상세정보
학술지
Real-world Multimodal Lifelog Dataset for Human Behavior Study
- 발행일
- 202206
- 출처
- ETRI Journal, v.44 no.3, pp.426-437
- ISSN
- 1225-6463
- 출판사
- 한국전자통신연구원 (ETRI)
- 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.
- KSP 제안 키워드
- Convolution neural network(CNN), Data collection method, Electrodermal activity, Emotional states, Human activity recognition(HAR), Learning model, Lifelog data, Mobile Sensing, Natural conditions, Quality data, Real-world
본 저작물은 공공누리 제4유형 : 출처표시 + 상업적 이용금지 + 변경금지 조건에 따라 이용할 수 있습니다.
