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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.

The construction of real-world knowledge is required if we are to understand real-world events that occur in a networked sensor environment. Since it is difficult to select suitable ‘events’ for recognition in a sensor environment a priori, we propose an incremental model for constructing real-world knowledge. Labeling is the central plank of the proposed model because the model simultaneously improves both the ontology of real-world events and the implementation of a sensor system based on a manually labeled event corpus. A labeling tool is developed in accordance with the model and is evaluated in a practical labeling experiment.

In a remote or local environment in which a sensor network always collects data produced by sensors attached to physical objects, the engine presented here saves the data sent through the Internet and searches for data segments that correspond to real-world events by using natural language (NL) words in a query that are input in an web browser. The engine translates each query into a physical quantity representation searches for a sensor data segment that satisfies the representation, and sends back the event occurrence time, place, or related objects as a reply to the query to the remote or local environment in which the web browser displays them. The engine, which we expect to be one of the upcoming Internet services, exemplifies the concept of symbiosis that bridges the gaps between the real space and the digital space.

This demo paper describes our daily activity sensing and recognition system with a wrist-worn sensor device called WristSense. The wrist-worn device is equipped with an accelerometer and camera, and can send the sensor data to a Bluetooth-enabled smart phone. With the accelerometer, we capture the wearer's hand postures and hand movements. With the camera, we capture visual information related to an object that the wearer is holding. An object that the wearer is using relates strongly to the activity that the wearer is performing. For example, an image (frame) including a coffee maker that is captured when the wearer is making coffee can be useful for recognizing the activity of making coffee. Also, a logger application on a phone records sounds related to daily activities. We use the sensor data in an attempt to recognize high-level daily activities that involve object use in real time.

This paper describes how we recognize activities of daily living (ADLs) with our designed sensor device, which is equipped with heterogeneous sensors such as a camera, a microphone, and an accelerometer and attached to a user's wrist. Specifically, capturing a space around the user's hand by employing the camera on the wrist mounted device enables us to recognize ADLs that involve the manual use of objects such as making tea or coffee and watering plant. Existing wearable sensor devices equipped only with a microphone and an accelerometer cannot recognize these ADLs without object embedded sensors. We also propose an ADL recognition method that takes privacy issues into account because the camera and microphone can capture aspects of a user's private life. We confirmed experimentally that the incorporation of a camera could significantly improve the accuracy of ADL recognition.

A sensor networked environment is capable of observing real-world phenomena as sensor readings, however, there are difficulties in understanding real-world ‘events’ such as activities of daily life occurring in the environment. We propose an incremental model for constructing real-world knowledge to allow us to understand real-world events. The central plank of the proposed model is the labeling practice. In the model, both the ontology of real-world events and the implementation of a sensor system are simultaneously improved based on a manually labeled event corpus. A labeling tool is developed in accordance with the model and event vocabularies are evaluated in a practical labeling experiment.

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.

Accessing information when we are on the move is a key feature if mobile computing environments, and using an optical see-through head mounted display (HMD) is one of the most suitable ways to do this. Although the HMD can display information without interfering with the user’s view, when the sight behind the display is too complex or too bright, the information displayed can bee very difficult to see. To solve this problem, we have created a way of laying out information for the optical see-through HMD. The ideal area for displaying information is determined by evaluating the sight image behind the HMD captured by a pantoscopic camera mounted on it. Moreover, if there is no suitable area for displaying information, our method select involves using the sight image around users use to the ideal direction and instructing them to face the direction. Our method displays information to ideal areas.

In order to utilize the real world via computers by means of camera images in augmented reality systems, the concise coordinate information of the systems must be identified. Image-based approaches are promising for this purpose since they provide direct mapping from the camera images to the coordinate system for virtual objects, and therefore, no camera calibration is necessary. However, the conventional image-based markers are typically stickers, and the information they deliver is limited and susceptible to change dynamically. In this paper, we propose a location marking method, called VCC (visual computer communication), which uses more intelligent markers. In our method, a marker, consisting of 16 LEDs (light-emitting diodes), keeps blinking to provide both coordinate information and attached information such as an address or a URL.

In wearable computing environments, an optical see-through head mounted display (HMD) is one of the most suitable wearable devices for displaying information. Although the HMD can display information without interfering with the user's view, the displayed information is difficult to see when the HMD background is too complex or too bright. We have created an information layout method for the optical see-through HMD that takes the background into account. It determines the ideal area for displaying objects by evaluating the HMD background captured by a camera.