상세정보
학술대회
An Empirical Study on Finding Experience Sampling Parameters to Explain Sleep Quality based on Dimension Reduction
- 발행일
- 201910
- 출처
- International Conference on Information and Communication Technology Convergence (ICTC) 2019, pp.1295-1299
- 협약과제
- 19ZS1100, 자율성장형 AI 핵심원천기술 연구, 송화전
- 초록
- The ground truth which is given a set of sensor data from life-long data whose definition are ambiguous, it is important to develop the way self-reporting without user intervention. The imperatively necessary items of self-reporting with the minimum number can be one solution. Therefore, we suggest the method that reduce the number of items of self-reporting with investigate representative items of others. Then we collect the data in real life and evaluate our proposed method. Before data collection, the items of self-reporting have been designed exquisitely for investigating critical activities and context for sleep quality. We then designed an experiment that collects both passive sensing data for sleep quality and self-reported data for daily activities to investigate activities and context closely relate to sleep quality. We reduced the number of items of self-reports in a manner of exploratory data analysis on this data and evaluate the results through logistic regression. The results indicate that a small number of items which is shown to be important can be considered for consisting items of self-reporting.
- KSP 제안 키워드
- Daily activity, Data Collection, Dimension Reduction, Empirical study, Experience sampling, Exploratory Data Analysis, Logistic Regression(LR), Passive Sensing, Representative items, Sampling parameters, Sensing data