for 
microcontroller 
stress 
is 
detection. 
interfaced 
An ESP32 
with a DHT11 
temperature–humidity sensor, a photoplethysmography
based pulse sensor, and a heart rate sensor to collect 
real-time data. These multimodal inputs are processed 
by an ANFIS model to estimate human stress levels. The 
system features a locally hosted web interface 
(HTML/CSS) on the ESP32, enabling real-time 
visualization of sensor readings and the inferred stress 
status. We evaluate expected performance based on 
literature benchmarks, anticipating around 90 % 
classification accuracy, with sensitivity and specificity in 
the 88–92 % range [2][3][4]. This work contributes a 
novel integration of ANFIS into IoT-based stress 
monitoring, demonstrating its potential for enhanced 
accuracy and adaptability in edge health devices. Future 
developments will focus on expanding stress level 
granularity and adding further biosignals.},
        keywords = {Stress monitoring, Internet of Things  (IoT), ESP32, DHT11, Pulse sensor, Heart rate  monitoring, Adaptive Neuro-Fuzzy Inference System  (ANFIS), Real-time web interface.},
        month = {June},
        }