Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective
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Preet Kamal
Abstract
Stress detection is being increasingly identified as a significant aspect of mental health evaluation and treatment, especially in a time when digital health technologies are developing fast. This book chapter gives an extensive overview of machine learning-based stress detection approaches with emphasis on the combination of physiological signals (e.g., heart rate variability, and skin conductance) and contextual information (e.g., activity levels and environmental stimuli). The research utilizes a multi model strategy, encompassing support vector machines (SVMs), random forests (RFs), and long short-term memory (LSTM) networks, to attain consistent and trustworthy classification of stress levels. Particular stress is given to the interpretability of the model, the applicability of the solution in real time, and ethics involved in deploying AI solutions for sensitive mental health surveillance. The chapter further addresses challenges related to dataset collection, labeling, and privacy, as well as mechanisms to improve the system continuously through feedback loops [1]. The results enhance the emerging domain of affective computing and illustrate the capabilities of intelligent systems in facilitating early identification and tailored mental healthcare.
Abstract
Stress detection is being increasingly identified as a significant aspect of mental health evaluation and treatment, especially in a time when digital health technologies are developing fast. This book chapter gives an extensive overview of machine learning-based stress detection approaches with emphasis on the combination of physiological signals (e.g., heart rate variability, and skin conductance) and contextual information (e.g., activity levels and environmental stimuli). The research utilizes a multi model strategy, encompassing support vector machines (SVMs), random forests (RFs), and long short-term memory (LSTM) networks, to attain consistent and trustworthy classification of stress levels. Particular stress is given to the interpretability of the model, the applicability of the solution in real time, and ethics involved in deploying AI solutions for sensitive mental health surveillance. The chapter further addresses challenges related to dataset collection, labeling, and privacy, as well as mechanisms to improve the system continuously through feedback loops [1]. The results enhance the emerging domain of affective computing and illustrate the capabilities of intelligent systems in facilitating early identification and tailored mental healthcare.
Chapters in this book
- Frontmatter I
- Contents V
- Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
- DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
- Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
- A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
- Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
- Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
- Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
- EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
- Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
- Machine Learning Techniques for Healthcare 193
- Applications and Benefits of Machine Learning in Healthcare 215
- Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
- Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
- Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
- MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
- On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
- Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
- AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
- A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
- Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
- Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
- Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
- MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
- Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
- Index
Chapters in this book
- Frontmatter I
- Contents V
- Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
- DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
- Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
- A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
- Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
- Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
- Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
- EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
- Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
- Machine Learning Techniques for Healthcare 193
- Applications and Benefits of Machine Learning in Healthcare 215
- Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
- Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
- Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
- MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
- On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
- Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
- AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
- A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
- Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
- Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
- Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
- MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
- Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
- Index
