**1. Introduction**

Radio frequency identification (RFID) systems are currently widespread in business ap‐ plications such as inventory management and supply chain management. In particular, the active type of system is often used for total management over a large area because of its long communication range. With the increasing miniaturization and price reduction of RFID tags, the applicable area is expanding from business to consumer. One of the most promising areas is the home environment. In the home environment, object management is as important as that in business areas such as warehouses, because there are many pieces of equipment in daily use. While many objects are accessed often in a warehouse environment, only a few objects are replaced in the home environment. The management of massive objects is thus unnecessary for the home. Another application is in the under‐ standing of the environmental situation. In warehouses, tags with temperature and hu‐ midity sensors are often used for quality management. These tags realize not only object identification but also understanding of the object's situation. The next step in situation understanding is behavior recognition for home occupants. Behavior recognition is useful for intelligent home automation, healthcare based on life patterns, and monitoring of people living in remote locations.

To capture human behavior using an active RFID system, the system must measure vari‐ ous information related to human behavior. A typical example of human behavior meas‐ urement using a wireless sensing system, which is regarded as a kind of active RFID system, is MITes [1]. MITes can capture home environmental information (e.g. lighting changes and passing people) using wireless sensor devices attached to each of the rooms. MITes can also measure details of human behavior using wearable sensors. Tradi‐

© 2013 Noguchi et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Noguchi et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

tional active RFID systems can capture large segments of human behavior, even without the use of a complicated system like MITes. Environmental information can be measured using sensors in the tags. With the addition of the environmental information, the RFID system can easily identify the object with the attached tags. However, while identifica‐ tion alone is suitable for object management, information about object handling and ob‐ ject locations is required to measure human behavior. For object handling, the work of Philipose et al. [2] indicates that the object handling sequence assists with the estimation of human behavior. This information can be captured easily with sensors included in the tags. For the location, as an example of the use of location information for human behav‐ ior recognition, the information that a cup exists on a sink indicates that someone is washing the cup. The presence of the cup on a table suggests that someone is drinking from it. Also, if it is known that one specific person uses the cup, this information also identifies the person who is drinking. Although direct information about humans is de‐ sirable for behavior recognition, direct measurement is difficult with active RFID sys‐ tems. If the inhabitants wear tags, some information can be captured. However, wearing the tags constricts the natural behavior. Intille et al. [3] suggested that a rough human lo‐ cation is useful for human behavior recognition. Based on their work, we decided that our measurement target for humans using active RFID systems is sub-room-level human localization without the humans wearing tags. Therefore, our research goal is object and human localization using an active RFID system.

less communications. ZigBee has advantages for accurate localization. RSSIs in ZigBee are sensitive to distance because of its high frequency radio wave. Another advantage is that ZigBee provides protocols for sensor devices, which leads to easy transmission of the sensor data from the tags. However, because the ZigBee-based RSSI is more sensitive than a low-frequency RFID system, the presence of humans disturbs the RSSI more se‐ verely. The use of sensor data on tags would improve the object localization perform‐ ance. Rowe et al. [8] have already reported that limitation of the location candidates improves the localization performance. We have expanded the previous algorithm to pre‐ vent performance degradation. The algorithm uses the RSSI data, the environmental sen‐ sor data, and data from the sensors on the tag to prevent degradation of the

Object and Human Localization with ZigBee-Based Sensor Devices in a Living Environment

http://dx.doi.org/10.5772/53366

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On the other hand, the sensitivity of ZigBee-based RSSIs to the presence of humans is effec‐ tive for human localization. Wilson and Patwari developed a human tracking method based on RSSI values from reference nodes at the outside of the walls [9]. Their approach requires many wireless devices to generate tomography data for tracking, and no obstacles exist in the room. For our application, we do not need high-resolution human positioning but re‐ quire only rough location using a few devices. If human interference with the radio waves is stable, the pattern of the RSSI values among the nodes specifies the human location. Our challenge is therefore to estimate a sub-room-level human location based on this RSSI dis‐

In this paper, we constructed a prototype active RFID system using ZigBee devices. We also proposed an object localization method using RSSIs among tags and data from sen‐ sors attached to the tags. Our experimental results demonstrated the feasibility of our lo‐ calization approach for both objects and persons in a realistic home environment. The results also show that our approach reduces the performance degradation caused by the

To avoid limitations in the sensor variety and the communication protocols, we developed a new ZigBee-based prototype system. The system consists of the target nodes, which are tags in the RFID system, and the reference nodes, which are readers in the RFID system. The dif‐ ference between this system and the traditional RFID system is that our system enables com‐ munication among the readers and can gather RSSI data because the reference nodes are also regarded as a kind of target node. The devices consist of the XBee, which is a commer‐ cial ZigBee communication module, and the Arduino or Arduino Fio microcontroller, which is commonly used in prototype device construction because of its compactness and ease of programming. The antenna used for wireless transmission and reception is non-directional to reduce the system performance dependence on device direction. The developed sensors

tortion using a fingerprinting approach, which is the same as object localization.

performance by human interference.

presence of humans.

**2. ZigBee-based sensor device**

and deployment examples are shown in Fig. 1.

Popular approaches for tag position estimation use radio signal strength indicators (RSSIs) for communication between tags and readers, because RSSI depends on the dis‐ tance between the tag and the reader [4-6]. The simplest approach uses a triangulation algorithm. However, in the home environment, which contains many obstacles for RFID systems such as furniture and electrical appliances, localization is more difficult because the strength of the radio wave can change easily with the room situation. One solution is the deployment of multiple reference tags, which indicate true position [5] [6]. However, this approach is impractical in a living environment because of the cost and difficulty. Distortion of the radio waves by the occupant's presence decreases the localization per‐ formance. When we consider the above applications, accurate position (i.e. x-y-z posi‐ tion) estimation is not necessary, but rough location (e.g., on a table, in a drawer, or in a cabinet) is required. Based on this idea, we have already proposed a method for localiza‐ tion of tag-attached objects [7]. The method uses a machine learning technique and a rule-based algorithm to combine RSSI data and sensor data captured by externally dis‐ tributed sensors across the room. This combination improves the performance in the presence of humans. However, this method has some disadvantages, including the cost of a commercial RFID system, the necessity for the tag readers to have a local area net‐ work (LAN) connection, the additional introduction of distributed sensors and the limita‐ tions of the estimation locations (e.g. the system cannot distinguish any drawers that do not contain switch sensors).

To overcome these problems, we must use a new active RFID system instead of the cur‐ rent commercial active RFID systems. We have focused on ZigBee technology for wire‐ less communications. ZigBee has advantages for accurate localization. RSSIs in ZigBee are sensitive to distance because of its high frequency radio wave. Another advantage is that ZigBee provides protocols for sensor devices, which leads to easy transmission of the sensor data from the tags. However, because the ZigBee-based RSSI is more sensitive than a low-frequency RFID system, the presence of humans disturbs the RSSI more se‐ verely. The use of sensor data on tags would improve the object localization perform‐ ance. Rowe et al. [8] have already reported that limitation of the location candidates improves the localization performance. We have expanded the previous algorithm to pre‐ vent performance degradation. The algorithm uses the RSSI data, the environmental sen‐ sor data, and data from the sensors on the tag to prevent degradation of the performance by human interference.

On the other hand, the sensitivity of ZigBee-based RSSIs to the presence of humans is effec‐ tive for human localization. Wilson and Patwari developed a human tracking method based on RSSI values from reference nodes at the outside of the walls [9]. Their approach requires many wireless devices to generate tomography data for tracking, and no obstacles exist in the room. For our application, we do not need high-resolution human positioning but re‐ quire only rough location using a few devices. If human interference with the radio waves is stable, the pattern of the RSSI values among the nodes specifies the human location. Our challenge is therefore to estimate a sub-room-level human location based on this RSSI dis‐ tortion using a fingerprinting approach, which is the same as object localization.

In this paper, we constructed a prototype active RFID system using ZigBee devices. We also proposed an object localization method using RSSIs among tags and data from sen‐ sors attached to the tags. Our experimental results demonstrated the feasibility of our lo‐ calization approach for both objects and persons in a realistic home environment. The results also show that our approach reduces the performance degradation caused by the presence of humans.
