Harnessing Haptic Technology for Real-Time Emotion Detection

Lital Levy; Yuval Blum; Asmare Ambaw; Roi Yozevitch; Eldad Holdengreber

doi:10.1109/LSENS.2025.3538804

Harnessing Haptic Technology for Real-Time Emotion Detection

Lital Levy, Yuval Blum, Asmare Ambaw, Roi Yozevitch, Eldad Holdengreber

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

This letter introduces a novel multi-modal environmental translator for real-time emotion recognition. The system integrates facial expression recognition (FER) and speech emotion recognition (SER) to analyze visual and vocal cues while conveying emotional feedback through vibrotactile signals. Emotions are mapped to distinct vibration frequencies - ranging from 0.4 Hz for neutral to 35 Hz for anger - enabling users to identify seven core emotions through tactile sensations intuitively. A user study involving ten participants demonstrated an average adaptation time of fewer than 7 min, indicating the system's effectiveness in quickly familiarizing users with the vibration signals. Overall, this innovative solution provides a robust approach to enhancing real-time emotion recognition through haptic feedback, making it suitable for everyday social interactions.

Original language	English
Article number	5500804
Journal	IEEE Sensors Letters
Volume	9
Issue number	3
DOIs	https://doi.org/10.1109/LSENS.2025.3538804
State	Published - 2025

Keywords

Sensor systems
emotion recognition
facial expression recognition (FER)
haptic feedback
machine learning (ML)
sensory substitution
speech emotion recognition (SER)

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10.1109/LSENS.2025.3538804

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title = "Harnessing Haptic Technology for Real-Time Emotion Detection",

abstract = "This letter introduces a novel multi-modal environmental translator for real-time emotion recognition. The system integrates facial expression recognition (FER) and speech emotion recognition (SER) to analyze visual and vocal cues while conveying emotional feedback through vibrotactile signals. Emotions are mapped to distinct vibration frequencies - ranging from 0.4 Hz for neutral to 35 Hz for anger - enabling users to identify seven core emotions through tactile sensations intuitively. A user study involving ten participants demonstrated an average adaptation time of fewer than 7 min, indicating the system's effectiveness in quickly familiarizing users with the vibration signals. Overall, this innovative solution provides a robust approach to enhancing real-time emotion recognition through haptic feedback, making it suitable for everyday social interactions.",

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AU - Levy, Lital

AU - Blum, Yuval

AU - Ambaw, Asmare

AU - Yozevitch, Roi

AU - Holdengreber, Eldad

PY - 2025

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AB - This letter introduces a novel multi-modal environmental translator for real-time emotion recognition. The system integrates facial expression recognition (FER) and speech emotion recognition (SER) to analyze visual and vocal cues while conveying emotional feedback through vibrotactile signals. Emotions are mapped to distinct vibration frequencies - ranging from 0.4 Hz for neutral to 35 Hz for anger - enabling users to identify seven core emotions through tactile sensations intuitively. A user study involving ten participants demonstrated an average adaptation time of fewer than 7 min, indicating the system's effectiveness in quickly familiarizing users with the vibration signals. Overall, this innovative solution provides a robust approach to enhancing real-time emotion recognition through haptic feedback, making it suitable for everyday social interactions.

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Harnessing Haptic Technology for Real-Time Emotion Detection

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