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The disinfection of environmental surfaces is an effective countermeasure for COVID-19. In this paper, we use a thermographic camera and lightweight background image processing and propose a method that detects and visualizes the places touched by a person. Our method will support effective disinfection activities.

In this paper, we propose PASNIC, a lightweight privacy-aware sensor node for image capturing. PASNIC masks the region of faces and private screens (smartphone and portable computer displays) in captured visible images before sending them to remote sites. Thermal information, which can also be captured with a thermal image sensor, is used to detect the regions of faces and private screens. Our masking algorithms are so simple that even sensor nodes with low processing power can sufficiently process them.

We present how crowdsensing with civil officers contribute to collect larger amount of urban data with high spatial-temporal coverage, and enable to understand urban features by analyzing the data. We implemented a crowdsensing system which fits to daily city management, and conducted long-term experiment with garbage section of Fujisawa city, Japan. Based on analysis of collected data, we show that our system provide fine-grained urban data compared to crowdsensing with citizens with improving efficiency of city management. In addition, we analyze our dataset with demographic urban data, and clarified the relationship between pattern of uncollectible garbage thrown and features of resident area.

We propose a method that interpolates missing values from sensor nodes in a sensor network using Nonnegative Matrix Factorization. Since nearby sensor nodes take approximate values, more reliable interpolation is possible with these values. We carried out experiments and evaluations using the data of sensors deployed in a real environment.

Garbage is intricately with our daily life. We are investigating method that estimates regional amounts of garbage using motion sensors mounted on garbage trucks. In this paper, we report out analysis results of garbage amounts using national census data. We could obtain insightful information from just motion sensors we mounted on garbage trucks.

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 city's individual's 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.

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 user's 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 user's environment.

When using wearable computers used for medical operation and aviation safety, disrupted information presentation due to hardware/software problems can have serious consequences including medical accidents. We propose a mechanism that maintains information presentation in such situatoins by I/O device bypassing. In out method, I/O devices directly communicate with other devices if a system failure happens. The proposed method selects appropriate data converters by considering recognizability in order to present information that is easily understandable to users. We confirmed that the proposed method works effectively by implementing several applications with our prototype system.

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.

The new concept proposed in this paper is a query free web search that automatically retrieves a web page including information related to the daily activity that we are currently engaged in for automatically displaying the page on Internet-connected domestic appliances around us such as televisions. When we are washing a coffee maker, for example, a web page is retrieved that includes tips such as `cleaning a coffee maker with vinegar removes stains well.' A method designed on the basis of this concept automatically searches for a web page by using a query constructed from the use of ordinary household objects that is detected by sensors attached to the objects. An in-situ experiment tests a variety of IR techniques and the experiment confirmed that our daily activities can produce related web pages with high accuracy.

Car navigation systems provide the best route to a destination quickly and effectively. However, during daily driving, this information is not necessary since drivers already know the route to the destination very well. In addition, it is time-consuming for drivers to input the destination. Thus, our research group has proposed a new car navigation system that provides information related to the destination by predicting the user's destination automatically. We propose the use of a new method that predicts the destination on the basis of the driving trajectory and the contexts in which the user drives. A system that uses our method knows the destination without user interaction and provides information related to the correct destination.

Motorbike racing is one of the most popular motorsports and many fans visit circuits to watch races. However, since audiences and pit crews can only obtain limited information, it is difficult for them to get detailed information about teams except for high ranking teams and few popular teams. To solve this problem, we have already presented an information browsing system for pit crews who used wearable computers. This paper describes an extension to the system. We also applied our system to racing audiences. Since our system can provide various information such as detailed ranking tables and favorite teams, it can make supporting the teams more advantageous and entertain audiences much more.

In this paper, we introduce our 's-room' project as well as the tagging strategies and environment developed for the project. In the s-room, many small sensor nodes are attached to various objects. Our project aims to construct a system for comprehending real-world events and the properties or status information of physical objects by utilizing sensor nodes distributed throughout the room as well as general knowledge obtained from web space. The events extracted in the s-room are then published as web contents. We defined a set of event descriptors as a middle language between the sensor data stream and natural language description. The descriptors are selected by a two-way method: 1) a top-down approach based on definitions in NL-dictionaries and laws in physics, 2) a bottom-up approach based on manually tagged sensor data streams. We also developed a tagging environment that enables us to arrange the relationship between NL phrase expressions of human activities and multiple sensor events automatically extracted from the sensor signal streams.