SynthesisSensors (Basel, Switzerland)2020
A Review of Emotion Recognition Methods Based on Data Acquired via Smartphone Sensors.
Synthesis in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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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.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Datasets for Automated Affect and Emotion Recognition from Cardiovascular Signals Using Artificial Intelligence- A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- A review of current capabilities and future directions in machine-based emotion recognition.Cognitive, affective & behavioral neuroscience · 2026Review
- The Effects of a Smartphone App (Feelee) to Enhance Adolescents' Emotion Regulation Skills in a Forensic Outpatient Setting: Protocol for a Multiple Single-Case Experimental Design.JMIR research protocols · 2025Article
- Facial Emotion Recognition of 16 Distinct Emotions From Smartphone Videos: Comparative Study of Machine Learning and Human Performance.Journal of medical Internet research · 2025Article
- Emotion Forecasting: A Transformer-Based Approach.Journal of medical Internet research · 2025Article
- How Digital Solutions Might Provide a World of New Opportunities for Holistic and Empathic Support of Patients with Hidradenitis Suppurativa.Dermatology and therapy · 2024Article
- Affective Impressions Recognition under Different Colored Lights Based on Physiological Signals and Subjective Evaluation Method.Sensors (Basel, Switzerland) · 2023Article
- Associations Between Smartphone Keystroke Metadata and Mental Health Symptoms in Adolescents: Findings From the Future Proofing Study.JMIR mental health · 2023Article
- Willingness of Participation in an Application-Based Digital Data Collection among Different Social Groups and Smartphone User Clusters.Sensors (Basel, Switzerland) · 2023Article
- Multi-Input Speech Emotion Recognition Model Using Mel Spectrogram and GeMAPS.Sensors (Basel, Switzerland) · 2023Article
- Emotion Detection Based on Pupil Variation.Healthcare (Basel, Switzerland) · 2023Article
- Smartphone-Based Facial Scanning as a Viable Tool for Facially Driven Orthodontics?Sensors (Basel, Switzerland) · 2022Article
- Development and validation of a highly dynamic and reusable picture-based scale: A new affective measurement tool.Frontiers in psychology · 2022Article
- Article
- Sensors and Artificial Intelligence Methods and Algorithms for Human-Computer Intelligent Interaction: A Systematic Mapping Study.Sensors (Basel, Switzerland) · 2021Article
- Attitudes towards Participation in a Passive Data Collection Experiment.Sensors (Basel, Switzerland) · 2021Article
- Machine Learning Methods for Fear Classification Based on Physiological Features.Sensors (Basel, Switzerland) · 2021Article
- Know Yourself: Physical and Psychological Self-Awareness With Lifelog.Frontiers in digital health · 2021Article
- Hybrid Method of Automated EEG Signals' Selection Using Reversed Correlation Algorithm for Improved Classification of Emotions.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, emotion recognition algorithms have achieved high efficiency, allowing the development of various affective and affect-aware applications. This advancement has taken place mainly in the environment of personal computers offering the appropriate hardware and sufficient power to process complex data from video, audio, and other channels. However, the increase in computing and communication capabilities of smartphones, the variety of their built-in sensors, as well as the availability of cloud computing services have made them an environment in which the task of recognising emotions can be performed at least as effectively. This is possible and particularly important due to the fact that smartphones and other mobile devices have become the main computer devices used by most people. This article provides a systematic overview of publications from the last 10 years related to emotion recognition methods using smartphone sensors. The characteristics of the most important sensors in this respect are presented, and the methods applied to extract informative features on the basis of data read from these input channels. Then, various machine learning approaches implemented to recognise emotional states are described.
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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.