A Method of Pedestrian Dead Reckoning Using Speed Recognition

Tuomo Kroger, Yuwei Chen, Ling Pei, Tomi Tenhunen, Heidi Kuusniemi, Ruizhi Chen

Department of Navigation and Positioning Finnish Geodetic Institute

Masala, Finland tuomo.kroger@fgLfi

Abstract- Accurate and robust ubiquitous localization is one of

the most demanding challenges that the navigation research community faces currently. The GNSS offers a perfect solution for open sky environment, however, in GPS-denied or unfavourable environments, for example urban canyons or indoors, the traditional GNSS standalone solution cannot provide the user's position at a reasonable accuracy. Therefore, dead reckoning algorithms are one of the research fields which have attracted most research attention in the last few years. A multi-sensor based PDR (pedestrian dead reckoning) algorithm is presented in this paper, which makes full use of the measurements from a barometer and a 3-axis accelerometer. User's dynamics and motion modes are recognized with different speeds. The walking distance is then calculated based on this information and the human physiological characteristics. Hence, the PDR solution can be propagated and a continuous position solution of the user is

available. Seven user dynamics with 3 different speeds are tested with the system. The algorithm recognizes the speed of the pedestrian both in horizontal and vertical direction, which makes the algorithm suitable to be applied in a multi-floor building which is a more complex task than navigation in a one floor construction.

Keywords: Pedestrian, Dead Reckoning; Speed Recognition

I. INTRODUCTION

Global Positioning System (GPS) has been extensively applied in most open sky environment applications. However, obtaining a seamless indoor/outdoor positioning solution is still a challenging task for any GNSS-only technology. With standalone GPS, it is difficult to get reliable positioning data in urban and indoor environment. To overcome this bottleneck, one solution is self-contained sensors such as barometers and gyros [ 1], accelerometers [2] and digital compasses [3]. These sensors are typically integrated to the GNSS receivers.

II. A UBIQUITOUS POSITIONING PLATFORM FOR

PEDESTRIAN APPLICATIONS

A wearable positioning platform for pedestrian use is designed and tested in a research project at FGI (Finnish Geodetic

978-1-4244-7879-8/101$26.00 ©201 0 IEEE

Wei Chen Department of Electronic Science and Technology

University of Science and Technology of China (USTC) Hefei, China

Institute) supported by the Finnish Funding Agency for Technology and Innovation (TEKES). The handheld platform is I) low in profile to be wearable, II) low in cost (less than 100 €) III) low in power consumption with long field testing capability and IV) equipped with wireless communication capability (Bluetooth connection). The multi-sensor platform consists of a GNSS receiver with an in-built DSP, a VTI 3-axis accelerometer (SCA3000-DOI) and a VTI barometer (SCPIOOO). The GNSS chip which contains a GPS Radio Frequency (RF) down-converter, a microprocessor core and corresponding peripheral circuits, is the core part of the solution. The accelerometer and the barometer communicate with the GNSS chip via an SPI (Serial Peripheral Interface) bus to synchronously feed the corresponding measurements. The digital compass communicates with the GNSS chip via a UART interface.

III. A SPEED RECOGNITION ALGORITHM FOR PEDESTRIAN

DEAD RECKONING

Although the indoor environment casts numerous disturbances to GNSS signals and sensor measurements, it also severely restricts the pedestrian dynamics because within a defined area, the pedestrian's dynamics and activity modes are limited.

If the user dynamics could be detected, the position of the user could be estimated with the following equations [5]:

Ei{H = £i{ Sit s' n(IP j:) (1)

Ni{H = N1i. Si{ c os (1P1i.) (2)

Hi{H = Hi{ tJ.H i{ (3)

where the subscript k denotes the parameter value at epoch k, E is the East position coordinate, N is the North position coordinate, H is the height coordinate, rp is the heading and tJ.H is the height difference calculated from barometer's measurements. S is the walking distance during the last epoch which can be estimated if the user's dynamics can be detected and the user speed recognized.

Barometer offers several kinds of information to positioning. Firstly, from barometer's measurements the user can calculate the information of height. This information can be used in several ways. Height information can be used also inside buildings. With the help of height information, it can be observed more precisely in which floor the user is located. The height can be calculated fairly straightforward from pressure data which can be read directly from the sensor.

The heading can be directly fetched from the digital compass; however, the measurements of the digital compass are contaminated by e.g. the elevator and the metallic fence of the stairs which bears magnetic disturbance. It is difficult to evaluate the performance of the pedestrian positioning algorithm directly without noting that the disturbed heading measurements should be discriminated first, which is impossible with the digital compass measurement only [8] and [9]. Therefore, currently this research is focused on the user dynamics classification and speed recognition, and the full evaluation of the positioning algorithm will be carried out in the future when gyroscope measurements are available in the system for cross checking the heading measurements of the digital compass.

Other information which also can be read directly out from the barometer is temperature. Temperature information can be used to discriminate whether a user is outdoors or indoors. Temperature also helps seamless indoor/outdoor positioning when moving from outside to inside or vice versa.

A sensor which is also used in experiments is an accelerometer. The accelerometer offers accelerations in three axes (x,y,z) about how fast the user is moving.

The user dynamics on a horizontal plane can be estimated from the acceleration measurements by using a simplified algorithm [7]. An experimental walking speed threshold has been specified for each user dynamics mode.

Equation 4 calculates the Euclidean norm of the accelerometer data a(i) from the x, y, z axes measurements for each sample i.

aW = (4)

The varIance of the acceleration a(i) , is calculated after defining a one second window length. Acceleration is calculated in every window.

Three thresholds (TNOrmQ HVQUiit19' Tp.astWQlliit19' 1jo99it19 ) of the variance are defined to recognize three types of pedestrian dynamics: normal walking, fast walking and jogging.

If the variance of the acceleration v is Va $ J;vorma LWQ r liin9 a , the pedestrian is walking at a normal speed. If

TNOrmQLWQtiiin9 < VQ $ TpQsnVQtiiin9 the pedestrian is walking

fast. If Tp<2StWQlk: in9 < vQ $ 1jo99in9 , the pedestrian is jogging, otherwise the pedestrian is running fast.

Different walking speeds have been empirically tested for different user dynamics. For normal walking, the speed is defined as is 1 mis, 1.5 mls for fast walking, and 2.5 mls for jogging.

A step detection algorithm can be used to calculate more precise information of the movement. The algorithm can calculate the variance at every step and the step duration period at every step [5].

The algorithm uses the total acceleration:

afi = CIr-QlI' (k) - B (5)

Where CIr-.aw (k) is the total acceleration of three axes' raw

measurements at time k. The Factor CIr-QW (k) is calculated as shown in Equation 4 and g is gravity and can be calculated at the beginning of the navigation when the user is stationary by:

N

B = I alN i = l (6)

where N is the number of samples when the pedestrian is stationary. A smoothing filter is chosen to reduce the influence of noise

(7)

where 2L + 1 is the length of the smoothing filter.

Then, the smoothed acceleration is sent to a sliding window and calculated whether it satisfies the condition of a peak and the magnitude is bigger than the threshold. If the peak exceeds the threshold, the algorithm has detected a new step and the next item in the process is to find the peak of a new step. When localizing the end of a step, the variance of the acceleration and the time period of the step are computed which are important parameters for step length estimation.

Step length is defined as the distance between the left and the right heel and is calculated as follows:

5 = A · {SIj B ws;ws�N(O , 65 ) (8)

where A and B are regressive coefficients that can be obtained through parameter estimation with a Kalman filter when GPS

is available, SV is the variance of acceleration during the step,

and W3 is Gaussian noise.

With these kinds of sensors and algorithms it is possible to get more precise information where a user is at a certain moment. This requires that the extra information such as the type of the building and the floor-plan of the building are known.

IV. EXPERIMENTS AND RESULTS

The system is tested with seven user dynamics with 3 different speeds. All experiments have been carried out at the FGI's office building to verify the test cases mentioned above. Corresponding data has been archived and analyzed. Figure 1 demonstrates testing environment.

Figure I. Testing environment

The altitude h is calculated from the output of the barometer by the following equation

tLR

h = To (1 _ P(h))9 k Po (9)

where Til and Po are the temperature and pressure at the sea level, respectively, R is the gas constant, k is the temperature lapse rate/the atmospheric temperature gradient, and g is the acceleration due to gravity [4].

The barometer was set on high resolution mode and the sample rate was 1.8 Hz. Accelerometer output rate was set on 20 Hz. These two sensors were synchronized with the ticker of the embedded DSP.

The seven user dynamics defined were the following:

1) Stationary

2) Walking

3 ) Moving on the third floor corridor of FGI

4) Moving up the stairs from the first floor to the third

floor

5) Moving down the stairs from the third floor to the

first

6) Going up in the elevator

7) Going down in the elevator

The three speeds defined were normal walking, fast walking and jogging.

At the start of every test case, the test person stood a minute still before starting to move.

The first two tests were carried out on the third floor corridor in FGI. The test was started at the other end of the corridor. The tester moved to the other end of the corridor and turned back. This was done in every test case. Figure 2 and 3 shows the test results when the test person walked on the third floor corridor of FGI at normal speed. Figure 2 shows the step period of the test. On the y-axis of the figure is the time duration of every step shown and on the x-axis the number of steps that have been taken. The slower the user is moving the larger the step length is which means simultaneously that the longer the step period is as well. Figure 3 shows the step detection results on the same test.

x10'1 Step Period during E\ery Step 18 -

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14 ,

12

� -g 10 ' ·c <l) 0.. a. 8 ' .Sl (f)

6

4

: �w l,����," o 20 40 60 80 100 120 140 160 180 200

Step Count

Figure 2. Step period in normal walk

Figure 3. Step detection in normal walk

Figures 4-7 demonstrate fast walking and jogging in the corridor. In Figures 5 and 7 it can be seen that the accelerations are larger than in normal walking which indicates that the user is moving faster. The same can be seen when comparing the step period figures (4 and 6); the step period gets smaller when acceleration gets larger.

Step Period during E\ery Step

20 .

'" 15 . :g .c: Ol 0.. c. Ol U5 10

5 .

o , WL� o 50 100

Step Count

150

Figure 4. Step period in fast walking

__ t 200 250

Figure 5. Step detection in fast walking

Step Period during E\ery Step

6

5

14 I 1\ g. 3 II

" : 1 1��il1\A�A"�At��vJ\PcM) 0'- ..!

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Figure 6. Step period in jogging

Figure 7. Step detection in jogging

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The next test case concentrates on stepping down and up the stairs in three speeds. Figures 8 - 11 demonstrate tests with normal walking speeds: Figures 8 and 10 illustrate test results where the barometer and the accelerometer measure the height and the acceleration of the test person. The barometer results show that the air pressure is changing which indicates that the person's position is changing in the vertical direction. At the same time the acceleration is changing. This gives us information of the speed. Figures 9 and 11 demonstrate step detection in the stairs. More occurrences can be found in the figure, the faster the test person is moving. Figures 12- 15 show test results of the fast walking speed in the stairs.

Figure 8. Stepping down the stairs at nonnal walking speed

Figure 9. Step detection stepping down the stairs in normal walking speed

Figure 10. Stepping up the stairs with normal walking speed

Figure II. Step detection stepping up the stairs with normal walking speed

-3'-----'-----''--- .01

Figure 12. Stepping down the stairs with fast walking speed

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Figure 13. Step detection stepping down the stairs with fast walking speed

Figure 14. Stepping up the stairs with fast walking speed

Figure 15. Step detection stepping up the stairs with fast walking speed

Figures 16 and 17 demonstrate results when the test person jogged down and up the stairs. Figures 18 and 19 show the step detection results from the same jogging test.

Figure 16. Jogging the stairs down

Figure 17. Jogging the stairs up

Figure 18. Step detection jogging the stairs up

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Figure 19. Step detection jogging the stairs down

Figures 20 and 22 demonstrate the tests when going down and up on an elevator. Figures 20 and 22 present how the air pressure changes which means that the elevator is moving. And when also acceleration changes it signifies that a person is moving in to the elevator and out from the elevator. The same issue can be observed in Figures 21 and 23 where the step detection result is demonstrated. In these figures it can be seen that there are significantly less steps than in the other motion tests where the test person was moving (e.g. Figure 8). There are some differences between Figures 21 and 23 mainly because the test person has been doing some moving around inside the elevator which explains why there are more steps detected when going up on the elevator than when going down. Still there are clearly less steps done than when moving on the third floor corridor.

Figure 20. Elevator down

Figure 21. Step detection on the elevator downwards

Figure 22. Elevator up

Figure 23. Step detection on the elevator upwards

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V. CONCLUSION AND FUTURE WORK

This paper presents a preliminary model of step length estimation and height determination and how they can be used to detect user motion modes and speed in an multi-floor indoor environment.

A pedestrian dead reckoning algorithm and self-contained sensors help determine user motion horizontally and vertically inside a building. Step detection gives useful information of how dense the step rhythm is, and the step period produce information on how much each step is taking time. This information in addition to the acceleration data gives us useful and valuable information on how fast and with what type of motion a user is moving.

A barometer gives us information of the air pressure and this can be calculated to height.

Within a defined area of an indoor construction, the pedestrian dynamics and activity modes are limited. With pedestrian dead reckoning and height determination from a barometer, the restricted user movement and location can be detected, and a seamless navigation solution of the user can be achieved.

Related future work include integrating a MEMS gyroscope to the system due to that some disturbances inside a typical building, like elevators, degrade the usability of digital compasses. Gyroscopes can overcome or at least minimize the problems caused by these disturbances and improve the user attitude measurements indoors.

Because the barometer gives information of the prevailing temperature, future research will also determine the transition when moving from outside to inside when using a pedestrian dead reckoning algorithm and self-contained sensors.

ACKNOWLEDGMENT

This study was fmancially supported by the Finnish Funding Agency for Technology and Innovation (TEKES) (project "3D-Personal Navigation and Location-Based Service for the World Exposition in Shanghai in 20 10).

REFERENCES

[I] 1. Parviainen, 1. Kantola, 1. Collin, "Differential Barometry in Personal Navigation", Proceedings of ION/IEEE PLANS 2008, May 5-8, 2008, Monterey, CA, USA, pp. 148-152.

[2] M. Chowdhary, M. Sharma, A. Kumar, K. Paul, M. Jain, C. Agarwal, G. Narula, "Context Detection for Improving Positioning Performance and Enhancing User Experience", Proceedings of ION GNSS 2009, September 22-25, 2009, Savannah, Georgia, USA, pp. 2072-2076

[3] J Collin, G Lachapelle and J Kl1ppi, "MEMS-IMU for Personal Positioning in a Vehicle - A Gyro-free Approach", Proceedingsof ION GPS 2002, Portland, OR, September 24-27, 2002, pp. 1153-1161.

[4] P.D Groves, "Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems", Artech House Inc., 2008, 518p.

[5] Wei Chen; Zhongqian Fu; Ruizhi Chen; Yuwei Chen; Andrei, 0.; Kroger, T.; Jianyu Wang; " An Integrated GPS and Multi-Sensor Pedestrian Positioning System for 3D Urban Navigation," Proc Urban Remote Sensing Event 2009 Joint, Shanghai China

[6] Y. Chen, R. Chen, L. Pei, T. Kroger, W. Chen, H. Kuusniemi, 1. Liu, "Knowledge-based Error Detection and Correction Method of a Multi­sensor Multi-network Positioning Platform for Pedestrian Indoor Navigation, " Proc IEEE/ION PLANS 2010, Palms Springs, CA, USA.

[7] J. Liu, R. Chen L. Pei, W. Chen, T. Tenhunen, T. Kroger, Y. Chen. "Accelerometer assisted robust wireless signal positioning based on a hidden Markov model", ION/IEEE PLANS 2010, Palms Springs, CA, USA.

[8] O. Mezentsev, J. Collin, G. Lachapelle. "Pedestrian Dead Reckoning in a Solution to Navigation in GPS Signal Degraded Areas", Geomatica 2005,59,2,175-182.

[9] O. Mezentsev. "Sensor Aiding of HSGPS Pedestrian Navigation", PhD Thesis, published as UCGE Report No. 20212, 2005, Department of Geomatics Engineering, University of Calgary, Canada.