Journal of Innovative Image Processing
IRO Journals
Volume 8 — Issue 1 — March 2026

Sustainable Detection and Monitoring of Psychiatric Risk and Abnormal Behavioral States using Multimodal Biosensor Data

https://doi.org/10.36548/jiip.2026.1.022
Pages: 415-438
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Akshaya D. Shetty
Department of Electronics and Communication Engineering, S.E.A College of Engineering & Technology, Bengaluru, Karnataka, India
Usha Desai
Department of Electronics and Communication Engineering, S.E.A College of Engineering & Technology, Bengaluru, Karnataka, India
Akash Saxena
Department of Electrical Engineering, School of Engineering and Technology, Central University of Haryana, Haryana, India
March 2026
Volume 8 — Issue 1
Journal of Innovative Image Processing
PDF

How to Cite

Shetty, Akshaya D., Usha Desai, and Akash Saxena. 2026. “Sustainable Detection and Monitoring of Psychiatric Risk and Abnormal Behavioral States Using Multimodal Biosensor Data”. Journal of Innovative Image Processing 8 (1): 415-38. https://doi.org/10.36548/jiip.2026.1.022.
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Keywords

— Electrodermal Activity
— Skin Conductance
— Galvanic Skin Response (GSR)
— Biofeedback
— Endosulfan Disabled Individuals
— Kernel Density Estimation (KDE)
Published: 25-03-2026

Abstract

Subjects exhibiting neurological, developmental, and behavioral disorders can manifest stress, agitation, and emotional deregulation. The prolonged emotional deregulation increases the development of depression, anxiety disorders and in extreme cases, this persistent deregulation contributes to onset of psychiatric symptoms. This study proposes a novel approach for the detection of psychiatric risk and abnormal behavioral states using multimodal biosensors integration namely Pulse, Electromyography (EMG), and Galvanic Skin Response (GSR) sensors. This experiment was conducted in a skill-training centre for Endosulfan victims aged 13±2 years with the necessary care and consent. Significant differences in electro-dermal signals such as GSR, pulse, and EMG were observed among 66 subjects, with multiple trials conducted for each subject, resulting in a dataset comprising 793 healthy and 750 high risk samples, respectively.  Data acquisition was performed by integrating sensors onto the Arduino UNO microcontroller and the Cool-Term software tool. In total, fifteen nonlinear transform domain features were extracted. The confidence intervals of the features were verified using the t-test (p<0.05). A classification model was created using machine learning algorithms, namely Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), XGBoost, and Neural Networks. Among these, Random Forest, XGBoost, and Neural Networks achieved a sustainable accuracy of 97.92%, while the ensemble model achieved an accuracy of 98.32%.  This approach of biosensor monitoring helps to consistently predict the behavioral states of differently abled individuals and supports healthcare providers in the early identification of behavioral and physiological markers indicative of psychiatric risks, contributing to a sustainable mental health ecosystem.

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