In daily life, a person is exposed to many negative factors and emotional states as arising from financial difficulties, working life and personal responsibilities. The aim of this study is to detect stress resulted …

Mar 28, 2025 · Indicators such as heart rate (HR), heart rate variability (HRV), blood volume pulse (BVP), electrodermal activity (EDA), skin temperature (ST), and motion activity are widely recognized …

Apr 2, 2018 · In order to detect stress of people effectively, a lot of research has been undertaken. Zhai [6] and Angus [7] created an automated system of emotional stress assessment by monitoring and …

Jan 24, 2024 · Wearable devices offer a means of real-time and ongoing data collection, facilitating personalized stress monitoring. Therefore, we collected physiological signals (blood pressure volume …

ns of EDA, BVP, ST, and their fusion. Our proposed model outperforms traditional Machine Learning methods as well as being 1:63% more accurate than the state-of-the-ar.

Aug 30, 2022 · In this research, we employ the bio-signals of the WESAD dataset, EDA, BVP, and ST signals, that can be recorded from separate sensors integrated on a low-cost consumer-grade …

Feb 20, 2021 · The main signals considered in current classification are electro-dermal activity (EDA) and blood volume pulse (BVP) signals.

In this work, we present a lightweight deep learning framework that integrates dual-branch convolutional neural networks (CNN) with gated recurrent units (GRU) for real-time stress detection from …

A binary definition of stress (stress, non-stress), as well as a three-class definition (baseline, stress, and amusement), are tested. However, all the tested algorithms are based on single sequence inputs, …

Jun 9, 2022 · This research aimed to detect stress using a stacking model based on machine learning algorithms using chest-based features from the Wearable Stress and Affect Detection (WESAD) …