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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.03698 |
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| _version_ | 1866915429701648384 |
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| author | Oh, Se Won Jeong, Hyuntae Chung, Seungeun Lim, Jeong Mook Noh, Kyoung Ju Lee, Sunkyung Jung, Gyuwon |
| author_facet | Oh, Se Won Jeong, Hyuntae Chung, Seungeun Lim, Jeong Mook Noh, Kyoung Ju Lee, Sunkyung Jung, Gyuwon |
| contents | Improving human health and well-being requires an accurate and effective understanding of an individual's physical and mental state throughout daily life. To support this goal, we utilized smartphones, smartwatches, and sleep sensors to collect data passively and continuously for 24 hours a day, with minimal interference to participants' usual behavior, enabling us to gather quantitative data on daily behaviors and sleep activities across multiple days. Additionally, we gathered subjective self-reports of participants' fatigue, stress, and sleep quality through surveys conducted immediately before and after sleep. This comprehensive lifelog dataset is expected to provide a foundational resource for exploring meaningful insights into human daily life and lifestyle patterns, and a portion of the data has been anonymized and made publicly available for further research. In this paper, we introduce the ETRI Lifelog Dataset 2024, detailing its structure and presenting potential applications, such as using machine learning models to predict sleep quality and stress. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03698 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Understanding Human Daily Experience Through Continuous Sensing: ETRI Lifelog Dataset 2024 Oh, Se Won Jeong, Hyuntae Chung, Seungeun Lim, Jeong Mook Noh, Kyoung Ju Lee, Sunkyung Jung, Gyuwon Signal Processing Human-Computer Interaction Machine Learning Improving human health and well-being requires an accurate and effective understanding of an individual's physical and mental state throughout daily life. To support this goal, we utilized smartphones, smartwatches, and sleep sensors to collect data passively and continuously for 24 hours a day, with minimal interference to participants' usual behavior, enabling us to gather quantitative data on daily behaviors and sleep activities across multiple days. Additionally, we gathered subjective self-reports of participants' fatigue, stress, and sleep quality through surveys conducted immediately before and after sleep. This comprehensive lifelog dataset is expected to provide a foundational resource for exploring meaningful insights into human daily life and lifestyle patterns, and a portion of the data has been anonymized and made publicly available for further research. In this paper, we introduce the ETRI Lifelog Dataset 2024, detailing its structure and presenting potential applications, such as using machine learning models to predict sleep quality and stress. |
| title | Understanding Human Daily Experience Through Continuous Sensing: ETRI Lifelog Dataset 2024 |
| topic | Signal Processing Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2508.03698 |