Recently, evaluation of Quality of Life (QoL) in everyday life has been regarded as important. It will be possible to provide in-home services which improve QoL if the stress level of the dweller while performing daily activities (e.g., cleaning, cooking, etc.) which take temporal and physical burden is recognized during daily life. In this study, we analyze the stress level while performing daily activities by the LF/HF ratio known as a stress evaluation index based on heartbeat. As an experiment, we asked five participants to live in our smart home testbed wearing wearable device (Empatica E4 wristband) for four days each, and collected biometric data including heartbeat and volume pulse wave while recording activity labels. Through analysis of the collected data, we report the relation between the daily living activities and the stress index.

Declining birthrate and aging population are common problems of developed countries. These cause increasing of single life of the elderly, and finally, increase the risk of lonely death of them. One of the best precautions against lonely death is the safety confirmation by a third person. So, the service of that for the elderly is spreading in various forms now. However, the service is not popular because of some kinds of problems, for example, workload, technical difficulty, and privacy. Therefore, the method using ambient sensors in the home has been proposed. In this method, some kinds of sensors are installed in living environment, such as motion sensor and door sensor and so on. These sensors have no personal information. The data is collected by sensors periodically and observed for safety confirmation. However, the sensor data have different meanings by lifestyle of residents. So, to determine the standard of safety, we have to consider the difference of lifestyle according to some conditions such as person and season. Hence, we aim to develop a new safety confirmation system which can optimize safety standard according to the resident's lifestyle. In this paper, we collected the sensor data of 19 elderly people for about 10 months and analyzed it as a first step of developing. As a result, we found the difference of sensor data in lifestyles according to residents and seasons.

The rent for restaurants is determined based on tacit knowledge such as experience and intuition cultivated by veteran sales man of real estate companies. Determinants of the rent include static information that is specific to the property, dynamic information that is around the property, potential information that include features of the property. Potential information is difficult to index by veteran sales man. In this research, we propose a method using catch phrase given to property as potential information. We use the catch phrase that was vectorized by Doc2Vec, and in order to remove noise, we selected part of speech. As a result, the estimation accuracy was higher to select part of speech, and when multiple regression analysis was used, the highest coefficient of determination 0.611 was obtained.

The evaluation function for an imperfect information game is always hard to define but has a significant impact on the playing strength of a program. Deep learning has made great achievements in several recent years, and already exceeded the level of top human players in perfect information games such as AlphaGo. Predicting opponents moves and hidden states is important in imperfect information games. This paper describes a model on building a Mahjong artificial intelligence with deep learning method and supervised learning theory. Four deep neural network for discarding and predicting opponents' waiting, waiting tiles and point changes are combined into one model and performs good during games. With improved feature engineering, our accuracies on validation data of these networks reach higher than Dr. Mizukami and Professor Tsuruoka's network.

In a large scale IoT system, structured and unstructured data are collected from various distributed sensors. It is important to visualize these data with an overview context composed of multiple views and interactively focus on some detail views to understand the current system status. But a unified VA (Visual Analysis) is difficult owing to due to being short of expressly relationship between distributed datasets from different sensors or processed subsets of big data. In this paper, we present a visualization framework to analytically acquire the relationship among distributed or processed subsets, integrate their views in a visualization context, and realize operation linkage between them.