International Journal of P2P
Network Trends and Technology

Research Article | Open Access | Download PDF
Volume 16 | Issue 1 | Year 2026 | Article Id. IJPTT-V16I1P402 | DOI : https://doi.org/10.14445/22492615/IJPTT-V16I1P402

PulseIQ-Integrated Computational Intelligence for Implicit State Recognition

Gudala Babu Pradeep, Bandaru Esther Sunanda, Mohammed Maimoona Tanveer, Mycharla Bhavya Sree, Nambari Aswini, Nammi Venkata Chandini

Received Revised Accepted Published
19 Feb 2026 26 Mar 2026 13 Apr 2026 30 Apr 2026

Citation :

Gudala Babu Pradeep, Bandaru Esther Sunanda, Mohammed Maimoona Tanveer, Mycharla Bhavya Sree, Nambari Aswini, Nammi Venkata Chandini, "PulseIQ-Integrated Computational Intelligence for Implicit State Recognition," International Journal of P2P Network Trends and Technology (IJPTT), vol. 16, no. 1, pp. 8-15, 2026. Crossref, https://doi.org/10.14445/22492615/IJPTT-V16I1P402

Abstract

In recent times, people are paying more attention to their health, but stress and sleep-related issues are still increasing due to busy schedules, academic pressure, and improper rest. To address this problem, the proposed system PulseIQ focuses on monitoring stress and sleep conditions using Heart Rate (HR) and Heart Rate Variability (HRV). The system is designed using a pulse rate sensor connected to a NodeMCU (ESP8266) module, which continuously collects real-time heart rate data. The collected data is transmitted to a Python-based system where it is stored and processed for further analysis. From this data, HRV features are extracted to understand the user’s physiological conditions related to stress and sleep. Machine learning algorithms such as Decision Tree and Random Forest are applied to classify stress levels and estimate sleep states. The dataset used for training is updated regularly to improve the performance and accuracy of the model. A simple user interface is also developed, where users can enter their basic details like name, age, and gender. The system displays real-time predictions along with graphical representations, making it easy to understand the results. Overall, the proposed system provides an affordable and efficient solution for continuous health monitoring, helping users detect stress early and manage their sleep patterns effectively. This system can also be useful in future healthcare applications.

Keywords

Beats Per Minute (BPM) Analysis, Heart Rate Variability (HRV), Internet of Things (IoT), Machine Learning, NodeMCU ESP8266, Pulse Rate Sensor, Real-Time Monitoring, Sleep State Monitoring, Stress Level Monitoring.

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