Aug 2026· International Journal for Research in Applied Science and Engineering Technology· Vol 14, pp. 157-162· 0 citations
TL;DR
A machine learning-based approach for assessing mental stress using wearable sensor data that can provide accurate and continuous stress monitoring, supporting applications in healthcare, workplace monitoring, and personal wellness management is presented.
Abstract
Mental stress is a major health issue that affects an individual's physical and psychological well-being. Conventional
stress assessment techniques mainly depend on self-reporting methods and clinical evaluations, which are subjective and
unsuitable for continuous monitoring. With the advancement of wearable sensor technology, physiological signals such as heart
rate, electrodermal activity, skin temperature, and body movement can be collected in real time. This paper presents a machine
learning-based approach for assessing mental stress using wearable sensor data. The proposed system involves data collection,
preprocessing, feature extraction, and classification using machine learning algorithms. The developed framework can provide
accurate and continuous stress monitoring, supporting applications in healthcare, workplace monitoring, and personal wellness
management.
Stress is a pervasive issue impacting physical and mental health in modern society, contributing to conditions such as anxiety, depression, and cardiovascular diseases. The integration of wearable technology with artificial intelligence (AI) and machine learning (ML) offers a promising approach for real-time stress det...
Rachana Parikh, V. Dahiya· Journal of Applied Informati...· 0 citations
Anxiety is an increasingly prevalent concern among graduate students, highlighting the need for objective approaches that complement conventional self-report assessments. Wearable sensing technologies combined with machine learning offer opportunities to characterize anxiety-related physiological patterns during au...
Adara Pulido-Sánchez, C. Sotelo, Jessica Alejandra Ruiz-Ramírez et al.· Frontiers in Education· 0 citations
Machine learning-based human stress detection from sleep patterns leverages wearable-
generated physiological and behavioural sleep indicators, including sleep duration, heart rate
variability, nocturnal body movement, and sleep stage distribution, to enable objective,
continuous evaluation of stress state. Nevertheles...
R. Jeevadharshini, M. Kannan· ITEGAM- Journal of Engineeri...· 0 citations
Depression is a main global cause of emotional disability, impacting cognitive function, social interactions, and quality of life. Traditional assessment methods depend on subjective, time-consuming self-reported surveys and clinical interviews, and challenging for continuous monitoring and intervention. To address the...
Jin Xue, Herdian· Discover Artificial Intellig...· 0 citations
Stress is a physiological and psychological response that can affect the well-being and daily functioning of an individual. In this study, we propose an IoT-enabled smart cap for real-time stress monitoring using multiple physiological signals. The system uses electroencephalography (EEG), electrocardiography (ECG), ga...
Bairavanathan Maha Sri, Kala Devi Managuran, R. Thinakaran et al.· International Journal of Adv...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
