MoodPupilar: Predicting Mood Through Smartphone Detected Pupillary Responses in Naturalistic Settings
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909279514001408 |
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| author | Islam, Rahul Zhang, Tongze Bisen, Priyanshu Singh Bae, Sang Won |
| author_facet | Islam, Rahul Zhang, Tongze Bisen, Priyanshu Singh Bae, Sang Won |
| contents | MoodPupilar introduces a novel method for mood evaluation using pupillary response captured by a smartphone's front-facing camera during daily use. Over a four-week period, data was gathered from 25 participants to develop models capable of predicting daily mood averages. Utilizing the GLOBEM behavior modeling platform, we benchmarked the utility of pupillary response as a predictor for mood. Our proposed model demonstrated a Matthew's Correlation Coefficient (MCC) score of 0.15 for Valence and 0.12 for Arousal, which is on par with or exceeds those achieved by existing behavioral modeling algorithms supported by GLOBEM. This capability to accurately predict mood trends underscores the effectiveness of pupillary response data in providing crucial insights for timely mental health interventions and resource allocation. The outcomes are encouraging, demonstrating the potential of real-time and predictive mood analysis to support mental health interventions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01855 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MoodPupilar: Predicting Mood Through Smartphone Detected Pupillary Responses in Naturalistic Settings Islam, Rahul Zhang, Tongze Bisen, Priyanshu Singh Bae, Sang Won Human-Computer Interaction MoodPupilar introduces a novel method for mood evaluation using pupillary response captured by a smartphone's front-facing camera during daily use. Over a four-week period, data was gathered from 25 participants to develop models capable of predicting daily mood averages. Utilizing the GLOBEM behavior modeling platform, we benchmarked the utility of pupillary response as a predictor for mood. Our proposed model demonstrated a Matthew's Correlation Coefficient (MCC) score of 0.15 for Valence and 0.12 for Arousal, which is on par with or exceeds those achieved by existing behavioral modeling algorithms supported by GLOBEM. This capability to accurately predict mood trends underscores the effectiveness of pupillary response data in providing crucial insights for timely mental health interventions and resource allocation. The outcomes are encouraging, demonstrating the potential of real-time and predictive mood analysis to support mental health interventions. |
| title | MoodPupilar: Predicting Mood Through Smartphone Detected Pupillary Responses in Naturalistic Settings |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2408.01855 |