**5. Human localization with RSSIs between reference nodes**

The human interference with the RSSI values degrades the localization performance. How‐ ever, because human distortion of the RSSI values is stable, this distortion may be used to indicate the human's location. The human location is estimated by the same approach as that for object localization, using the pattern recognition technique. This is our idea for subroom-level human localization. While the object location is estimated at the application re‐ quest time, the human position is always required. Because the continuous use of a target node reduces the battery life, only the reference nodes are used for human localization.

#### **5.1. Experiment on human localization in four areas**

To confirm that the distorted RSSI can be used for human localization, we conducted a sim‐ ple experiment. In this experiment, we make one person stand or sit to cut off the RF signals between two reference nodes. Because this situation drastically disturbs the RSSIs, estima‐ tion of the human's location should be easy.

The conditions for the evaluation experiment are described in Fig. 13. The datasets were gathered from 4 reference nodes. When one node is selected to be the base node, as shown in Fig. 13, the node collects RSSI from the three surrounding nodes. In total, 12 (=4×3) RSSIs were used for human localization. In the experiments, the subject sat or stood at the four lo‐ cations illustrated. Data for the human absence case were also collected. This problem is re‐ garded as 5-class classification. Direct human interference means that the RSSIs between two particular reference nodes are frequently missed, which means that the RF signals could not be received successfully. This data deficit may lead to human location estimation failure. We therefore compensated the part with the data deficit using the average of the successful‐ ly collected RSSIs. We evaluated the ratio of the true positive value in all data. Each pattern recognition method adopted the most suitable parameters for the estimation. Ten-fold crossvalidation was also used for the evaluation.

The estimation results are shown in Table 1. These results indicate the possibility of estimat‐ ing the four assumed human locations using the RSSIs among the reference nodes. Estima‐ tion with the 3-layered NN algorithm appears to be a little difficult, but estimations based on KNN and DKNN showed high accuracies.

**Figure 13.** Conditions for experiments on human localization in four areas.

**Figure 12.** Performance results for each location.

238 Radio Frequency Identification from System to Applications

**5.1. Experiment on human localization in four areas**

tion of the human's location should be easy.

**5. Human localization with RSSIs between reference nodes**

The human interference with the RSSI values degrades the localization performance. How‐ ever, because human distortion of the RSSI values is stable, this distortion may be used to indicate the human's location. The human location is estimated by the same approach as that for object localization, using the pattern recognition technique. This is our idea for subroom-level human localization. While the object location is estimated at the application re‐ quest time, the human position is always required. Because the continuous use of a target node reduces the battery life, only the reference nodes are used for human localization.

To confirm that the distorted RSSI can be used for human localization, we conducted a sim‐ ple experiment. In this experiment, we make one person stand or sit to cut off the RF signals between two reference nodes. Because this situation drastically disturbs the RSSIs, estima‐

The conditions for the evaluation experiment are described in Fig. 13. The datasets were gathered from 4 reference nodes. When one node is selected to be the base node, as shown in Fig. 13, the node collects RSSI from the three surrounding nodes. In total, 12 (=4×3) RSSIs were used for human localization. In the experiments, the subject sat or stood at the four lo‐

The estimation results suggest two points in particular. The first is that the 3-layered NN algorithm is poor at distinguishing the human presence case from the human absence case. It is also weak at human location estimation when compared with the other two pattern recognition methods. The other point is that KNN and DKNN can not only tell the difference between the human presence case and human absence case, but can also estimate human locations with high accuracy even under the condition where the human presence is unknown.


The results show that RSSIs among the reference nodes can be used as good indicators to localize a human in the environment. It is interesting that although a human standing at the right lower corner of the room does not disturb the radio wave directly, the method esti‐ mates the location accurately, which may indicate that the pattern recognition method is

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

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

241

sensitive to slight differences caused by human interference.

**Figure 14.** Conditions and results for the human localization experiment

ronments with RSSIs only.

tain some trade off between spatial resolution and localization accuracy.

There are some positions that are difficult to estimate with our approach. The worst two es‐ timations are illustrated in Fig. 15. These scattered estimation candidates are the minority of the estimation as a whole and the majority of the mistaken estimation candidates are quite close to the correct location. This result means that loose conditions such as large grid size may improve the localization performance. Human localization using only RSSIs may con‐

These results demonstrated that the system could localize human positions in indoor envi‐

\*Upper selection: Estimation including human absence data.

Lower selection: Estimation excluding human absence data.

**Table 1.** Results for human localization in four areas with RSSIs among the reference nodes.

#### **5.2. Experiments on sub-room-level human localization**

The previous experimental results showed that the direct human interference in communi‐ cation between two reference nodes contained rich information for human localization. We now address a more complicated case.

The conditions for the evaluation experiment are shown in Fig. 14a). The reference node in‐ stalled at the table is regarded as the center node, which then receives 14 RSSIs from the re‐ maining surrounding nodes. For human location estimation, we took measurements with each node acting as the center node in turn to cover the whole environment. However, in this case, the same approach will increase the dimensions of the input RSSI data dramatical‐ ly, which definitely results in the estimation time being too long. Therefore, we only use the reference node on the table as the center node because it is located at the center of the envi‐ ronment and, as Fig. 14a) illustrates, the RSSIs between this center node and other surround‐ ing nodes can cover the majority of the environment.

We divided the environment into 49 grids (0.5 m×0.5 m) as shown in the left part of Fig. 4. We asked a subject to stand or sit on each grid to collect data sets for human localization. Also, human absence was appended to the data sets as one of the conditions. Thus, the prob‐ lem is regarded as 50 (49+1) class discrimination from 14-dimensional vector data. For the experiment, 50 data sets were collected per location.

In this experiment, we assume an "Unknown" class in the output classes, which is the class to be used when the estimated result is less probable. This means that when the similarity between an input dataset and the most likely dataset in the learning database is smaller than a certain threshold, the system regards the estimated result as wrong and classifies it into the unknown class.

The estimation results are shown in Fig. 14b). The estimation accuracy as a whole is 86.2%, and the discrimination between the human presence case and the human absence case can be discriminated completely, with an accuracy of 100%. The percentage that was estimated as being in the unknown class was 1.3%, which means that almost all of the data is correctly classified.

The results show that RSSIs among the reference nodes can be used as good indicators to localize a human in the environment. It is interesting that although a human standing at the right lower corner of the room does not disturb the radio wave directly, the method esti‐ mates the location accurately, which may indicate that the pattern recognition method is sensitive to slight differences caused by human interference.

**Absence Sofa Bed Table Desk Total**

91.7% (100.0%)

93.3% (100.0%)

51.7% (68.3%)

93.3% (96.7%)

96.7% (95.0%)

86.7% (95.0%)

94.3% (95.8%)

95.7% (96.7%)

72.0% (90.0%)

93.3% (88.8%)

93.3% (93.3%)

95.0% (98.3%)

The previous experimental results showed that the direct human interference in communi‐ cation between two reference nodes contained rich information for human localization. We

The conditions for the evaluation experiment are shown in Fig. 14a). The reference node in‐ stalled at the table is regarded as the center node, which then receives 14 RSSIs from the re‐ maining surrounding nodes. For human location estimation, we took measurements with each node acting as the center node in turn to cover the whole environment. However, in this case, the same approach will increase the dimensions of the input RSSI data dramatical‐ ly, which definitely results in the estimation time being too long. Therefore, we only use the reference node on the table as the center node because it is located at the center of the envi‐ ronment and, as Fig. 14a) illustrates, the RSSIs between this center node and other surround‐

We divided the environment into 49 grids (0.5 m×0.5 m) as shown in the left part of Fig. 4. We asked a subject to stand or sit on each grid to collect data sets for human localization. Also, human absence was appended to the data sets as one of the conditions. Thus, the prob‐ lem is regarded as 50 (49+1) class discrimination from 14-dimensional vector data. For the

In this experiment, we assume an "Unknown" class in the output classes, which is the class to be used when the estimated result is less probable. This means that when the similarity between an input dataset and the most likely dataset in the learning database is smaller than a certain threshold, the system regards the estimated result as wrong and classifies it into

The estimation results are shown in Fig. 14b). The estimation accuracy as a whole is 86.2%, and the discrimination between the human presence case and the human absence case can be discriminated completely, with an accuracy of 100%. The percentage that was estimated as being in the unknown class was 1.3%, which means that almost all of the data is correctly

KNN 96.7%

DKNN 98.3%

NN 36.7%

96.7% (98.3%)

96.7% (98.3%)

90.0% (98.3%)

**5.2. Experiments on sub-room-level human localization**

ing nodes can cover the majority of the environment.

experiment, 50 data sets were collected per location.

the unknown class.

classified.

**Table 1.** Results for human localization in four areas with RSSIs among the reference nodes.

\*Upper selection: Estimation including human absence data. Lower selection: Estimation excluding human absence data.

240 Radio Frequency Identification from System to Applications

now address a more complicated case.

**Figure 14.** Conditions and results for the human localization experiment

There are some positions that are difficult to estimate with our approach. The worst two es‐ timations are illustrated in Fig. 15. These scattered estimation candidates are the minority of the estimation as a whole and the majority of the mistaken estimation candidates are quite close to the correct location. This result means that loose conditions such as large grid size may improve the localization performance. Human localization using only RSSIs may con‐ tain some trade off between spatial resolution and localization accuracy.

These results demonstrated that the system could localize human positions in indoor envi‐ ronments with RSSIs only.

**Author details**

versity of Tokyo, Japan

versity of Tokyo, Japan

**References**

Hiroshi Noguchi1\*, Hiroaki Fukada2

nology, The University of Tokyo, Japan

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\*Address all correspondence to: hnogu-tky@umin.ac.jp

, Taketoshi Mori3

1 Department of Life Support Technology (Molten), Graduate School of Medicine, The Uni‐

2 Department of Mechano-Informatics, Graduate School of Information Science and Tech‐

3 Department of Life Support Technology (Molten), Graduate School of Medicine, The Uni‐

4 Department of Gerontological Nursing/Wound Care Management, Graduate School of Medicine, The University of Tokyo; Tomomasa Sato, Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Japan

5 Department of Mechano-Informatics, Graduate School of Information Science and technol‐

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

and Tomomasa Sato5

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**Figure 15.** Estimation results at the worst estimation score areas.

#### **6. Conclusion**

We proposed methods for object and human localization using a ZigBee-based RFID sys‐ tem. Our method estimates the node locations using a pattern recognition technique from RSSI data among the nodes, environmental sensor data and estimated human behavior to reduce performance deterioration caused by human interference with radio waves. The ex‐ periments demonstrated that our method increases object localization accuracy by about 20% under human presence conditions. Considering the fact that human interference with the RSSI is stable, we also performed human localization using pattern recognition based on the RSSI values. Our experimental results showed that our approach is feasible for subroom-level human localization.
