Evidence map›Paper›PMID 42817256›Full record

ArticleSensors (Basel, Switzerland)2026

Emotion-Aware Virtual Reality Through Multimodal ECG and Postural Fusion.

Juan Benavides, Mayra Carrión-Toro, Cindy López, David Morales-Martínez, Marco Santórum, Patricia Acosta-Vargas

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Juan BenavidesDepartamento de Informática y Ciencias de la Computación, Escuela Politécnica Nacional, Quito 170525, Ecuador.ORCID 0000-0003-4527-7137
Mayra Carrión-ToroDepartamento de Informática y Ciencias de la Computación, Escuela Politécnica Nacional, Quito 170525, Ecuador.ORCID 0000-0001-5720-1960
Cindy LópezDepartamento de Informática y Ciencias de la Computación, Escuela Politécnica Nacional, Quito 170525, Ecuador.ORCID 0000-0002-9182-6865
David Morales-MartínezDepartamento de Informática y Ciencias de la Computación, Escuela Politécnica Nacional, Quito 170525, Ecuador.ORCID 0000-0002-6578-3013
Marco SantórumDepartamento de Informática y Ciencias de la Computación, Escuela Politécnica Nacional, Quito 170525, Ecuador.ORCID 0000-0001-7251-923X
Patricia Acosta-VargasIntelligent and Interactive Systems Laboratory, Universidad de Las Américas, Quito 170503, Ecuador.ORCID 0000-0003-4210-0117

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immersive Virtual Reality (VR) environments are increasingly adopted in clinical psychology and stress-management contexts; however, their therapeutic effectiveness depends on the system's ability to understand and dynamically respond to users' affective states. Emotional regulation plays a key role in psychological resilience, directly influencing stress coping mechanisms, cognitive performance, and overall mental well-being. Despite recent advances, automatic recognition of scenario-associated affective conditions in VR remains challenging because head-mounted displays occlude facial features. This study proposes a VR-based serious game for affective training and regulation, in which users interact with goal-oriented scenarios targeting fear, anger, and joy. We introduce a multimodal affective computing model to objectively assess users' emotional responses by integrating electrocardiogram (ECG) signals and posture-based features extracted through computer vision. An early-fusion architecture combined with a Long Short-Term Memory (LSTM) network captures temporal dependencies in synchronized multimodal data. We established a controlled experimental framework using immersive VR scenarios, enabling the collection of synchronized physiological and behavioral data from a cohort of 20 healthy adult participants. The proposed model was evaluated under a strict Leave-One-Subject-Out (LOSO) cross-validation scheme across independent subjects, achieving a robust inter-subject accuracy of 78.94%±10.77% and a global macro F1-score of 0.635, demonstrating strong generalization to entirely unseen users without data leakage. Furthermore, the system maintained an outstanding balance in detecting active emotional states (recall > 80.0% for fear, anger, and joy). Additionally, subjective evaluations using the PANAS and SGU questionnaires confirmed the coherence between detected and perceived emotional states, as well as the system's high usability. The results suggest the potential viability of combining immersive environments and multimodal affective computing to explore the technical feasibility of adaptive frameworks that could eventually translate into healthcare contexts. This work may contribute to the development of intelligent digital health technologies by providing a foundation for responsive VR systems that can monitor emotional regulation and are fully aligned with sustainable well-being ecosystems (SDG 3: Good Health and Well-being and SDG 10: Reduced Inequalities).

Indexed as

ElectrocardiographyEmotionsPostureVirtual RealityAdultFemaleHumansLong Short Term MemoryMaleaffective computingdigital mental healthelectrocardiogram (ECG)emotion regulationmultimodal emotion recognitionpostural analysisSDG 3virtual reality

Identifiers

PMID42817256
PMCPMC13611044

What OpenQuestion holds

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Registered trials

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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.