ArticleJMIR AI2024
A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study.
Article in JMIR AI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review.Biosensors · 2025Pooled it
- Personalized Classification of Scenario-Derived Operational Driver-State Classes from Non-Intrusive Wearable Signals in Real-World SAE Level 2 Automated Driving.Sensors (Basel, Switzerland) · 2026Article
- Detecting cognitive impairment and psychological well-being among older adults.Machine learning. Health · 2026Article
- Personalized modeling of stress and blood pressure reactivity using mobile health data.Npj mental health research · 2026Article
- Psychological-physical synergy of athletes based on artificial intelligence and deep learning.Scientific reports · 2026Article
- MEPT-LLM: a multimodal generative AI model for identifying and understanding cultural-emotional barriers in the language classroom.Frontiers in psychology · 2026Article
- Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective.Biomimetics (Basel, Switzerland) · 2025Review
- Smart Healthcare at Home: A Review of AI-Enabled Wearables and Diagnostics Through the Lens of the Pi-CON Methodology.Sensors (Basel, Switzerland) · 2025Review
- Personalization of AI Using Personal Foundation Models Can Lead to More Precise Digital Therapeutics.JMIR AI · 2025Article
- Action unit based micro-expression recognition framework for driver emotional state detection.Scientific reports · 2025Article
- A cross-domain framework for emotion and stress detection using WESAD, SCIENTISST-MOVE, and DREAMER datasets.Frontiers in bioengineering and biotechnology · 2025Article
- Hidden Markov model for acoustic pesticide exposure detection and hive identification in stingless bees.PloS one · 2025Article
- Reliability, validity, and correlates of an AI voice emotion recognition app among nurses.PloS one · 2025Article
- Balancing Between Privacy and Utility for Affect Recognition Using Multitask Learning in Differential Privacy-Added Federated Learning Settings: Quantitative Study.JMIR mental health · 2024Article
- Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive Sensing.AI (Basel, Switzerland) · 2024Article
- Emotion Recognition Using EEG Signals through the Design of a Dry Electrode Based on the Combination of Type 2 Fuzzy Sets and Deep Convolutional Graph Networks.Biomimetics (Basel, Switzerland) · 2024Article
- Automatic Recognition of Multiple Emotional Classes from EEG Signals through the Use of Graph Theory and Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review.Sensors (Basel, Switzerland) · 2024Article
- Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review.Biosensors · 2024Review
- Personalized AI-Driven Real-Time Models to Predict Stress-Induced Blood Pressure Spikes Using Wearable Devices: Proposal for a Prospective Cohort Study.JMIR research protocols · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundThere are a wide range of potential adverse health effects, ranging from headaches to cardiovascular disease, associated with long-term negative emotions and chronic stress. Because many indicators of stress are imperceptible to observers, the early detection of stress remains a pressing medical need, as it can enable early intervention. Physiological signals offer a noninvasive method for monitoring affective states and are recorded by a growing number of commercially available wearables.
objectiveWe aim to study the differences between personalized and generalized machine learning models for 3-class emotion classification (neutral, stress, and amusement) using wearable biosignal data.
methodsWe developed a neural network for the 3-class emotion classification problem using data from the Wearable Stress and Affect Detection (WESAD) data set, a multimodal data set with physiological signals from 15 participants. We compared the results between a participant-exclusive generalized, a participant-inclusive generalized, and a personalized deep learning model.
resultsFor the 3-class classification problem, our personalized model achieved an average accuracy of 95.06% and an F
conclusionsOur results emphasize the need for increased research in personalized emotion recognition models given that they outperform generalized models in certain contexts. We also demonstrate that personalized machine learning models for emotion classification are viable and can achieve high performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.