Personalized Emotion Detection from Floor Vibrations Induced by Footsteps
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917946985545728 |
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| author | Wu, Yuyan Dong, Yiwen Vaid, Sumer Harari, Gabriella M. Noh, Hae Young |
| author_facet | Wu, Yuyan Dong, Yiwen Vaid, Sumer Harari, Gabriella M. Noh, Hae Young |
| contents | Emotion recognition is critical for various applications such as early detection of mental health disorders and emotion based smart home systems. Previous studies used various sensing methods for emotion recognition, such as wearable sensors, cameras, and microphones. However, these methods have limitations in long term domestic, including intrusiveness and privacy concerns. To overcome these limitations, this paper introduces a nonintrusive and privacy friendly personalized emotion recognition system, EmotionVibe, which leverages footstep induced floor vibrations for emotion recognition. The main idea of EmotionVibe is that individuals' emotional states influence their gait patterns, subsequently affecting the floor vibrations induced by their footsteps. However, there are two main research challenges: 1) the complex and indirect relationship between human emotions and footstep induced floor vibrations and 2) the large between person variations within the relationship between emotions and gait patterns. To address these challenges, we first empirically characterize this complex relationship and develop an emotion sensitive feature set including gait related and vibration related features from footstep induced floor vibrations. Furthermore, we personalize the emotion recognition system for each user by calculating gait similarities between the target person (i.e., the person whose emotions we aim to recognize) and those in the training dataset and assigning greater weights to training people with similar gait patterns in the loss function. We evaluated our system in a real-world walking experiment with 20 participants, summing up to 37,001 footstep samples. EmotionVibe achieved the mean absolute error (MAE) of 1.11 and 1.07 for valence and arousal score estimations, respectively, reflecting 19.0% and 25.7% error reduction compared to the baseline method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04190 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Personalized Emotion Detection from Floor Vibrations Induced by Footsteps Wu, Yuyan Dong, Yiwen Vaid, Sumer Harari, Gabriella M. Noh, Hae Young Systems and Control Human-Computer Interaction Signal Processing Emotion recognition is critical for various applications such as early detection of mental health disorders and emotion based smart home systems. Previous studies used various sensing methods for emotion recognition, such as wearable sensors, cameras, and microphones. However, these methods have limitations in long term domestic, including intrusiveness and privacy concerns. To overcome these limitations, this paper introduces a nonintrusive and privacy friendly personalized emotion recognition system, EmotionVibe, which leverages footstep induced floor vibrations for emotion recognition. The main idea of EmotionVibe is that individuals' emotional states influence their gait patterns, subsequently affecting the floor vibrations induced by their footsteps. However, there are two main research challenges: 1) the complex and indirect relationship between human emotions and footstep induced floor vibrations and 2) the large between person variations within the relationship between emotions and gait patterns. To address these challenges, we first empirically characterize this complex relationship and develop an emotion sensitive feature set including gait related and vibration related features from footstep induced floor vibrations. Furthermore, we personalize the emotion recognition system for each user by calculating gait similarities between the target person (i.e., the person whose emotions we aim to recognize) and those in the training dataset and assigning greater weights to training people with similar gait patterns in the loss function. We evaluated our system in a real-world walking experiment with 20 participants, summing up to 37,001 footstep samples. EmotionVibe achieved the mean absolute error (MAE) of 1.11 and 1.07 for valence and arousal score estimations, respectively, reflecting 19.0% and 25.7% error reduction compared to the baseline method. |
| title | Personalized Emotion Detection from Floor Vibrations Induced by Footsteps |
| topic | Systems and Control Human-Computer Interaction Signal Processing |
| url | https://arxiv.org/abs/2503.04190 |