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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.

A concentration of people attracted to urban areas or large events raises demand for crowd traffic control systems. Such systems need to achieve a collective control for dealing with the crowd interacting with each other, as well as a flexible control for managing the traffic depending on application scenarios such as swift passage on streets or safety-guaranteed traffic. This paper presents a Bayesian optimization (BO) approach using a multi-agent simulation for efficiently finding a control of traffic: the multi-agent simulation considers the interaction of people changing their routes for a collective control, whereas the black-box nature of BO is capable of replacing the optimization metric for different scenarios without algorithmic modification to achieve flexible traffic management. Experiments use two maps: synthetic grid-like streets and the Enoshima Coast firework festival site in Japan. In both cases, the results demonstrate that our traffic control is effective for optimizing various metrics such as an efficiency-oriented arrival rate, a safety-oriented low traffic rate, and their combination.

Machine learning is a promising technology for analyzing diverse types of big data. The Internet of Things era will feature the collection of real-world information linked to time and space (location) from all sorts of sensors. In this paper, we discuss spatio-temporal multidimensional collective data analysis to create innovative services from such spatio-temporal data and describe the core technologies for the analysis. We describe core technologies about smart data collection and spatio-temporal data analysis and prediction as well as a novel approach for real-time, proactive navigation in crowded environments such as event spaces and urban areas. Our challenge is to develop a real-time navigation system that enables movements of entire groups to be efficiently guided without causing congestion by making near-future predictions of people flow. We show the effectiveness of our navigation approach by computer simulation using artificial people-flow data.

The datafication of spatio-temporal city-wide events is one essential factor for smart management of the city. For this purpose, the combination of real-time event detection on edge sensor nodes mounted public vehicles, and event accumulation on a server is one realistic and efficient solution. We can analyze the accumulated data to understand complex phenomena occurring in entire the city. In this paper, we introduce a novel datafication procedure of city-wide events by sensor mounted garbage trucks and evaluated the preliminary implementation of event detection system on actual vehicle-mounted sensors.

In this paper, we propose a method for directly measuring and analyzing driving behavior using wireless 3D-accelerometers. Whereas existing systems installed many sensors into a specially equipped test vehicle to indirectly measure driving behaviors, our method uses wireless 3D-accelerometers attached to a driver for directly measuring his/her behaviors in a vehicle. After applying independent component analysis (ICA) to reduce car-caused noise, our method detects anomalies in driving behaviors using one-class SVM. By directly measuring driving behavior, our method allows to point out anomalies in driving behaviors characteristics to novice drivers with precision of 71.1% and recall of 73.9%.

This paper presents the proposal for the design of a measurement of cognitive process of nurses at acute care setting when they make cognitive error. nurses especially at acute care setting perform their numerous nursing tasks simultaneously in time-stressed, information-rich situation. To cope with these severe cognitive demands expertise nurses develop simplification strategies not to consume their mental resources so much. However medical errors happen, regardless of nurses' experience. The resource allocation theory of Norman and Bobrow suggests that inappropriate selection of simplification strategy when the task is needed more attention induce the cognitive error. In this paper too much mental workload is presumed one of the reasons to make inappropriate selection. And the study design to measure cognitive process under the different mental workload situation by talk aloud protocol and psycho-physiological response is proposed.

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.

We propose a method for recognizing workers' routine activities by combining location information and body movements of a user. We describe methods for capturing and recognizing the nursing context with an infrared-ID location sensor system and accelerometers worn by a user. We show experimental results of recognizing typical activities in a nursing scenario in a laboratory setting.

This paper presents a wearable sensing device called "B-Pack", which is designed to capture nursing activities in real hospitals. To provide usability and flexibility, B-Pack features wireless connectivity using Bluetooth and a rechargeable battery with on-board charging circuitry. Furthermore, to acquire synchronized data on distributed parts of wearer's body, B-Pack features a time synchronization protocol. Through experiments and interviews, we confirmed that the data from each body part was appropriately collected for nursing activity recognition and that the design is suitable for a real nursing environment.

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.

This study clarified that a robot tracking a user can influence user decision-making. Two types of robot were used and the robots tracked or did not track the user. A laboratory experiment (n = 118) tested differences in selection ratios corresponding to a color name recommended by the robots. Subjects selected the recommended color name significantly more often when the robot tracked subjects' faces than in the no-recommendation situation, however, when the robot did not track subjects' faces with recommendation, there was no significant difference. The physiological data also showed that the tracking condition generated higher levels of arousal than the non-tracking condition. This suggests that tracking the user's face influences user decision-making.

This paper describes a type of multiagent-based distributed manipulator control in which the execution of a task involves the interaction between multiple software modules called agents. The intent of this work is to integrate a model-based approach and a behavior-based approach under a multiagent control paradigm. This paradigm is applied to a manipulator control problem without the use of inverse kinematics. A conventional manipulator is thought of as a system composed of a group of spatially distributed freedoms, each of which is independently controllable. Our manipulator control system has multiple functional agents each of which individually attempts to contribute in a positive way toward improving the degree of the given task achievement. The experimental system for multiagent-based distributed control includes a Lisp machine, two types of sensors (force/vision) and a manipulator. An attempt is made to evaluate our control paradigm by giving the system an example task that involves the end-effector movement between two points. Agents are implemented as multiple concurrent processes programmed in Lisp.

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.