The purpose of this study is to clarify the evaluation structure of visual environment in residential spaces with windows and to test the hypothesis that satisfaction with daylighting quality is ensured if view quality is good enough. Firstly, we conducted a questionnaire survey for 371 residents to assess the impressions of windows and visual environment. Using covariance structure analysis, pass diagrams of evaluation on lighting quality and view through windows were obtained with observed and latent variables. As a result, it was statistically clarified that the lighting quality and satisfaction of the visual environment is greatly affected by view.
The purpose of this study was to develop a model for estimating wake time using only time-series electricity consumption data and to verify its accuracy.
Presence/absence, sleeping/non-sleeping, air conditioner on/off, and electricity consumption data obtained from a survey of 12 single elderly people over a one-year period were used.
The model classifies people into two categories: active and inactive, and estimates their wake time from the results of the two classifications.
When the two factors that reduced accuracy were excluded, it was possible to estimate wake times within one hour of estimation error on 278 days of the year.
This study conducted a survey of office workers via an Internet questionnaire and statistically analyzed the results to quantitatively understand the relationship among office environment, employee wellness, organizational strength, and corporate value.
This study revealed the following two main points.
1. The results of correlation and multiple regression analysis indicated that the office environment is associated with employee wellness, organizational strength, and corporate value, suggesting that office environment improvement is an important factor in health management.
2. The results of two-way ANOVA indicated that office environment improvement may have some effect on most types of companies.
When introducing robots and sensors into living spaces, predicting turning movements while walking is an important element to ensure safety.
In this study, we assumed a living space, set experimental conditions that did not interfere with the subjects’ free movement, and measured natural turning movements.
First, the parameters used for prediction were selected from the measured data. Then, using those parameters, he built a system that predicted 90-degree turning movements in real time. Finally, we verified the system.
This research will contribute to the development and practical application of an analysis and prediction system for turning movements during walking.