Recognizing steps taken by people walking is a technique used in a method for estimating a person's indoor position called Pedestrian Dead Reckoning (PDR), and also plays a key role in health-care applications such as pedometers and activity trackers. Generally, accelerometers are used for step recognition, because the sensor signals exhibit clear periodic changes as the user walks. As an alternative, step recognition methods that use angular velocity or magnetic sensors to measure hip rotation have also been proposed. In this article, we propose another step recognition method that uses an air-pressure sensor. We discovered that air-pressure sensor values vary periodically as the user walks in cases such as when a smartphone is held in a hand and the user consciously swings their arms while walking, when it is placed in a sealed case attached to the waist, or when placed in a trouser pocket. We perform step recognition by applying a simple algorithm to these periodic signals, with digital filtering, extreme value detection, and thresholding. Experiments confirmed that steps were estimated with an error rate of approximately 7.5%. Performing sensing with alternate sensors in this way can help reduce the number of sensors needed in a device or system, contributing to reducing power consumption and device size. As an example, activity monitors sometimes calculate the number of steps using accelerometers, and the number of stairs ascended or descended using an air-pressure sensor. These research results could be used to implement an activity monitor that only uses an air-pressure sensor.

This paper demonstrates a new RFID based health management system with a low-power ARM microcomputer. RFID cards are usually used as identification in many companies. The developed RFID cards can measure exercise quantity of office workers by deploying a low-power microcomputer and sensors. It is well known that a quantity of exercise has a strong relation with a health condition. Therefore, a quantity of exercise is a key indicator to prevent chronic diseases. The developed system employs a standardized RFID system to communicate with RFID reader/writers. Therefore, it is easy to install the developed system by updating software for RFID reader/writers in entrance gates, doors, etc. This paper demonstrates the prototype system for health management system built on a low-power ARM microcomputer.

This paper demonstrates a new ID card based momentum measurement system for office workers' health managements in companies. Our system can measure office workers' momentum with a new ID card that implements a microprocessor, sensors and a RFID tag because an amount of momentum has a relation with a health condition and it is an important indicator to prevent chronic diseases. RFID based ID cards are widely used in office buildings to manage a security control and to record working hours. Therefore, companies can install the proposed system by updating software for a RFID reader/writer in entrance gates, doors, etc. The demonstration shows a prototype system for health management system with ID card based measurement devices of momentum.

There are many methods for indoor positioning. These methods are divided into the relative localization and absolute localization. In the relative localization, one widely used method is Pedestrian Dead Reckoning (PDR). Relative localization estimates the moving distance, orientation, and height of the pedestrian. However, relative localization has a problem caused by an accumulated error: the longer the path, the worse the accuracy of relative localization. There is another problem in the existing evaluating metrics: they compare only the actual location and the estimated location of the destination. Relative localization also has this evaluation problem. We propose PIEM: Path Independent Evaluation Metric for Relative Localization. PIEM is a path independent evaluation metric, considering the complexity of the path; distance, orientation, and height. Then we evaluate these three factors of relative localization in addition to the position. Our proposed method showed more consistent results for the complexity of the path than the existing methods of relative localization evaluation.

A highly accurate estimation method of 3-D pedestrian trajectories from walking activity sensing data is proposed. This method uses data from an accelerometer, a gyrometer, and an air pressure sensor, and does not require detailed information on the building structure. In activity sensing using wearable sensors, higher accuracy can be expected from detection of zones in which there is continuously little change in the states of the sensor signals than from detection of zones in which there are large changes in the sensor signals within a short time. We focus on such stability of sensing signal and, as an application example, we used the concept to estimate walking direction on the basis of stable walking zone detection using a gyrometer and estimation of movement between the floors of a building by detection of stable floor zones with an air pressure sensor. We then integrated these estimations to obtain a 3-D pedestrian trajectory. The results of evaluation experiments using an indoor pedestrian sensing corpus (HASC-IPSC) showed that this method achieved higher accuracy for both walking direction and movement between floors than was achieved by a method based on large changes in the sensor signals. We also confirmed that the cumulative error rate for estimation of the 3-D pedestrian trajectory was 1 m per 10 seconds of movement.

Up to now Scene Analysis has been based on the WiFi location estimation technique and it has been necessary to have a large scale database and a large amount of calculation. We propose a WiFi estimation method that uses little data or calculation. First of all we apply Gaussian Mixture Model to represent the large scale WiFi database to decrease the WiFi data by no less than 95%. Secondly, we apply Particle Filter to adjust the possible calculation quantity needed for the location estimation technique. As experimental result, we achieved real-time location estimation within 6~10m. Another important issue for Scene Analysis technique is the high cost of operation of the previous WiFi observation. Accordingly crowdsourcing approach was used, employing as system where some users could contribute and other uses could share. The ideal system is a composition of the Web and mobile terminal. WiFi data observed by mobile terminals is uploaded to a Web server where it is managed and integrated into GMM and large scale operations are carried out on data and calculations. When the lightweight modeled data is downloaded to the mobile-terminal, the mobile terminal then has the ability to carry out real-time location estimation independently.

Collaborative distributed tabletop activities involving real objects are complicated by invisibility factors introduced into the workspace. In this paper, we propose a technique called "remote lag" to alleviate the problems caused by the invisibility of remote gestures. The technique provides people with instant playback of remote gestures to recover from the missed context of coordination. To examine the effects of the proposed technique, we studied four-person groups who engaged in two mentoring tasks using physical objects with and without remote lags. Our results show that remote lags effectively alleviated the invisibility problems, resulting in fewer questions/confirmations and redundant instructions during collaboration. The technique also decreased the overall workload of workers as well as the temporal demands for both helpers and workers.

To accelerate and simplify human activity recognition research, we have been developing a data processing tool named "HASC Tool." As the activity corpus becomes huge, it is not simple to handle the large number of files because it takes a lot of time to process. In this paper, we propose a distributed data processing mechanism which is implemented in the HASC Tool. By using the system, we can simply scale the local system into distributed processing. We also show the preliminary experimental result.

Human activity recognition through the wearable sensor will enable a next-generation human-oriented ubiquitous computing. However, most of research on human activity recognition so far is based on small number of subjects, and non-public data. To overcome the situation, we have gathered 4897 accelerometer data with 116 subjects and compose them as HASC2011corpus. In the field of pattern recognition, it is very important to evaluate and to improve the recognition methods by using the same dataset as a common ground. We make the HASC2011corpus into public for the research community to use it as a common ground of the Human Activity Recognition. We also show several facts and results of obtained from the corpus.

In this paper, we present t-Room, the next generation video communication system we are developing. Our approach is to build rooms with identical layouts, including walls of display panels on which users and physical or virtual objects are all shown at life-size. In this way, the user space enclosed by t-Room's surrounding displays can be shared as a common space at any other site. In other words, the enclosed spaces overlap each other. This configuration effectively provides symmetric reproduction of the audio-visual information surrounding local and remote users and objects. The feeling provided by t-Room is different from that by conventional videoconferencing systems, since there is no spatial barrier separating users such as the video screen of a conventional videoconferencing system. Furthermore, t-Room benefits in every way from Next Generation Network (NGN) technology: QoS, service productivity, and security. We view t-Room as a future form of telephone service.