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Advanced Sensors–Based Emotion Sensing and Recognition (2nd Edition)

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

Deadline for manuscript submissions: 25 October 2026 | Viewed by 687

Special Issue Editor


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Guest Editor
Psychological Process Team, Robotics Project, BZP, RIKEN, Kyoto 619-0288, Japan
Interests: emotion; facial expression; social interaction; human-robot interaction; neuroimaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, emotion sensing and recognition has become one of the most active topics in artificial intelligence research, and a wide range of approaches to emotion recognition using various sensors has been extensively investigated. Advanced sensors include, but are not limited to, EEG, ECG, and EMG sensors, biometric sensors, wearable sensors, cameras, smartphones, and depth sensors.

This Special Issue aims to collect original research articles and review papers focused on emotion sensing and recognition, with an emphasis on advanced sensing technologies, novel data analysis methods, and their applications.

Topics and keywords include, but are not limited to, the following:

  • Emotion recognition/sensing technologies;
  • Wearable sensors for emotion recognition/sensing;
  • Dana analysis for emotion recognition/sensing;
  • Artificial intelligence for emotion recognition/sensing;
  • Affective computing;
  • Human–computer emotional

Dr. Wataru Sato
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • emotion sensing
  • emotion recognition
  • EEG/BCI/EMG/ECG
  • sensor devices
  • affective computing
  • human–computer emotional

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Published Papers (1 paper)

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Research

25 pages, 2401 KB  
Article
Multivariate Timing and Granger Causality Analysis of Spontaneous Facial Mimicry in Response to Android Dynamic Facial Expressions
by Chun-Ting Hsu, Anna Kelbakh, Dongsheng Yang, Takashi Minato and Wataru Sato
Sensors 2026, 26(6), 1881; https://doi.org/10.3390/s26061881 - 17 Mar 2026
Viewed by 413
Abstract
Although evidence exists for android-induced spontaneous facial mimicry, the timing and temporal precedence (Granger causality) of this effect remain uncertain. We used the Facial Action Coding System (FACS) to analyze simultaneous dyadic facial video recordings of participants observing android Nikola’s negative (frowning) and [...] Read more.
Although evidence exists for android-induced spontaneous facial mimicry, the timing and temporal precedence (Granger causality) of this effect remain uncertain. We used the Facial Action Coding System (FACS) to analyze simultaneous dyadic facial video recordings of participants observing android Nikola’s negative (frowning) and positive (smiling) dynamic facial expressions. Principal component analysis of Nikola’s expressions indicated that, in addition to the action units (AUs) 04 (brow lowerer) and 12 (lip-corner puller), AUs 25 (lips part) and 26 (jaw drop) contributed significantly to Nikola’s facial expressions. Cross-correlation analysis revealed AU04 mimicry of negative expressions and AU12 mimicry of positive expressions from 400 ms onwards. AU25 and AU26 mimicry occurred faster, starting at around 200 ms. Multilevel vector autoregression incorporated the android and participant AUs and quantified the temporal evolution of the Granger causality for the first time. In addition to paired android–human AU04, 12, 25, and 26 effects, significant Granger causality was found between different android–human AU combinations, such as from android AU04 to participant AU25 in the negative condition, and from android AU25 to participant AU12 in the positive condition. These results suggest that the spontaneous facial responses to Nikola’s expressions involved not only motor copying, but also higher-level goal emulation and motor planning in the mirror mechanism, supporting the reliability of the social function of android facial expressions. Cross-correlation and Granger causality analysis can be valuable when further investigating behavioral matching in real-life contexts. Full article
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