In recent years, smart buildings have garnered attention for their ability to integrate various systems and devices within a building, allowing for centralized data platform-based control to achieve purposes such as energy management. Smart building data platforms are required to provide vendor-agnostic APIs (for device control and data acquisition) and simplify device addition and operation. In this paper, we design a platform architecture that fulfills those requirements. The differing IoT gateway functionalities for each device are dynamically deployed to the IoT gateway. The devices are abstracted into metrics, which are a type of data, and the API is called with the metric and the building structure in which the device is located. Furthermore, we propose a method to standardize metric names of the same type across the platform. The designed platform is implemented and verified.

In Building Operating Systems (BOS), developing new applications often poses a risk of unintentionally affecting live environments, making safe and efficient development difficult. This challenge hinders the realization of a development approach that integrates development, testing, and operations within the BOS environment. To address this issue, it is essential to establish an access control mechanism that allows application behavior to be defined and validated under production-equivalent conditions, without impacting the live system. This paper proposes an authorization gateway architecture that meets this requirement by integrating OAuth 2.0 with a sandbox mechanism implemented as an overlay structure. The proposed Overlay based sandbox enables temporary and revocable permission assignment directly on the production infrastructure without modifying permanent rules. The architecture is implemented using a container-based deployment model and evaluated through performance profiling and comparative analysis. Experimental results confirm that the proposed method achieves flexible and secure access control with minimal overhead, providing a practical foundation for safe application development and continuous deployment on BOS platforms.

Recently, with the spread of smart houses, the smartness of housing equipment and home appliances has progressed, and the functionality and usability of interfaces between people and equipment and between people and home appliances have become important factors. Currently, the main interfaces are remote controls, smartphone applications, and even voice recognition. Furthermore, research is also being conducted on interfaces that can be operated without having the device at hand using cameras and radio waves. However, special equipment must be installed for operation, and compatibility with room design has become an issue. In this research, we proposed a system to transform existing furniture into an interface rather than providing a new interface. The proposed system focused on vibration sensors that are small, inexpensive, and can be attached to existing furniture or hidden from view. To evaluate the proposed system, an experiment was conducted to transform existing furniture into an interface for swiping by simply attaching the vibration sensor to the existing furniture. Specifically, the system attaches four vibration sensors with synchronized output signals to a table and uses a CNN to learn the vibration data obtained from the sensors to predict the direction of the swipe. As a result, when the table and person swiping were fixed, the system could predict the swipe with an accuracy of over 0.86.

In the field of crowd estimation, most non-visual approaches confine their objective to only crowd counting, whereas there are a number of vision-based researches which can estimate both the number and location of people. By observation, we figured out that the WiFi channel state information (CSI) also contains the potential characteristics for both estimations. In this paper, we propose a user-device-free simultaneous crowd estimation system that enables both crowd counting and localization simultaneously, by WiFi CSI and Machine Learning. The originality of this study is that we leverage the CSI bundles as the source for extracting features that contain characteristics depending on the dynamic state (counting) and static state (localization). By experiments during three different-time sessions, we confirm that we could achieve up to 94% counting accuracy and 95% localization accuracy by k-fold cross-validation.

A wearable based long-term lifelogging system is desirable for the purpose of reviewing and improving users lifestyle habits. Energy harvesting (EH) is a promising means for realizing sustainable lifelogging. However, present EH technologies suffer from instability of the generated electricity caused by changes of environment, e.g., the output of a solar cell varies based on its material, light intensity, and light wavelength. In this paper, we leverage this instability of EH technologies for other purposes, in addition to its use as an energy source. Specifically, we propose to determine the variation of generated electricity as a sensor for recognizing "places" where the user visits, which is important information in the lifelogging system. First, we investigate the amount of generated electricity of selected energy harvesting elements in various environments. Second, we design a system called EHAAS (Energy Harvesters As A Sensor) where energy harvesting elements are used as a sensor. With EHAAS, we propose a place recognition method based on machine-learning and implement a prototype wearable system. Our prototype evaluation confirms that EHAAS achieves a place recognition accuracy of 88.5% F-value for nine different indoor and outdoor places. This result is better than the results of existing sensors (3-axis accelerometer and brightness). We also clarify that only two types of solar cells are required for recognizing a place with 86.2% accuracy.

Abstract In recent years, many types of research and developments on behavior change have been conducted. The purpose of behavior change is to improve people's lifestyle pattern or to maintain the improvement for a long time with the aim to achieve a goal such as promoting health condition improvement. To achieve the foundation of a new lifestyle, it is necessary to recognize the daily life patterns of users and give triggers for behavior change to users in their daily life. To realize this, in our research, we develop an interactive signage which is able to identify and actively talk to the passing user and try to induce behavior change by sending visual and auditory stimulation. Then, we record users' reactions and upload them to the server. In this paper, we report the investigation result on users' reactions and feelings to the developed interactive signage. As a survey experiment, we set up four interactive signs on a floor of our university and asked 15 participants to carry a name tag with a Bluetooth Low Energy beacon during their daily life. Five kinds of tasks based on dialogue scenarios are posted to the approaching participants. Participants can respond to these tasks through a touchscreen. The period of the experiment was three weeks. To get the data in an ideal environment, during the first week, we asked all the participants to respond to the utterance from the interactive signage whenever they hear the voice message. During the next two weeks, participants were not asked to respond to the task definitely to get the data in the real environment. The result of the experiment showed that our proposed interactive signage could induce behavior change effectively. Based on the result of experiment, we updated our interactive signage system by adding response time (the time passed from showing contents until user respond), record function, and voice feedback function. Furthermore, to collect the data of response time that is considered as a part of users' reactions, we conducted an additional experiment with the same participants in previous experiment (except for one missing participant) for one week after updating the system. In the additional experiment, the participants were not asked to respond to the utterance definitely. As a result, it is shown that the behavior change by the proposed signage is still effectively induced. We also analyzed the relationship between the day passed and the response rate of each task type. The result shows that the number of ignorance of personal task and check task does not rise even as the time passes.

Human Activity Recognition (HAR) with body-worn sensors has been studied intensively in the past decade. Existing approaches typically rely on data from inertial sensors. This paper explores the potential of using point cloud data gathered from wearable depth cameras for on-body activity recognition. We discuss effects of different granularity in the depth information and compare their performance to inertial sensor based HAR. We evaluated our approach with a total of sixteen participants performing nine distinct activity classes in three home environments. 10-fold cross-validation results of KNN and Random Forests classification exhibit a significant increase in F-score from inertial data to depth information (by > 12 percentage points) and show a further improvement when combining low-resolution depth matrices and sensor data. We discuss the performance of the different sensor types for different contexts and show that overall, depth sensors prove to be suitable for HAR.

These days, smart home applications such as a concierge service for residents, home appliance control and so on are attracting attention. In order to realize these applications, we strongly believe that we need a system which recognizes the various human activities accurately with a low cost device. There are many studies which work on the activity recognition in the smarthome. Moreover, we also have proposed the activity recognition technique in the smarthome by utilizing the digitaloutput-PIR sensor, door sensor, watt meter. However, the study has the challenge: we cannot distinguish between the similar tiny activities at the same place: “eating” and “reading” with sitting on a sofa. In order to cope with this challenge, we introduce ALPAS: analog-output-PIR-sensor-based activity recognition technique which recognizes the detailed activities of the user. Our technique recognizes the activity of the user by utilizing the machine learning. We evaluated the proposed technique in a smarthome which belongs to the authors' university. In the evaluation, three subjects performed four different activities with sitting on a sofa. As a result, we achieved F-Measure: 57.0%.

Recently, as elderly people population grows, the burdens on caretakers are getting larger. In daycare centers, caretakers make a daycare report aiming to improve the senior citizen's Quality of Life. However, in the present situation, it is difficult for caretakers to record the senior citizen's activity in detail, since each caretaker needs to take care of several senior citizens at the same time. To reduce the burden of caretakers, many elderly monitoring systems have been proposed so far, but most of them are not effective in the sense that they force the senior citizen to use dedicated devices such as smart phone and/or particular applications that are obtrusive and cumbersome for care receivers. In this paper, we propose a semi-automatic care-taking report generation system which can monitor movements/activity of senior citizens in daycare centers. Our proposed system estimates multiple locations (areas) where senior citizens are located with the BLE beacon, by utilizing RSSI of the Bluetooth radio wave. Also, the accelerometer implemented in the tag estimates the activity of the elderly. The information of the estimated area and activity is stored in a server with time stamp. The server generates the daycare report based on it. In order to evaluate the proposed system, we have deployed our system in a daycare center: Ikoi-no-ie 26. Evaluation result in Ikoi-no-ie 26 showed that our system estimated the subject's present area with F-measure: 80.6% and activity with F-measure: 73.8% and generated the daycare report.

Family Tracing and Reunification (FTR) is the process whereby families separated by disasters are reunited. Current FTR systems use either inefficient paper-based forms and notice boards or digital registries that need the Internet, which may be unavailable during disasters. In this demonstration we present Milk Carton: a system that aids in FTR. Milk Carton creates a registry containing evacuee records. To find separated persons, Milk Carton uses Eigenfaces face recognition to match queries with existing records. Milk Carton uses a clustered architecture of Computing Nodes to handle data storage and execute the Eigenfaces algorithm. To operate under challenged-network environments, Milk Carton uses response patrol vehicles as data ferries to deliver data. In this demonstration, we show how Milk Carton uses Eigenfaces to locate separated persons and how data ferries and Computing Nodes function.

These days, smart home applications such as a concierge service for residents, home appliance control and so on are attracting attention. In order to realize these applications, we strongly believe that we need an indoor positioning system which fulfills the following requirements: Req 1: high accuracy; Req 2: low installation cost; Req 3: small burden on the user; Req 4: low privacy invasion. There are several studies which work on the indoor positioning system. However, these previous works do not accomplish the requirements. In this paper, we present a piezo sensor-based indoor positioning system which estimates the position of the user by utilizing a piezo component attached on the floor. To realize the proposed positioning system, we have tackled two challenges. First challenge is the development of an indoor positioning technique. We cannot utilize TDoA technique that is used to estimate the distance from the target, since the calculation of vibration velocity is difficult. To cope with this challenge, we have developed a new technique which estimates the position of the user from floor vibrations caused by their actions. Second challenge is the selection of the feature vector to estimate the vibration type accurately. We have selected MFCC, FFT, and Envelope shape features from preliminary experiments. We have implemented the proposed system in our smart home testbed. We have evaluated the performance of the vibration type estimation technique. As a result, we have confirmed that our technique estimates the type with F-measure: 93.9%.

In this paper, we propose an indoor localization method using a distance-illuminance model of lighting devices and trilateration. We propose a method that estimates distance from three controllable lighting devices based on the illuminance at the target point, by alternately turning on each of the devices. Then, the proposed method estimates the position of the target point based on trilateration. The proposed method are two merits. First, it can be realized at low cost because only three lighting devices and an illuminance sensor are required. Second, it is robust against influences by external lighting devices (or sunlight) since the proposed method measures the difference of the illuminance before and after turning on a lighting device. We conducted experiments in a room in an ordinary home environment and confirmed that the proposed method could estimate the position of the illuminance sensor within 0.5m error on average.

Recently, as elderly people population grows, the burdens on caretakers are getting larger. In daycare center, caretakers make a daycare report aiming to improve the senior citizen's Quality of Life. However, in the aged society, it is difficult for caretakers to record the senior citizen's activity in detail, since each caretaker needs to take care of several senior citizens at the same time. In this paper, we propose a semi-automatic day-care report generation system. Our proposed system estimates the present area and activity of the senior citizen by utilizing the accelerometer-implemented-BLE-beacon tags and generate the daycare report. Based on the generated report, the caretaker works effectively. Evaluation result in a daycare center showed that our system estimated the subject's present area with F-measure: 80.6% and activity with F-measure: 73.8% and automatically generated the daycare report.

Recently, lack of nurses and care nurses is a serious problem because of the increasing demands of nursing care for age people. So, indoor robot navigation system is important as one of the possible robot technologies to compensate for the lack of nurses. In particular, an indoor robot navigation system using the RFID system which is easy configuration and cheap relatively is studied all over the world. But these studies have assumed an ideal environment. For example, there are not any obstacles. To avoid obstacles, location estimation method and the moving control method that considered the surrounding environment are desired. In this paper, we propose a new indoor robot navigation system that is composed of passive RFID system to avoid obstacles. In this system, we install a passive RFID tag with an obstacle and store the information of the obstacles in the tag. A mobile robot avoids the obstacle by using the information. We performed experiments of a mobile robot to show the effectiveness of the proposed system. As a result, it is shown that it is possible to avoid the obstacle by the proposed system.

Recently, indoor mobile robot navigation systems have been developed all over the world as the technology for assisting aged and physically handicapped people. Especially, the systems using passive RFID with features of low cost and simple composition have attracted attention over the years. However, in these systems, there are two serious problems. 1) A mobile robot performs the meandering moving. 2) A mobile robot is difficult to avoid obstacles. Hence, a moving correction method is required very much to solve the above two problems. In this paper, we propose a new moving correction method of a mobile robot using only passive RFID to improve the moving trajectory. This method reduces the serpentine moving of mobile robot and avoids obstacles by predicting obstacles and walls in front of the mobile robot. To show the effectiveness of the proposed system, we carry out the performance evolutions by computer simulations and experiments. As the results, we show that the proposed method can reduce the serpentine moving of the mobile robot compared with the conventional method and also can avoid obstacles.

The RFID (Radio Frequency Identification) is considered as one of the most preferable ways for the position estimation in indoor environments, since GPS does not work in such situations. In RFID system, an RFID reader enables to estimate the position of RFID tags easily and inexpensively. In applications with the position estimation of RFID tags, indoor robot navigations are very important for human society. The problem is how to obtain the position estimations of RFID tags as accurately as possible. Previously S-CRR (Swift Communication Range Recognition) has been proposed for the appropriate estimation method of this kind of applications. This method is capable of the accurate position estimation of an RFID tag in very short time. The disadvantage of S-CRR is that the mobile robot must stop to search RFID tags accurately at each position. In indoor robot navigations, mobile entities like robots have to move continuously because they need to navigate smoothly and safely. In this paper, we propose a new position estimation method of RFID tags with continuous moving only using RFID technology. We call this Continuous Moving CRR (CM-CRR). CM-CRR uses two communication ranges, long and short ranges and switches them appropriately. The system estimates the position of RFID tags using their approaches and continuous moving. To show the effectiveness of CM-CRR, we evaluate the estimation error of an RFID tag by computer simulations. From the results, CM-CRR can accurately estimate the position of RFID tags with continuously moving of the mobile robot and be applied to indoor robot navigations.

Recently, the radio frequency identification (RFID) system is paid attention as an identification source that can realize a ubiquitous environment. One of the important technologies that apply the RFID system is the indoor position estimation of RFID tags. For example, it can be applied to an indoor navigation system for handicapped person and recognition of ambient environment by a mobile robot, etc. In this paper, we propose a new positional estimation method to estimate the RFID tag position by using the tune of the antenna directivity of the RFID reader (D-CRR). This method controls the directivity of the antenna by dynamically changing the distance between the antenna elements of the RFID reader. We show the effectiveness of the proposed method by computer simulations.

The RFID (Radio Frequency IDentification) system is paid attention to as a new identification source that achieves a ubiquitous environment. Each RFID tag has the unique ID, and is attached to some object. A user reads the unique ID of a RFID tag with RFID readers and obtains the information on the object. One of the most important technologies that use the RFID system is the position estimation of RFID tags. The position estimation means to estimates the location of the object with the RFID tag. It can be very useful to acquire the location information of RFID tag. If a user can understand the position of the RFID tag, the position estimation can be applied to a navigation system for walkers. In this paper, we propose a new method named as Swift Communication Range Recognition (S-CRR) method as an extended improvement of the previous CRR method on the estimation delay. In this method, the position of RFID tag is estimated by selecting the communication area model which corresponds to those boundary angles. We carry out the performance evaluation by the experiments of RFID system and show the effectiveness of S-CRR for position estimation.

RFID (Radio Frequency IDentification) systems have become meaningful as a new identification source that is applicable in ubiquitous environments. Each RFID tag has a unique ID, and is attached to some object. A user reads the unique ID of a RFID tag with RFID readers and obtains the information on the object. One of the important technologies that use the RFID systems is the indoor position estimation of RFID readers. If a user can understand a position of an RFID reader, the position estimation can be applied to a navigation system for walkers. Using conventional methods, the system needs more than two RFID tags for the accurate indoor position estimation, and the accuracy itself of position estimation is not so high. In this paper, we propose a new method for accurate indoor position estimation of passive RFID systems. In this method, the position of an RFID reader is estimated using SCRR (Swift Communication Range Recognition) method. The proposed method is capable of accurate position estimation in near real time regardless for large numbers of RFID tags.