Futoshi Naya

Differences in effect of robot and screen agent recommendations on human decision-making

This paper compares the effect of a robots and on-screen agents recommendations on human decision-making using a quantitative evaluation method. We are interested in whether a robots physical body produces some differences in the effect or not. Previous research investigated the advantage of a physical body; however, the advantage was not clarified quantitatively and there was not enough evidence to give the results credibility. Our method based on quantitative evaluation clarifies the effect of a robots and on-screen agents behavior on user decision-making. Comparing a robots behavior with an on-screen agents, we show that the degree of the effect firmly depends on the interaction environment and that geometrical consistency between the interaction environment and embodied social agents (ESAs), which include robots and on-screen agents, is important in the recommendation situation.

Bluetooth-based indoor proximity sensing for nursing context awareness

This paper proposes a Bluetooth-based indoor proximity detection method for nursing context awareness. We exploit proximity between Bluetooth devices attached to people and objects for estimating 1) room-level proximity of people and objects, and 2) mutual proximity between moving people and objects. We show that the proximity information exchanged between several devices can be updated at a rate of more than 1 Hz by effectively choosing the timing parameters of Bluetooth inquiry mechanism. Empirical results of evaluating the receiver signal strength indicator (RSSI) at various distances between devices are also shown.

Practical Design of A Sensor Network for Understanding Nursing Activities

We have constructed a sensor network in a real hospital environment to develop a system that prevents medical accidents by monitoring nursing activities. The network has been carefully designed to be dependable for practical purposes even in a network-unready environment with considerations of safety and stability as well as the consistency of the obtained sensor data. This paper describes the design and implementation of our sensor network. Through an experiment of about one week, we confirmed that our design performs well in a practical environment and obtains consistent data among the sensors worn by nurses and installed in the environment. Since a hospital has strict limitations on the use of sensor network equipment, the design described in this paper provides a practical solution for the construction of sensor networks in many indoor applications

Transferring positioning model for device-free passive indoor localization

This paper proposes a new method that makes it easy for us to construct a positioning model for device-free passive indoor localization by using model transfer techniques. With device-free passive indoor positioning, a wireless sensor network is used to detect the movement of a person based on the fact that RF signals transmitted between a transmitter and a receiver are affected by human movement. However, because device-free passive indoor positioning relies on machine learning techniques, we must collect labeled training data at many training points in an end users environment. This paper proposes a method that transfers a signal strength model used for locating a person obtained in another environment (source environment) to the end user environment. With the transferred models, we can construct a positioning model for the end user environment inexpensively. Our evaluation showed that our method achieved almost the same positioning performance as a supervised method that requires labeled training data obtained in an end users environment.

SVD-based hierarchical data gathering for environmental monitoring

We introduce a new data compression method for efficient data gathering in hierarchical sensor networks. Our proposed method compresses sensor data sequences by decomposing them into local patterns and weight variables using Singular Value Decomposition (SVD). Our proposed method can achieve efficient data gathering for environmental monitoring.

Wireless sensor network system for supporting nursing context-awareness

We developed a wireless sensor network system for supporting context-awareness of nursing activities in hospitals. Our system is aimed at automated recording of nursing work, providing context-aware services to nurses and visualising analytical results from nursing histories. The system consists of heterogeneous devices utilising three kinds of wireless networks: a ZigBee TM (ZigBee is a registered trademark of the ZigBee Alliance)-based location sensor network, a Bluetooth TM (Bluetooth is a trademark owned by Bluetooth SIG, Inc., USA)-based body-area sensor network for capturing nurse activities and a Wi-Fi TM (Wi-Fi is a registered trademark of the Wi-Fi Alliance)-based network for communication between PDAs worn by nurses and a server PC. We illustrate the layered structure of the entire system as well as algorithms for estimating nurse locations and activities during their operations in a hospital. These estimated results are managed by a real-time database system with which nurse contexts can be visualised in real time on a server PC and/or PDAs. We also show empirical results evaluating the performance of retrieving, analysing and visualising detailed nursing contexts.

Agile Environmental Monitoring Exploits Rapid Prototyping and In Situ Adaptation

Agile environmental monitoring is a novel style of environmental-monitoring development that lets you rapidly develop sensor devices and monitoring systems through trial and error during continuous sensing. The authors developed three environmental-monitoring systems and adapted them in situ based on actual field requirements. This article is part of a special issue on smart cities.

Toward On-Demand Urban Air Quality Monitoring using Public Vehicles

For protecting the public health, many research groups have tried to capture dense air quality information using sensorized vehicles that travel within urban areas. However, it remains difficult to accumulate real-time and fine-grained air quality information to fulfill a wide variety of requests from citizens and authorities. In this paper, we propose on-demand urban air quality monitoring using public vehicles. By adapting the monitoring behavior of sensors to each citys individuals needs, smart cities can accumulate enough air quality information with just a few sensorized vehicles. This paper describes the mechanisms of remote update programs on sensor nodes mounted on public vehicles for on-demand monitoring. We adopted the mechanisms on sensorized public vehicles in Fujisawa City, Japan. The paper also reports our air quality monitoring field trial and describes a functional design for on-demand air quality monitoring.

A habitat-monitoring system for an endangered fish using a sensor network

Conservation of biodiversity is an important issue. We are investigating a system for monitoring the habitat of endangered fish (Japanese rosy bitterling) using a wireless sensor network. Accordingly, measurements are taken for dissolved oxygen (DO), water temperature, air temperature, humidity, and illuminance. In this paper, we describe this habitantmonitoring system for the rosy bitterling.

Estimating People Flow from Spatiotemporal Population Data via Collective Graphical Mixture Models

Thanks to the prevalence of mobile phones and GPS devices, spatiotemporal population data can be obtained easily. In this article, we propose a mixture of collective graphical models for estimating people flow from spatiotemporal population data. The spatiotemporal population data we use as input is the number of people in each grid cell area over time, which is aggregated information about many individuals; to preserve privacy, they do not contain trajectories of each individual. Therefore, it is impossible to directly estimate people flow. To overcome this problem, the proposed model assumes that transition populations are hidden variables and estimates the hidden transition populations and transition probabilities simultaneously. The proposed model can handle changes of people flow over time by segmenting time-of-day points into multiple clusters, where different clusters have different flow patterns. We develop an efficient variational Bayesian inference procedure for the collective graphical mixture model. In our experiments, the effectiveness of the proposed method is demonstrated by using four real-world spatiotemporal population datasets in Tokyo, Osaka, Nagoya, and Beijing.