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Human Emotion Recognition and Reactions Through Sensor Technologies: Findings, Challenges, Opportunities and Future Directions

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (25 May 2026) | Viewed by 640

Special Issue Editor

Prof. Dr. Messias Silva

E-Mail Website
Guest Editor
1. Department of Industrial Engineering, Engineering and Science School of Guaratinguetá, São Paulo State University—UNESP, Guaratinguetá, SP, Brazil
2. Department of Chemical and Industrial Engineering, Engineering School of Lorena, University of São Paulo—USP, Lorena, SP, Brazil
Interests: neuroscience; neuroengineering; innovation in education; emotional regulation; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The recognition and interpretation of human emotions through sensor technologies have emerged as a rapidly advancing interdisciplinary field, bridging neuroscience, computer science, psychology, biomedical engineering, and human–computer interaction. By leveraging wearable and ambient sensors, physiological signals, and advanced computational models, researchers are now able to capture subtle emotional states and reactions in real time. These developments hold promise for transformative applications in healthcare, education, workplace monitoring, entertainment, and adaptive intelligent systems.

Despite significant progress, major challenges remain. Issues of multimodal data integration, algorithmic accuracy, interpretability, cross-cultural generalizability, and ethical concerns such as privacy and informed consent continue to demand critical attention. At the same time, opportunities abound for developing novel approaches that combine artificial intelligence, affective computing, and advanced sensor networks to enhance the robustness and societal relevance of emotion recognition technologies.

This Special Issue, titled "Human Emotion Recognition and Reactions Through Sensor Technologies: Findings, Challenges, Opportunities and Future Directions", welcomes contributions that address methodological innovations, experimental studies, practical implementations, and theoretical insights. By fostering dialogue across disciplines, we aim to advance the scientific and applied understanding of how sensor-based technologies can reliably and ethically contribute to recognizing and responding to human emotions.

Prof. Dr. Messias Silva
Guest Editor

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • human emotion recognition
  • sensor technologies
  • multimodal data fusion
  • physiological signal processing
  • machine learning and AI
  • human–computer interaction
  • neurocognitive responses
  • ethical and privacy issues
  • applications in healthcare and education

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Published Papers (1 paper)

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Research

29 pages, 2769 KB  
Article
A Predictive Dual-Stage Neural Framework for Phase-Coherent Auditory Synthesis on Edge Devices
by Sathit Pairoch, Pattarapong Phasukkit and Teeraporn Suteewong
Sensors 2026, 26(11), 3344; https://doi.org/10.3390/s26113344 - 25 May 2026
Viewed by 296
Abstract
Real-time binaural beat synthesis in dynamic acoustic environments is challenged by carrier non-stationarity, interaural phase discontinuities, and processing delay in conventional digital signal processing pipelines. This study proposes a predictive dual-stage neural framework for phase-coherent auditory synthesis under non-stationary acoustic conditions. The framework [...] Read more.
Real-time binaural beat synthesis in dynamic acoustic environments is challenged by carrier non-stationarity, interaural phase discontinuities, and processing delay in conventional digital signal processing pipelines. This study proposes a predictive dual-stage neural framework for phase-coherent auditory synthesis under non-stationary acoustic conditions. The framework decouples real-time carrier estimation from phase-coherent signal generation through two specialized modules. An intelligent acoustic sensing module (AI-1) estimates time-varying carrier information across harmonic, fluctuating, and broadband acoustic profiles using a causal neural front-end with an adaptive confidence-driven strategy. A predictive phase-coherent generator (AI-2) then forecasts short-horizon carrier trajectories and drives a discrete-time phase accumulator to maintain continuous phase evolution during binaural beat embedding. Objective evaluation under multiple acoustic profiles and noise conditions shows that the proposed framework maintains strong phase continuity, with a Phase Coherence Factor greater than 0.91, and low artifact levels, with a Signal-to-Artifact Ratio greater than 39.8 dB, under the evaluated conditions. Additional comparisons with conventional DSP baselines, stronger classical F0 estimators, a lightweight neural F0 tracker, and component-wise ablation variants further demonstrate that the performance improvement arises from the combination of adaptive carrier estimation and predictive phase-coherent actuation, rather than from carrier estimation alone. Hardware profiling shows a combined INT8 inference time of 2.4 ms per frame on a resource-constrained Raspberry Pi Zero 2W-class edge device. Importantly, this inference time and the sub-millisecond phase-accumulator resolution should not be interpreted as sub-millisecond end-to-end physical audio latency. The complete system still includes buffering, framing, neural inference, and output processing delay; the proposed method instead reduces effective phase-boundary misalignment through short-horizon predictive compensation. These results support the proposed framework as a lightweight engineering solution for real-time phase-continuous auditory synthesis in dynamic listening environments. The reported PCF and SAR values should be interpreted as signal-level indicators of phase continuity and artifact suppression, rather than as evidence of listener comfort, perceptual preference, or neurophysiological efficacy. Full article
(This article belongs to the Special Issue Human Emotion Recognition and Reactions Through Sensor Technologies: Findings, Challenges, Opportunities and Future Directions)
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