Manato Fujimoto

A new indoor position estimation method of RFID tags for continuous moving navigation systems

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.

Accurate indoor position estimation by Swift-Communication Range Recognition (S-CRR) method in passive RFID systems

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.

A Novel Method for Position Estimation of Passive RFID Tags; Swift Communication Range Recognition (S-CRR) Method

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.

Disaster area mapping using spatially-distributed computing nodes across a DTN

Disaster area mapping is critical to guiding evacuees to safety and aiding responders in decision-making. During disasters however, Cloud-based mapping services cannot be relied upon, because network infrastructures may have been damaged. In this study, we propose a disaster area mapping system that functions under challenged-network environments in a disaster area. The system infers a pedestrian map with walking speed information from data gathered by civilians and responders with mobile devices. To generate the map, the system addresses the following challenges: how to collect disaster area data, how to share data without continuous end-to-end networks, and how to generate maps without Cloud-based mapping services. First, the system leverages human mobility to collect disaster area data. Civilians and responders with mobile devices function as sensor nodes and log their GPS and velocity traces while moving based on the Post-Disaster Mobility Model. Second, the system uses mobile devices to establish a Delay-Tolerant Network, through which nodes opportunistically share data. Finally to generate the map, the collected data are routed to Computing Nodes: devices with more computational resources than mobile devices that are spatially-distributed across the disaster area. The Computing Nodes infer the map from the data and share it with evacuees. Through experimental evaluations and computer simulations, we found that the system significantly decreases the time required to generate and deliver a map to an evacuee, compared to a case without the system. Furthermore, the overall reduction in time increases as the size of the data required to generate the map and the number of DTN nodes increase.

Beacon-based multi-person activity monitoring system for day care center

Recently, as elderly people population grows, the burden on caretakers are getting larger. In day care center, caretakers are taking care records aiming to improve care receivers Quality of Life (QoL). However, in the present situation, it is difficult for caretakers to record care receivers activity in detail because each care worker needs to take care of several care receivers at the same time and it is a large burden. 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 care receivers to use dedicated device such as smart phone and/or particular applications that are obtrusive and cumbersome for care receivers. In this paper, we propose a novel elderly monitoring system which can monitor movements/activity of multiple care receivers at the same time by estimating existence area of each of the care receivers, without burdening them. Our proposed system estimates multiple care receivers existence area only using RSSI (Received Signal Strength Indication) of BLE (Bluetooth Low Energy). The feature of our proposed system is that it takes Movable-Beacon and Fixed Scanner style. We have validated the proposed system and confirmed that we can estimate multi-persons existence area at high accuracy using only BLE devices.

Low-cost and Device-free Activity Recognition System with Energy Harvesting PIR and Door Sensors

Progress of IoT and ubiquitous computing technologies has strong anticipation to realize smart services in households such as efficient energy-saving appliance control and elderly monitoring. In order to put those applications into practice, high-accuracy and low-cost in-home living activity recognition is essential. Many researches have tackled living activity recognition so far, but the following problems remain: (i)privacy exposure due to utilization of cameras and microphones; (ii) high deployment and maintenance costs due to many sensors used; (iii) burden to force the user to carry the device and (iv) wire installation to supply power and communication between sensor node and server; (v) few recognizable activities; (vi) low recognition accuracy. In this paper, we propose an in-home living activity recognition method to solve all the problems. To solve the problems (i)--(iv), our method utilizes only energy harvesting PIR and door sensors with a home server for data collection and processing. The energy harvesting sensor has a solar cell to drive the sensor and wireless communication modules. To solve the problems (v) and (vi), we have tackled the following challenges: (a) determining appropriate features for training samples; and (b) determining the best machine learning algorithm to achieve high recognition accuracy; (c) complementing the dead zone of PIR sensor semipermanently. We have conducted experiments with the sensor by five subjects living in a home for 2-3 days each. As a result, the proposed method has achieved F-measure: 62.8% on average.

Milk Carton: A Face Recognition-Based FTR System Using Opportunistic Clustered Computing

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.

Daily living activity recognition with ECHONET Lite appliances and motion sensors

Recently, IoT (Internet of Things) technologies have been attracting increasing attention. Among many applications of IoT, homes can be the most promising target. One of the purposes to deploy IoT in homes is automatic recognition of activities of daily living (ADLs). It is expected that ADL recognition in homes enables many new services such as elderly people monitoring and low energy appliance control. In existing studies on ADL recognition, however, it is hard to build a system to acquire data for ADL recognition in terms of installation cost. In this paper, we propose a method that reduces costs of the ADL recognition system by using ECHONET Lite-ready appliances which are expected to be widely spread in the future. ECHONET Lite is a communication protocol for control and sensor networks in smart-homes and standardized as ISO/IEC-4-3. The proposed method utilizes information (e.g., on/off state) from appliances and motion sensors attached to them as features and recognizes ADLs through machine learning. To evaluate the proposed method, we collected data in our smart-home testbed while several participants are living there. As a result, the proposed method achieved about 68% classification accuracy for 9 different activities.

Toward real-time in-home activity recognition using indoor positioning sensor and power meters

Automatic recognition of activities of daily living (ADL) can be applied to realize services to support user life such as elderly monitoring, energy-saving home appliance control, and health support. In particular, “real-time” ADL recognition is essential to realize such a service that the system needs to know the users current activity. There are many studies on ADL recognition. However, none of these studies address all of the following problems: (1) privacy intrusion due to the utilization of high privacy-invasive devices such as cameras and microphones; (2) limited number of recognizable activities; (3) low recognition accuracy; (4) high deployment and maintenance costs due to many sensors used; and (5) long recognition time. In our prior work, we proposed a system which solves the problems (1)– (4) to some extent by using users position data and power consumption data of home electric appliances. In this paper, aiming to solve all the above problems including (5), we propose a new system by extending our prior work. To realize “real-time” ADL recognition while keeping good recognition accuracy, we developed new power meters with higher sensing frequency and introduced new techniques such as adding new features, selecting the best subset of the features, and selecting the best training dataset used for machine learning. We collected the sensor data in our smart home facility for 11 days, and applied the proposed method to these sensor data. As a result, the proposed method achieved accuracy of 79.393% in recognizing 10 types of ADLs.

Elderly person monitoring in day care center using Bluetooth Low Energy

Recently, as elderly people population grows, the burden on caretakers are getting larger. In day care center, caretakers are taking care records aiming to improve care receivers Quality of Life. However, in the present situation, its difficult for caretakers to record care receivers activity in detail because each care worker needs to take care of several care receivers at the same time and it is a large burden. 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 care receivers to use dedicated device such as smart phone and/or particular applications that are obtrusive and cumbersome for care receivers. In this paper, we propose a novel elderly monitoring system which can monitor movements/activity of multiple care receivers at the same time by estimating existence area of each of the care receivers, without burdening them. Our proposed system estimates multiple care receivers existence area only using RSSI of BLE (Bluetooth Low Energy). The feature of our proposed system is that it takes Movable-Beacon and Fixed Scanner style. We have validated the proposed system and confirmed that we can estimate multi-persons existence area at high accuracy using only BLE devices.