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Journal Article
Real‐world multimodal lifelog dataset for human behavior study
- Authors
- Seungeun Chung, Chi Yoon Jeong, Jeong Mook Lim, Jiyoun Lim, Kyoung Ju Noh, Gague Kim, Hyuntae Jeong
- Issue Date
- 2022-06
- Citation
- ETRI Journal, v.44, no.3, pp.426-437
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- Abstract
- To understand the multilateral characteristics of human behavior and physiological markers related to physical, emotional, and environmental states, extensive lifelog data collection in a real-world environment is essential. Here, we propose a data collection method using multimodal mobile sensing and present a long-term dataset from 22 subjects and 616 days of experimental sessions. The dataset contains over 10 000 hours of data, including physiological, data such as photoplethysmography, electrodermal activity, and skin temperature in addition to the multivariate behavioral data. Furthermore, it consists of 10 372 user labels with emotional states and 590 days of sleep quality data. To demonstrate feasibility, human activity recognition was applied on the sensor data using a convolutional neural network-based deep learning model with 92.78% recognition accuracy. From the activity recognition result, we extracted the daily behavior pattern and discovered five representative models by applying spectral clustering. This demonstrates that the dataset contributed toward understanding human behavior using multimodal data accumulated throughout daily lives under natural conditions.
- KSP Keywords
- Behavior pattern, Convolution neural network(CNN), Data collection method, Electrodermal Activity, Emotional states, Human activity recognition, Lifelog data, Mobile Sensing, Natural conditions, Network-based, Quality data
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
