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학술지 Real-world Multimodal Lifelog Dataset for Human Behavior Study
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저자
정승은, 정치윤, 임정묵, 임지연, 노경주, 김가규, 정현태
발행일
202112
출처
ETRI Journal, v.권호미정, pp.1-12
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.2020-0446
협약과제
20ZS1100, 자율성장형 복합인공지능 원천기술 연구, 송화전
초록
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.
키워드
data collection, human behavior pattern, lifelog, real-world dataset
KSP 제안 키워드
Convolution neural network(CNN), Data collection method, Electrodermal activity, Emotional states, Human Behavior Pattern, Human activity recognition(HAR), Learning model, Lifelog data, Mobile Sensing, Natural conditions, Quality data
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