Stress Detection System Research Articles

Stress Detection System using Machine Learning and Sensors

This project presents a novel stress detection system that leverages machine learning and sensor technology to monitor and assess stress levels in real-time. As stress has become a widespread concern affecting mental health, our system integrates wearable sensors—such as heart rate monitors, galvanic skin response sensors, and accelerometers—to capture physiological data indicative of stress. The project involves several key components: data collection and preprocessing, feature extraction, and the development of machine learning models capable of accurately classifying stress levels based on the sensor data. Various algorithms, including Support Vector Machines and Neural Networks, will be employed and optimized for performance. The system is designed to provide real-time feedback to users, offering insights and personalized recommendations for stress management based on detected levels. A user-friendly interface will enable individuals to track their stress over time, fostering greater awareness and encouraging proactive coping strategies

Towards an AI-Driven Cyber-Physical System for Closed-Loop Control of Plant Diseases

Plant diseases are a major biosecurity threat to food production and the bio-energy industry. Early detection and control of plant diseases can improve producers’ profitability and reduce environmental impacts from chemical inputs. We proposed to develop a cyber-physical system with three major components: an AI-driven imaging system for early stress detection, an autonomous robotic system to collect plant samples, and a sequencing pipeline to detect molecular signatures of pathogens for disease confirmation. This system is envisioned to control a detected disease by removing or pruning infected plants. This manuscript describes the major milestones achieved by this CPS project and provides a future perspective on disease control automation in agriculture.

Identifying physiological indicators of the cognitive, thermal, and combined (cognitive-thermal) stress conditions.

Physiologically based stress detection systems have proven to be effective in identifying different stress conditions in the body to determine the source of stress and be able to counteract it. However, some stress conditions have not been widely studied, including thermal stress, cognitive stress, and combined (thermal-cognitive) stress conditions, which are frequently encountered in work or school environments. In order to develop systems to detect and differentiate these conditions, it is necessary to identify the physiological indicators that characterize each of them. The present research aims to identify which physiological indicators (heart rate, respiratory rate, galvanic skin response, and local temperature) could differentiate different stress conditions (no-stress, cognitive stress, thermal stress, and combined (thermal-cognitive) stress conditions). Thirty participants were exposed to cognitive, thermal, and combined stress sources while recording their physiological signals. The findings indicate that both mean heart rate and mean galvanic skin response identify moderate thermal and cognitive stress conditions as distinct from a no-stress condition, yet they do not differentiate between the two stress conditions. Additionally, heart rate uniquely identifies the cognitive-thermal stress condition, effectively distinguishing this combined stress condition from the singular stress conditions and the no-stress condition. Mean local temperature specifically signals thermal stress conditions, whereas mean respiratory rate accurately identifies cognitive stress conditions, with both indicators effectively separating these conditions from each other and from the no-stress condition. This is the first basis for differentiating thermal and cognitive stress conditions through physiological indicators.

An Explainable Deep Learning Approach for Stress Detection in Wearable Sensor Measurements.

Stress has various impacts on the health of human beings. Recent success in wearable sensor development, combined with advancements in deep learning to automatically detect features from raw data, opens several interesting applications related to detecting emotional states. Being able to accurately detect stress-related emotional arousal in an acute setting can positively impact the imminent health status of humans, i.e., through avoiding dangerous locations in an urban traffic setting. This work proposes an explainable deep learning methodology for the automatic detection of stress in physiological sensor data, recorded through a non-invasive wearable sensor device, the Empatica E4 wristband. We propose a Long-Short Term-Memory (LSTM) network, extended through a Deep Generative Ensemble of conditional GANs (LSTM DGE), to deal with the low data regime of sparsely labeled sensor measurements. As explainability is often a main concern of deep learning models, we leverage Integrated Gradients (IG) to highlight the most essential features used by the model for prediction and to compare the results to state-of-the-art expert-based stress-detection methodologies in terms of precision, recall, and interpretability. The results show that our LSTM DGE outperforms the state-of-the-art algorithm by 3 percentage points in terms of recall, and 7.18 percentage points in terms of precision. More importantly, through the use of Integrated Gradients as a layer of explainability, we show that there is a strong overlap between model-derived stress features for electrodermal activity and existing literature, which current state-of-the-art stress detection systems in medical research and psychology are based on.

Psychological Stress Detection Using Transformer-Based Models

Stress is a significant mental health problem that results in a lack of concentration. It has been more widely identified through social media since people who are under stress usually post about their physical pain and tiredness. However, stress assessment through social media by professionals can be expensive and time-consuming. The research aimed to produce a stress detection system trained using a Twitter dataset to predict stress using the user’s input sentence. The experiments that were done in the research used transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT (RoBERTa). The research involved data pre-processing, model training, and model evaluation to ensure high-quality train data. Since the data were imbalanced, data trimming was performed in pre-processing to select data randomly until the balance matched. This process ensured the model’s effectiveness in the training and evaluation stages. The features used in these experiments were features from each pre-trained model. In evaluating the model, accuracy, loss, and F1 score were used as metrics. In the result, for BERT, accuracy reaches 0.848 with an F1 score of 0.847. Meanwhile, RoBERTa has an accuracy of 0.837 and 0.834. The results prove that BERT and RoBERTa can be used to classify stress with accuracy and an F1 score above 0.8. The experiment result shows that the BERT deep learning model can detect stress using the Twitter datasets.

User Requirements and Perceptions of a Sensor System for Early Stress Detection in People With Dementia and People With Intellectual Disability: Qualitative Study.

Timely detection of stress in people with dementia and people with an intellectual disability (ID) may reduce the occurrence of challenging behavior. However, detecting stress is often challenging as many long-term care (LTC) residents with dementia and residents with ID have communication impairments, limiting their ability to express themselves. Wearables can help detect stress but are not always accepted by users and are uncomfortable to wear for longer periods. Integrating sensors into clothing may be a more acceptable approach for users in LTC. To develop a sensor system for early stress detection that is accepted by LTC residents with dementia and residents with ID, understanding their perceptions and requirements is essential. This study aimed to (1) identify user requirements for a garment-integrated sensor system (wearable) for early stress detection in people with dementia and people with ID, (2) explore the perceptions of the users toward the sensor system, and (3) investigate the implementation requirements in LTC settings. A qualitative design with 18 focus groups and 29 interviews was used. Focus groups and interviews were conducted per setting (dementia, ID) and target group (people with dementia, people with ID, family caregivers, health care professionals). The focus groups were conducted at 3 time points within a 6-month period, where each new focus group built on the findings of previous rounds. The data from each round were used to (further) develop the sensor system. A thematic analysis with an inductive approach was used to analyze the data. The study included 44 participants who expressed a positive attitude toward the idea of a garment-integrated sensor system but also identified some potential concerns. In addition to early stress detection, participants recognized other potential purposes or benefits of the sensor system, such as identifying triggers for challenging behavior, evaluating intervention effects, and diagnostic purposes. Participants emphasized the importance of meeting specific system requirements, such as washability and safety, and user requirements, such as customizability and usability, to increase user acceptance. Moreover, some participants were concerned the sensor system could contribute to the replacement of human contact by technology. Important factors for implementation included the cost of the sensor system, added value to resident and health care professionals, and education for all users. The idea of a garment-integrated sensor system for early stress detection in LTC for people with dementia and people with ID is perceived as positive and promising by stakeholders. To increase acceptability and implementation success, it is important to develop an easy-to-use, customizable wearable that has a clear and demonstrable added value for health care professionals and LTC residents. The next step involves pilot-testing the developed wearable with LTC residents with dementia and residents with ID in clinical practice.

The Real-Time Image Sequences-Based Stress Assessment Vision System for Mental Health

Early detection and prevention of stress is crucial because stress affects our vital signs like heart rate, blood pressure, skin temperature, respiratory rate, and heart rate variability. There are different ways to determine stress using different devices, such as the electrocardiogram (ECG), electrodermal activity (EDA), the electroencephalogram (EEG), photoplethysmography (PPG), or a questionnaire-based method of stress assessment. In this study, we proposed a camera-based real-time stress detection system using remote photoplethysmography (rPPG). We trained different machine learning models using three datasets: the SWELL dataset, the PPG sensor dataset, and the last ECG and EEG-based stress dataset. The models with the highest predictive accuracy were used to classify stress based on HR and HRV features obtained from the face using a camera. HR and HRV estimations from the face were validated on the PURE public dataset and the custom dataset. In this study, it was observed that the random forest algorithm performs significantly better than other models, achieving an impressive 99% predictive accuracy in the SWELL dataset. In the second dataset, the logistic regression technique shows the best result, achieving an accuracy rate of 84.24%. In the last dataset, the ensemble model achieved an accuracy rate of 67%. We also checked the proposed algorithm in the process of public speaking to estimate stress in a real-time situation.

Stress Tracking and Relieving System

Abstract: Current stress detection systems mostly rely on sensors and survey forms. Sensors are expensive. Survey forms can be inaccurate. To solve these problems our system tries to predict stress from face using deep learning. Our system also predicts heart rate using camera.

Python Based Stress Detection System Using Machine Learning & Algorithm’s

In today's high-pressure world, the need for innovative, non-intrusive stress monitoring solutions is more critical than ever. This paper introduces a sophisticated stress detection system that combines facial expression analysis with machine learning to deliver real-time, continuous stress assessment. Our system employs Haar Cascade classifiers for precise face detection, CNN for feature extraction and utilizes the K-Nearest Neighbors (KNN) algorithm to classify stress-related facial expressions. A significant advantage of our system is its non-intrusive nature, relying solely on visual data, thus eliminating the need for cumbersome wearable sensors. This enhances user comfort and convenience. This paper outlines the design, implementation, and evaluation of our stress detection system, emphasizing its potential applications in workplace wellness, mental health monitoring, and human-computer interaction Key Words: - KNN, Facial Expressions, Machine Learning, Stress Detection, Image Processing, CNN

Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review

Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents a scoping review of personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, wearable devices, and real-world implementation challenges. The review presents an overview of stress and its biological mechanisms, details the methodology for the literature search, and synthesizes the findings. It shows that biosignals, especially EDA and PPG, are frequently utilized for stress detection and demonstrate potential reliability in multimodal settings. Evidence for a trend towards deep learning models was found, although the limited comparison with traditional methods calls for further research. Concerns arise regarding the representativeness of datasets and practical challenges in deploying wearable technologies, which include issues related to data quality and privacy. Future research should aim to develop comprehensive datasets and explore AI techniques that are not only accurate but also computationally efficient and user-centric, thereby closing the gap between theoretical models and practical applications to improve the effectiveness of stress detection systems in real scenarios.

STRESS TRACKING AND RELIEVING SYSTEM

Current stress detection systems mostly rely on sensors and survey forms. Sensors are expensive. Survey forms can be inaccurate. To solve these problems.Our system tries to predict stress from face using deep learning. Our system also predicts heart rate using camera. Keywords: Machine learning, live detection, heart rate, Healthcare routine.

Stress Detection for Employee’s using Image Processing and Text Mining

The major goal of this study is to use vivid Machine Learning and Image Processing methods to identify stress in the human body. Our system is an upgraded version of previous stress detection systems that did not include live detection or personal counselling, but this system includes live detection and periodic analysis of employees, as well as detecting physical and mental stress levels in them and providing proper stress management remedies via a survey form. Our method is primarily focused on stress management and creating a healthy and spontaneous work environment for workers in order to get the most out of them during working hours

Stress Detection through EEG Signals: Employing a Hybrid Approach integrating Time Domain, Frequency Domain Features and Machine Learning Techniques

Mental stress is a widespread problem that affects people all over the world and can have negative health consequences if not appropriately controlled. The importance of early detection and management in minimizing the detrimental effects of stress cannot be overstated. This study provides a hybrid technique for stress detection that combines time domain and frequency domain information taken from EEG data. Machine learning techniques are used to create a stress detection model that is accurate and dependable. The goal is to increase the accuracy and reliability of stress detection so that prompt intervention and assistance may be provided. The paper opens with an introduction to stress, its effects on mental health, and the need for automated stress detection systems. EEG signals are introduced as a valuable data source for recording stress-related brain activity. To test the model's success in stress detection, performance evaluation criteria such as accuracy, sensitivity, specificity, and F1-score are used. When the result compared to the present approach, the Hybrid Approach has higher accuracy (SVM-98.33% & RF-95%).

Stress-Nets- A Novel LSTM Ensembled Single Feed Forward Layers for Stress Classification with EEG Signals

Mental instability and emotional imbalance of the individual can be reflected in the form of stress which results in an inappropriate work ethics. There are various methods for the Stress creation. Moreover , several bio-signal sources such as Electroencephalograph(EEG), Electrocardiography(ECG) and Electromyography(EMG) are considered to be the important catalyst for developing the stress detection systems(SDS). Recently, machine and deep learning algorithms has gained more popularity in designing the SDS using the bio-signals. Further, EEG based SDS with ML and DL algorithms plays an important role for its high classification accuracy and performance. However, these EEG based SDS systems needs the better lime light of improvisation in terms of performance and computational overhead to deal with multiple datasets. In this context, this manuscript proposes new Long Short term Memory recurrent units(LSTM) for extracting temporal features while enhanced extreme learning machines are employed for better classification with less computational complexity. The different data sources are used to collect the EEG signals in which the collected signals are preprocessed for evaluating the proposed model. Additionally, the experiments are performed by DEAP and Kaggle datasets as well as performance parameters and compared by conventional Fused Support vector machines (F-SVM),BI-Long Short Term Memory(BILSTM), Random Forest(RF) and Deep Convolutional Neural network(DCNN). Results shows which proposed EEG based SDS has better performance than other conventional ones of high accuracy in stress detection and for diagnosing classify the stress-levels .

Posture and stress detection system using open CV and media pipe

Posture and stress are two critical factors affecting a person's physical and mental well-being. Proper posture and stress management can help avoid a range of health issues. Poor posture may result in chronic discomfort, decreased mobility, and an increased risk of musculoskeletal problems. Similarly, stress can negatively impact physical and mental health, contributing to conditions such as depression, anxiety, and cardiovascular disease. Traditional methods for assessing posture and stress, including physical examinations or self-reporting, can be subjective and time-consuming. Recent advances in machine learning and computer vision techniques have enabled the development of models that automatically detect posture and stress levels from video data. In this study, a Django framework was built that incorporates models to assess posture by calculating angles between tracked distance vectors. Stress levels are evaluated based on facial features and expressions. The use of skeleton data for human posture recognition is a key research area in human-computer interaction. By employing the MSR 3D Action Dataset, 33 key skeletal points on the body are detected, aiding in posture determination. Additionally, analyzing facial features and emotions is essential for estimating stress levels. The approach relies on convolutional neural networks.

Implementation of Thermal Camera for Human Stress Detection: A Review

Stress has become a major problem that people face today. The high level of competition and environmental demands make people more susceptible to stress. Stress can interfere with a person's ability to work effectively. If left unchecked for a long time, stress can cause various dangerous diseases such as hypertension, heart problems, and others that can lead to death. Research has been conducted for a long time to detect stress. Various technologies have been used to detect and anticipate stress that occurs in humans. One promising technology for detecting stress is the use of thermal cameras. Thermal cameras have several advantages: being non-contact and non-invasive, quick, easy to use, and cost-effective. In general, the architecture of the stress detection system using a thermal camera consists of several stages, including image acquisition, pre-processing, ROI tracking and selection, feature extraction, and statistical analysis or classification. This paper aims to review the use of thermal cameras in detecting stress in humans. This paper also seeks to answer the research question of what analysis can be done to improve stress detection accuracy using thermal camera images. Research shows that ROI selection must be carefully considered to obtain good accuracy. Combining thermal images with other data can improve accuracy in stress detection. Machine learning in classification provides many benefits in recognizing patterns but is highly influenced by the number of datasets used.

Stress and Emotion Detection System Using IoT and Machine Learning with a Fuzzy Inference-Based Mental Health Risk Assessment Module

This research paper describes the conceptualization, deployment, and application of a stress and emotion detection system (SEDS) that utilizes emerging technologies including machine learning, the Internet of Medical Things (IoMT), and fuzzy logic. Particle Photon microcontrollers were used to collect and analyze data from non-invasive biosensing devices, input switches for behavioral, emotional, and cognitive stress indicators, and input signals generated by the machine learning-based facial identification sub-module, which detects an individual's emotional states (happy, sad/upset, angry/irritable, or nervous/scared). The acquired parameters were stored and made readily accessible using IoT cloud services and dedicated mobile phone applications. The emotional condition of an individual was assessed by the utilization of the Personal Image Classifier tool within the MIT App Inventor, which involved the analysis of facial expressions. Furthermore, the SEDS was integrated with a module for mental health risk assessment which employs fuzzy logic to categorize the user's stress-related psychological health threat level as very low, low, moderate, high, or extremely high based on the data collected. The customized smartphone application provided users specific recommendations for effectively managing their mental health, based on stress level assessments. When the SEDS determined that the level of mental health risk posed by stress was high, it automatically generated a referral notification and transmitted it via text message to the mental health care professional, facilitating the provision of appropriate psychological counseling. Based on the collected data, the stress and emotion detection system produced results that were comparable to those from the DASS21 stress scale. The system demonstrated an improved accuracy of 90% in a test involving thirty individuals who volunteered to participate. The machine learning-based emotion detection system achieved a classification accuracy of 86.67% in correctly detecting happy, neutral, sad, angry, or nervous feelings through the analysis of facial expressions. This research is designed to provide mental health care professionals such as guidance counselors, psychologists, and psychiatrists with the resources essential to facilitate the evaluation and treatment of mental health issues. It also aims to raise people's understanding and detection of psychological conditions through enhanced awareness initiatives.

On the Development of Machine Learning Based Real-Time Stress Monitoring : A Pilot Study

During specific environmental changes, the human body regulates itself through emotional, physical or mental responses. One such response is stress. The psychological and physical stability of an individual may be affected by recurrent occurrences of acute stress. This often leads to anxiety disorder, other psychological illnesses, hypertension, and other physiological disorders. The work performance of the individual is also negatively affected due to long-term stress. Across various age groups, the global population is primarily influenced by anxiety, depression and psychological stress. The long-term adverse effects of stress can be mitigated by effectively monitoring and managing stress through a cost-efficient and reliable stress detection system. This paper mainly focuses on stress detection using a machine-learning approach. Wearable sensor data from electroencephalogram (EEG) and electrocardiogram (ECG) are considered during exposure to stress and the level of stress undergone by the participant is further analyzed. This approach helps in stress detection, analysis and mitigation, which in turn improves the quality life of people. Machining Learning technique k-means clustering algorithm is used after removal of artifacts to obtain case-specific clusters that segregate features pointing to non-stress and stress periods. The results of the proposed K-means clustering algorithm are compared to state-of-the-art techniques such as Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF) and Support Vector Machine (SVM). From the results, it was concluded that the proposed algorithm outperformed the other with an accuracy of 96% in the overall analysis.

Real-time stress detection and monitoring system using IoT-based physiological signals

Currently, medical experts use psychophysiological questionnaires to evaluate human stress levels during counseling or interviews. Typically, biochemical samples use urine, saliva, and blood samples to identify the effects of stress on the human body. This research explains that stress detection can be done by analyzing psychological signals and the importance of monitoring stress levels. The authors develop research on stress detection based on psychological signals. The system then processes the recorded data; the android application displays the calculation results. The database can also be accessed as a spreadsheet via a web application. The design of real-time stress detection and monitoring using internet of things (IoT) can work well.

Automated Multimodal Stress Detection in Computer Office Workspace

Nowadays, changes in the conditions and nature of the workplace make it imperative to create unobtrusive systems for the automatic detection of occupational stress, which can be feasibly addressed through the adoption of Internet of Things (IoT) technologies and advances in data analysis. This paper presents the development of a multimodal automated stress detection system in an office environment that utilizes measurements derived from individuals’ interactions with the computer and its peripheral units. In our analysis, behavioral parameters of computer keyboard and mouse dynamics are combined with physiological parameters recorded by sensors embedded in a custom-made smart computer mouse device. To validate the system, we designed and implemented an experimental protocol simulating an office environment and included the most known work stressors. We applied known classifiers and different data labeling methods to the physiological and behavioral parameters extracted from the collected data, resulting in high-performance metrics. The feature-level fusion analysis of physiological and behavioral parameters successfully detected stress with an accuracy of 90.06% and F1 score of 0.90. The decision-level fusion analysis, combining the features extracted from both the computer mouse and keyboard, showed an average accuracy of 66% and an average F1 score of 0.56.