Pi-Navi: Finding Location of a Pedestrian Using Pace Tracking
Seon-Woo Lee and Kenji Mase
ATR Media Integration& Communications Research Laboratories
senu, mase  @mic.atr.co.jp

1 Introduction
Recognizing location may be used to provide mobility
helps for blind, context-aware guidance systems for exhibition touring, and spatially-based applications of a wearable computer. Many researcher, therefore, have attempted
to develop a variety of systems, sensors, and techniques tofind the location of an user [1, 2].
In this paper, we describes a method to find the location of a pedestrian wearing a sensing module, which isone of the basic function blocks in an indoor navigation
system called “Pi-Navi”. The proposed method uses deadreckoning which integrates incremental user motions, i.e.,
one step in our case. A bi-axial accelerometer and a digitalcompass are used to measure the acceleration and heading of a pedestrian, respectively. When the system detects
a new step, it updates the current position of an user via
dead reckoning. We also propose a classification methodof walking behaviors as: walking on level ground (“level”),
going up (“up”) a stairway, or going down (“down”) a stairway. Such recognition is important because it can be used
as absolute position information, enabling the system tocorrect position errors. Experimental results demonstrate
the performance of the navigation system.

Figure 1: Sensing module3

Proposed method

Counting steps using a detection method allows the direct
determination of the distance. Therefore, we need to findan accurate and robust detection method. We selected positive and negative peaks of the forward/upward acceleration
as a basic feature vector, which is detected in the period
from the moment of last step detection to current step detection. When all four peaks are found, the system tests thefollowing conditions to determine each new step.
1. Whether the detected four peak values are above theminimum threshold values.

2 Hardware

2. Whether the time since the last walking detection isgreater than some minimum period.

The system consists of a notebook PC, a card type data acquisition module and a sensing module. As shown in Figure 1, the sensing module consists of a bi-axial accelerometer, a digital compass module and an infrared light detector.
The sensing module is assumed to be fixed to the middleof the waist of the user to measure forward and upward accelerations and heading accurately. The data is read every
20 [msec]. The digital compass module can give us compass heading information of the module via RS-232 serial
communication channel.

!#“Pi-Navi:"%$&'( ) *     ”,  ,  , ATR

3. To prevent false detection from other body movements, we introduce a lag parameter on the autocorrelation function of ,.+ -0/21 as:3546487 9;:=<?>@B7 JKADJ C JL7 M6NIO

M6N,8-QPR1 ,S-QP?T 3 10U (1)< FIH + +3 46487 9:=< must be greater thanThe magnitude of the lag
EGFIHa threshold value.
If the above conditions are true, then the system increases
the step count, and performs the dynamic step size estimation.

To estimate the current step size, we use a first-orderpolynomial model derived from the relationship between
the step size and walking speed. We measured these features from nine persons in walking the same distance atthree speeds: slow, normal, and fast. From our measurements, we found that if a user walks faster, both the step
size [m] and step rate [steps/sec] increase.
The system tries to classify a step into one of three behaviors: walking on level ground (“level”), going up (“up”)
a stairway, or going down (“down”) a stairway. The classification method is based on a nearest neighborhood methodon the feature vector space. To increase the recognition
ratio, we introduced also a cross-correlation function offorward and upward acceleration as another feature. Because the cross-correlation of “down” behavior is very distinguishable from the others.
At the time a “level” step is detected, the system determines the step size using step rate, then adds the northand east components of this displacement to the total north,V < -Q/21 and east, V#W -Q/21 accumulator as:

V < -Q/YX[Z.1]\ V < -Q/21^X`_.-Q/21ba5c.dL-6eGf8-Q/21g1
V#W -Q/YX[Z.1]\ VW -0/21^X`_S-0/21bd A=C -6eGfh-Q/21g1

Figure 2: Tracking result of walking around a big squarepath(2)(3)

where the heading eGfh-Q/21 is a quantized heading from thedigital compass.
For error correction, we used a simple infrared transmitter which fixed on certain places. When a signal isdetected, the system make the current position to be prestored location. When a “up/down” step is detected at firstin normal operation, same as the detection of an infrared
signal, the current position is adjusted to the location of thestarting place on near a stairway.

4 Experimental results
Figure 2 shows a tracking result when a user walks arounda big loop path. The total length of the square path isaround 128 [m]. We installed simple infrared light emittersat three places as shown in the figure. By using the infraredtransmitter, the system correct the position error as sown in
the region denoted by circle. The average of the errors between the final estimated position and the starting position
from three trials are 0.83 [m] and 1.83 [m] for east andnorth components, i.e., x and y in the map, respectively.
We carried out an experiment taking a path include descending stairs as shown in Figure 3. The estimated tracking path is much different from the real path initially, because of a high magnetic interference in the region. Whenthe system detect a “down” step at first, the correction ofthis error was accomplished. This result demonstrates thatthe detection of stairway using the proposed classification
method can be used as an absolute position measurement
like an infrared transmitter.

Figure 3: Compensation using detecting a “down” behavior

5Conclusion

In this paper, we proposed a tracking method of the location of a pedestrian using dead reckoning based on pacetracking and simple infrared beacon method. Experimental results showed a reliable performance of the proposed
method. In future works, we are studying how to improve
the performances of step detection and step size estimation
methods.

References[1] T. Starner, D. Kirsch, and S. Assefa, “The locustswarm: An environmentally-powered, networkless location and messaging system,” in The First Int’l
Symposium on Wearable Computers, (Cambridge),
pp. 169–170, October 1997.
[2] C. T. Judd, “A personal dead reckoning module,” in Institute of Navigation’s ION 97 Conference, (Cambridge), pp. 169–170, October 1997.
(http://www.pointresearch.com/ionpapper.htm).