An Advanced Stress Detection Approach based on Processing Data from Wearable Wrist Devices

Alshamrani, Mazin

doi:10.14569/ijacsa.2021.0120745

Cited by 6 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [26], the researchers utilized the WESAD dataset, accumulating 14 relevant features. A neural network was subsequently employed for classification, yielding an accuracy of 85%.…”

Section: Related Workmentioning

confidence: 99%

A machine-learning Approach for Stress Detection Using Wearable Sensors in Free-living Environments

Al-Alim,

Mubarak,

Salem

et al. 2024

Preprint

View full text Add to dashboard Cite

Stress is a psychological condition due to the body's response to a challenging situation. If a person is exposed to prolonged periods and various forms of stress, their physical and mental health can be negatively affected, leading to chronic health problems. It is important to detect stress in its initial stages to prevent psychological and physical stress-related issues. Thus, there must be alternative and effective solutions for spontaneous stress monitoring. Wearable sensors are one of the most prominent solutions, given their capacity to collect data continuously in real-time. Wearable sensors, among others, have been widely used to bridge existing gaps in stress monitoring thanks to their non-intrusive nature. Besides, they can continuously monitor vital signs, e.g., heart rate and activity. Yet, most existing works have focused on data acquired in controlled settings. To this end, our study aims to propose a machine learning-based approach for detecting the onsets of stress in a free-living environment using wearable sensors. The authors utilized the SWEET dataset collected from 240 subjects via electrocardiography (ECG), skin temperature (ST), and skin conductance (SC). In this work, four machine learning models were tested on this data set consisting of 240 subjects, namely K-Nearest Neighbors (KNN), Support vector classification (SVC), Decision Tree (DT), and Random Forest (RF). These models were trained and tested on four data scenarios. The K-Nearest Neighbor (KNN) model had the highest accuracy of 98%, while the other models also performed satisfactorily.

show abstract

“…In [26], the researchers utilized the WESAD dataset, accumulating 14 relevant features. A neural network was subsequently employed for classification, yielding an accuracy of 85%.…”

Section: Related Workmentioning

confidence: 99%

A machine-learning Approach for Stress Detection Using Wearable Sensors in Free-living Environments

Al-Alim,

Mubarak,

Salem

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…ML technologies have enabled numerous useful applications of AI, including ones in the medical industry, with incredible success [22]. Previous research has extensively investigated the development of stress detection methods such as Decision Trees (DT), K-Nearest Neighbors (KNN), Random Forests, and Logistic Regression [23][24][25][26][27]. Machine learning approaches often need a large enough quantity of data to operate effectively during training.…”

Section: Motivationmentioning

confidence: 99%

Wrist-Based Electrodermal Activity Monitoring for Stress Detection Using Federated Learning

Almadhor

Sampedro

Abisado

et al. 2023

Sensors

View full text Add to dashboard Cite

With the most recent developments in wearable technology, the possibility of continually monitoring stress using various physiological factors has attracted much attention. By reducing the detrimental effects of chronic stress, early diagnosis of stress can enhance healthcare. Machine Learning (ML) models are trained for healthcare systems to track health status using adequate user data. Insufficient data is accessible, however, due to privacy concerns, making it challenging to use Artificial Intelligence (AI) models in the medical industry. This research aims to preserve the privacy of patient data while classifying wearable-based electrodermal activities. We propose a Federated Learning (FL) based approach using a Deep Neural Network (DNN) model. For experimentation, we use the Wearable Stress and Affect Detection (WESAD) dataset, which includes five data states: transient, baseline, stress, amusement, and meditation. We transform this raw dataset into a suitable form for the proposed methodology using the Synthetic Minority Oversampling Technique (SMOTE) and min-max normalization pre-processing methods. In the FL-based technique, the DNN algorithm is trained on the dataset individually after receiving model updates from two clients. To decrease the over-fitting effect, every client analyses the results three times. Accuracies, Precision, Recall, F1-scores, and Area Under the Receiver Operating Curve (AUROC) values are evaluated for each client. The experimental result shows the effectiveness of the federated learning-based technique on a DNN, reaching 86.82% accuracy while also providing privacy to the patient’s data. Using the FL-based DNN model over a WESAD dataset improves the detection accuracy compared to the previous studies while also providing the privacy of patient data.

show abstract

“…Human health is closely related to mental stress, which is subjective and continuously variable. Thus, determining the onset, duration, and severity of stressful events is a difficult and worthy research topic [1,2]. Furthermore, it is a challenging task for researchers and clinicians to obtain biomarkers of stress and health [3].…”

Section: Introductionmentioning

confidence: 99%

Psychological Stress Level Detection Based on Heartbeat Mode

Gao

2022

Applied Sciences

View full text Add to dashboard Cite

The effective detection and quantification of mental health has always been an important research topic. Heart rate variability (HRV) analysis is a useful tool for detecting psychological stress levels. However, there is no consensus on the optimal HRV metrics in psychological assessments. This study proposes an HRV analysis method that is based on heartbeat modes to detect drivers’ stress. We used statistical tools for linguistics to detect and quantify the structure of the heart rate time series and summarized different heartbeat modes in the time series. Based on the k-nearest neighbors (k-NN) classification algorithm, the probability of each heartbeat mode was used as a feature to detect and recognize stress caused by the driving environment. The results indicated that the stress from the driving environment changed the heartbeat mode. Stress-related heartbeat modes were determined, facilitating detection of the stress state with an accuracy of 93.7%. We also concluded that the heartbeat mode was correlated to the galvanic skin response (GSR) signal, reflecting real-time abnormal mood fluctuations. The proposed method revealed HRV characteristics that made quantifying and detecting different mental conditions possible. Thus, it would be feasible to achieve personalized analyses to further study the interaction between physiology and psychology.

show abstract

An Advanced Stress Detection Approach based on Processing Data from Wearable Wrist Devices

Cited by 6 publications

References 24 publications

A machine-learning Approach for Stress Detection Using Wearable Sensors in Free-living Environments

A machine-learning Approach for Stress Detection Using Wearable Sensors in Free-living Environments

Wrist-Based Electrodermal Activity Monitoring for Stress Detection Using Federated Learning

Psychological Stress Level Detection Based on Heartbeat Mode

Contact Info

Product

Resources

About