REAL-TIME ADAPTIVE STRESS DETECTION USING PHYSIOLOGICAL SIGNALS FOR HIGH-RISK OPERATIONS

PRASANNA KUMARI PANAMALA; VAKALAPUDI KRISHNA PRATAP; KAPU NAGESWARA RAO

doi:10.36948/ijfmr.2025.v07i03.47295

REAL-TIME ADAPTIVE STRESS DETECTION USING PHYSIOLOGICAL SIGNALS FOR HIGH-RISK OPERATIONS

Author(s)	Ms. PRASANNA KUMARI PANAMALA, Mr. VAKALAPUDI KRISHNA PRATAP, Dr. KAPU NAGESWARA RAO
Country	India
Abstract	Accurate and real-time stress detection is essential to ensure safety and maintain peak performance in hazardous occupations. This study presents a tailored, real-time tension detection system that applies machine learning techniques to physiological inputs. The system collects data on heart rate variability (HRV), electrodermal activity (EDA), and skin temperature using wearable sensors. A machine learning procedure that involves feature extraction, normalization, and model training is used to dynamically classify stress levels. Personalized models are created for each individual using supervised learning techniques like support vector machines (SVM), Random Forests (RF), and sophisticated machine learning methods (CNN LSTM) to account for physiological differences. The system steadily increases accuracy by adjusting and learning from new data. Experiments carried out in hazardous simulation environments have demonstrated that the proposed method can identify stress in real time, offering crucial information for preventative measures. This study builds intelligent safety solutions for high-risk occupations using physiological sensing and adaptive machine learning.
Keywords	Support Vector Machines (SVM), physiological signals, machine learning, and stress detection. deep learning
Field	Engineering
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-06-28
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.47295
Short DOI	https://doi.org/g9rnxh

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REAL-TIME ADAPTIVE STRESS DETECTION USING PHYSIOLOGICAL SIGNALS FOR HIGH-RISK OPERATIONS

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