A Low Cost EDA-based Stress Detection Using Machine Learning

Hosseini, Elahe; Fang, Ruijie; Zhang, Ruoyu; Parenteau, Anna; Hang, Sally; Rafatirad, Setareh; Hostinar, Camelia E.; Orooji, Mahdi; Homayoun, Houman

doi:10.1109/bibm55620.2022.9995093

Cited by 11 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some studies utilized machine learning to classify discomfort from EDA signals [8,9,17]. Based on these approaches, future research should focus on using machine learning to evaluate discomfort at binding parts.…”

Section: Discussionmentioning

confidence: 99%

“…Posada-Quintero et al [16] found that the mean SCL (skin conductance level) and the number of SCR peaks significantly increased when applying physical stimuli, postural stimulation, and the cold pressor test. Hosseini et al [17] demonstrated that 87 features extracted from EDA signals in the WESAD (wearable stress and affect detection) dataset were sufficient to classify stress and non-stress groups with over 80% accuracy. They also found that five features-the mean SCL, maximum SCL, number of SCR peaks, maximum SCR amplitude, and standard deviation of SCR rise time-were adequate to classify the two groups with 97% accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Measurements of Electrodermal Activity, Tissue Oxygen Saturation, and Visual Analog Scale for Different Cuff Pressures

Kim,

Han,

Jung

et al. 2024

Sensors

View full text Add to dashboard Cite

The quantification of comfort in binding parts, essential human–machine interfaces (HMI) for the functioning of rehabilitation robots, is necessary to reduce physical strain on the user despite great achievements in their structure and control. This study aims to investigate the physiological impacts of binding parts by measuring electrodermal activity (EDA) and tissue oxygen saturation (StO2). In Experiment 1, EDA was measured from 13 healthy subjects under three different pressure conditions (10, 20, and 30 kPa) for 1 min using a pneumatic cuff on the right thigh. In Experiment 2, EDA and StO2 were measured from 10 healthy subjects for 5 min. To analyze the correlation between EDA parameters and the decrease in StO2, a survey using the visual analog scale (VAS) was conducted to assess the level of discomfort at each pressure. The EDA signal was decomposed into phasic and tonic components, and the EDA parameters were extracted from these two components. RM ANOVA and a post hoc paired t-test were used to determine significant differences in parameters as the pressure increased. The results showed that EDA parameters and the decrease in StO2 significantly increased with the pressure increase. Among the extracted parameters, the decrease in StO2 and the mean SCL proved to be effective indicators. Such analysis outcomes would be highly beneficial for studies focusing on the comfort assessment of the binding parts of rehabilitation robots.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measurements of Electrodermal Activity, Tissue Oxygen Saturation, and Visual Analog Scale for Different Cuff Pressures

Kim,

Han,

Jung

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…The study was carried out considering the data available for a single subject (intra-subject classification) as well as taking into account all available data (intersubject classification). Similarly, Hosseini et al [17] focused on stress detection using normalized EDA signals and employing machine learning models such as AdaBoost, RF, and Support Vector Machines (SVM). Asif et al [20] examined the effect of music tracks on human stress levels using EEG signals from twenty-seven subjects.…”

Section: Introductionmentioning

confidence: 99%

Contribution of EEG biosignals for Stress Detection

Fernandez,

Martínez,

Innocenti

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In recent years, the rapid advancement of wearable technology has opened a new era for continuous health monitoring and bioelectrical signal analysis [1]. Wearable devices have become important in providing real-time bioinformation, enhancing communication, and enabling health monitoring [2,3]. This evolution has been made possible through the integration of sophisticated textiles, materials, and microelectronics [4].…”

Section: Introductionmentioning

confidence: 99%

Design, Fabrication, and Evaluation of 3D Biopotential Electrodes and Intelligent Garment System for Sports Monitoring

Shen,

Wang,

Koncar

et al. 2024

Sensors

View full text Add to dashboard Cite

This study presents the development and evaluation of an innovative intelligent garment system, incorporating 3D knitted silver biopotential electrodes, designed for long-term sports monitoring. By integrating advanced textile engineering with wearable monitoring technologies, we introduce a novel approach to real-time physiological signal acquisition, focusing on enhancing athletic performance analysis and fatigue detection. Utilizing low-resistance silver fibers, our electrodes demonstrate significantly reduced skin-to-electrode impedance, facilitating improved signal quality and reliability, especially during physical activities. The garment system, embedded with these electrodes, offers a non-invasive, comfortable solution for continuous ECG and EMG monitoring, addressing the limitations of traditional Ag/AgCl electrodes, such as skin irritation and signal degradation over time. Through various experimentation, including impedance measurements and biosignal acquisition during cycling activities, we validate the system’s effectiveness in capturing high-quality physiological data. Our findings illustrate the electrodes’ superior performance in both dry and wet conditions. This study not only advances the field of intelligent garments and biopotential monitoring, but also provides valuable insights for the application of intelligent sports wearables in the future.

show abstract

A Low Cost EDA-based Stress Detection Using Machine Learning

Cited by 11 publications

References 14 publications

Measurements of Electrodermal Activity, Tissue Oxygen Saturation, and Visual Analog Scale for Different Cuff Pressures

Measurements of Electrodermal Activity, Tissue Oxygen Saturation, and Visual Analog Scale for Different Cuff Pressures

Contribution of EEG biosignals for Stress Detection

Design, Fabrication, and Evaluation of 3D Biopotential Electrodes and Intelligent Garment System for Sports Monitoring

Contact Info

Product

Resources

About