ReviewFrontiers in psychology2020
Toward Emotion Recognition From Physiological Signals in the Wild: Approaching the Methodological Issues in Real-Life Data Collection.
Review in Frontiers in psychology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Continuous Emotion Recognition Using EDA-Graphs: A Graph Signal Processing Approach for Affective Dimension Estimation.Applied sciences (Basel, Switzerland) · 2026Article
- A survey of machine learning for estimating workload: considering unknown tasks.Frontiers in robotics and AI · 2026Review
- Utilizing Tympanic Membrane Temperature for Earphone-Based Emotion Recognition.Sensors (Basel, Switzerland) · 2025Article
- Retracted: A Dynamic Adaptive Ensemble Learning Framework for Noninvasive Mild Cognitive Impairment Detection: Development and Validation Study.JMIR medical informatics · 2025Article
- A Resource-Efficient Multi-Entropy Fusion Method and Its Application for EEG-Based Emotion Recognition.Entropy (Basel, Switzerland) · 2025Article
- Preparing Wearable Data for AI-Powered Mood and Compliance Prediction in HCT Patients and Caregivers.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2024Article
- Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care.JMIR mHealth and uHealth · 2024Article
- Physiological data for affective computing in HRI with anthropomorphic service robots: the AFFECT-HRI data set.Scientific data · 2024Article
- A real-world dataset of group emotion experiences based on physiological data.Scientific data · 2024Article
- I DARE: IULM Dataset of Affective Responses.Frontiers in human neuroscience · 2024Article
- Socially interactive agents for robotic neurorehabilitation training: conceptualization and proof-of-concept study.Frontiers in artificial intelligence · 2024Article
- Identification of heart rate change during the teaching process.Scientific reports · 2023Article
- The Mandarin Chinese auditory emotions stimulus database: A validated set of Chinese pseudo-sentences.Behavior research methods · 2023Article
- Emodiversity evaluation of remote workers through health monitoring based on intra-day emotion sampling.Frontiers in public health · 2023Observational
- Assessment of the human response to acute mental stress-An overview and a multimodal study.PloS one · 2023Article
- Bosses without a heart: socio-demographic and cross-cultural determinants of attitude toward Emotional AI in the workplace.AI & society · 2023Article
- Fear Detection in Multimodal Affective Computing: Physiological Signals versus Catecholamine Concentration.Sensors (Basel, Switzerland) · 2022Article
- Exploring Unsupervised Machine Learning Classification Methods for Physiological Stress Detection.Frontiers in medical technology · 2022Article
- An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations.Sensors (Basel, Switzerland) · 2021Article
- Measuring and Modeling the Effect of Audio on Human Focus in Everyday Environments Using Brain-Computer Interface Technology.Frontiers in computational neuroscience · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emotion, mood, and stress recognition (EMSR) has been studied in laboratory settings for decades. In particular, physiological signals are widely used to detect and classify affective states in lab conditions. However, physiological reactions to emotional stimuli have been found to differ in laboratory and natural settings. Thanks to recent technological progress (e.g., in wearables) the creation of EMSR systems for a large number of consumers during their everyday activities is increasingly possible. Therefore, datasets created in the wild are needed to insure the validity and the exploitability of EMSR models for real-life applications. In this paper, we initially present common techniques used in laboratory settings to induce emotions for the purpose of physiological dataset creation. Next, advantages and challenges of data collection in the wild are discussed. To assess the applicability of existing datasets to real-life applications, we propose a set of categories to guide and compare at a glance different methodologies used by researchers to collect such data. For this purpose, we also introduce a visual tool called Graphical Assessment of Real-life Application-Focused Emotional Dataset (GARAFED). In the last part of the paper, we apply the proposed tool to compare existing physiological datasets for EMSR in the wild and to show possible improvements and future directions of research. We wish for this paper and GARAFED to be used as guidelines for researchers and developers who aim at collecting affect-related data for real-life EMSR-based applications.
Indexed as
Identifiers
What OpenQuestion holds
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.