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What is the most effective method used for stress detection in mobile applications? 

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Smartwatch
Engineering
Feature selection
Anxiety
Best insight from top research papers

The most effective method for stress detection in mobile applications integrates various technologies and approaches to accurately assess and manage stress levels. The SELF-CARE system, which employs a generalized selective sensor fusion method, stands out for its robust stress classification through wearable devices, demonstrating high accuracy rates in both wrist-worn and chest-worn sensors . Similarly, the KnowStress app utilizes the K-means clustering algorithm to categorize individual responses into stress-related groups, offering a convenient solution for diagnosing stress and related disorders . Smartwatches, equipped with sensors for heart rate variability (HRV) and Electro Dermal Activity (EDA), provide a non-invasive means to detect stress, although their precision may not match medical equipment . A novel approach through speech analysis, using a fusion of Teager Energy Operator and Mel Frequency Cepstral Coefficients, has shown promising results in recognizing stressed emotions with high classification accuracies . Machine learning models developed for smartphones and edge devices, particularly those combining feature selection methods with the kNN classification algorithm, have proven effective in distinguishing stress levels through EEG and ECG signals . An Android application has been developed to measure cortisol concentration, a stress hormone, using a colorimetric sensor, indicating stress levels through color changes detected by smartphone cameras . Automated stress detection systems, leveraging physiological signals and machine learning algorithms, offer a scientific tool for stress management, potentially improving societal welfare . The multimethod fusion approach, combining EEG signal analysis with advanced algorithms, has shown superior accuracy in human emotion classification, including stress . An IoT-based system integrating multiple sensors and AI algorithms for real-time stress detection and classification achieves a high accuracy rate, demonstrating the potential of wearable and mobile technologies in stress management . Lastly, a community-wide approach aims to detect signs of excessive stress non-intrusively, without requiring dedicated app installations, offering a scalable solution . In conclusion, the integration of sensor technologies, machine learning models, and IoT systems presents the most effective method for stress detection in mobile applications, offering high accuracy, convenience, and non-intrusive monitoring capabilities.

Answers from top 8 papers

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Papers (8)Insight
Proceedings Article•DOI
Passive Detection of Perceived Stress Using Location-driven Sensing Technologies at Scale (demo)
Camellia Zakaria, Youngki Lee, Rajesh Krishna Balan 
12 Jun 2019
1 Citations
Passive stress detection using location-driven sensing technologies without dedicated apps or wearable sensors is the most effective method for stress detection in mobile applications.
Proceedings Article•DOI
Stress Detector System Using IoT and Artificial Intelligence
Areej Mustafa, Maitha Alahmed, Aysha Alhammadi, Bassel Soudan 
01 Feb 2020
7 Citations
The most effective method for stress detection in mobile applications is through integrating skin conductance, ECG, and skin temperature sensors in a wearable device, analyzed by AI algorithms for real-time detection.
Proceedings Article•DOI
Stress Detection based on TEO and MFCC speech features using Convolutional Neural Networks (CNN)
Muhammad Syafiq Nordin, Ani Liza Asnawi, Nur Aishah Bt. Zainal, Rashidah Funke Olanrewaju, Ahmad Jusoh, S. Noorjannah Ibrahim, Nor Fadhillah Mohamed Azmin 
14 Nov 2022
The most effective method for stress detection in mobile applications is the fusion of Teager Energy Operator (TEO) and Mel Frequency Cepstral Coefficients (MFCC) known as Teager-MFCC (T-MFCC) with Convolutional Neural Networks (CNN).
Proceedings Article•DOI
Utilizing Smartphone as Colorimetric Sensor in Detecting Stress Level
Erry Dwi Kurniawan, Razi Deniwa Zakiyudin, Robeth Viktoria Manurung, Asih Setiarini, Yulia Setia, Salita Ulitia Prini 
06 Dec 2022
Utilizing a smartphone as a colorimetric sensor to measure cortisol concentration, translating it into RGB values, is an effective method for stress detection in mobile applications.
Proceedings Article•DOI
The Development of Intelligent Models for Stress Detection towards Real-world Applications
Apit Hemakom, Danita Atiwiwat, Jongsook Sanguantrakul, Pasin Israsena 
22 Jun 2022
The most effective method for stress detection in mobile applications is the combination of Hybrid feature selection and kNN classification algorithms based on EEG and ECG signals.
Open access•Journal Article•DOI
Stress Detection using Context-Aware Sensor Fusion from Wearable Devices
Nafiul Rashid, Trier R. Mortlock, Mohammad Abdullah Al Faruque 
14 Mar 2023-IEEE Internet of Things Journal
Selective sensor fusion method, SELF-CARE, utilizing context-aware ensemble machine learning, proves most effective for stress detection in mobile applications, achieving high accuracy rates with wrist and chest-worn sensors.
Open access•Journal Article•DOI
Stress Detection Smartwatch
Meghshanth Sara
31 Jul 2022-International Journal For Science Technology And Engineering
Heart rate variability (HRV) and Electro Dermal Activity (EDA) sensors are utilized for stress detection in mobile applications, providing reliable insights into stress levels based on physiological responses.
Book Chapter•DOI
KnowStress: A Mobile Application Prototype Detection of Stress and Stress Related Disorders
Matthias Blum
01 Jan 2023
The most effective method for stress detection in mobile applications is the utilization of the K-means clustering algorithm, achieving a validation accuracy of 88.86% in the KnowStress prototype.

Related Questions

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How can stress be detected using voice?5 answersStress can be detected using voice by analyzing various sound features extracted from speech signals. Deep learning models, such as CNN architectures, have been developed to detect stress through voice recordings. These models use features like Mel Frequency Cepstral Coefficients (MFCC) and Teager Energy Operator (TEO) to capture the nonlinear components and instantaneous energy of speech. The fusion of TEO and MFCC, known as Teager-MFCC (T-MFCC), has shown improved performance in recognizing stressed emotions compared to using MFCC alone. Additionally, voice stress detection can be enhanced by incorporating speaker embeddings into the features extracted from self-supervised models like Hybrid BYOL-S. By training on diverse datasets with speakers from different language groups and considering individual variabilities, voice stress detection systems can achieve high accuracy in classifying stressed and unstressed speech.
What are the recent studies on detecting stress from wearables data?5 answersRecent studies have focused on detecting stress from wearable data. One study proposed a two-stage semi-supervised learning framework that leverages wearable sensor data to help with stress detection. The framework includes an auto-encoder pre-training method for learning from unlabeled data and a consistency regularization approach to enhance model robustness. Another study explored the feasibility of using wrist-based electrodermal activity (EDA) signals collected from wearable devices to predict stress status. The study found that Support Vector Machine (SVM) outperformed other machine learning approaches with an accuracy of 92.9% for stress prediction. A neural network-based wearable physiological IoT system was also developed to detect stress and emotion levels in IT professionals. The system achieved an accuracy rate of 91.6% in detecting stress and emotion levels. Additionally, a scoping review highlighted the potential of wearable EDA sensors in detecting perceived stress, but emphasized the need for field studies in relevant populations and real-life situations.
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