Improving Inertial Navigation Systems with Pedestrian Locomotion

Classifiers

Courtney Ngo

, Solomon See

and Roberto Legaspi

College of Computer Studies, De La Salle University-Manila, Manila, Philippines

Research Organization of Information and Systems, The Institute of Statistical Mathematics, Tokyo, Japan

Keywords: Machine Learning, Inertial Navigation System, Sensors.

Abstract: Researches on inertial navigation systems (INS) have formulated complex step detection algorithms and

stride length estimations. But for current systems to work, INSs have to correctly identify negative

pedestrian locomotion. Negative pedestrian locomotion are movements that a user can naturally make

without any real position displacement, but has sensor signals that might be misidentified as steps. As the

INS’s modules have a cascading nature, it is important that these false movements are identified beforehand.

This research aims to provide a solution by studying patterns exhibited by positive and negative pedestrian

locomotion when sensors are placed on a user’s front pocket. A model was then built to classify negative

from positive pedestrian locomotion, and to improve the INS’s accuracy overall.

1 INTRODUCTION

Indoor navigation systems determine where a device

has traversed inside a building. These navigation

systems can be employed in applications to help

users find a specific location in closed places like

conference centers and office buildings. Unlike

outdoor navigation systems like the Global

Positioning System (GPS), indoor navigation

systems cannot use satellite signals as heavy

attenuation takes place when the signals make their

way through physical obstacles.

To solve this, researchers have experimented

with Wi-fi signals like (Bahl and Padmanabhan,

2000a), (Bahl and Padmanabhan, 2000b), (Battiti,

2005), (Youssef et al., 2003), and (Youssef and

Agrawala, 2004); vision (Karlsson et al., 2005);

ultra-wide bands (Teuber and Eissfeller, 2006);

cellular-based signals (Otsason et al, 2005);

magnetometers (Chung et al., 2011); and

combinations of these (Brunato and Battiti, 2005).

All of these researches are dependent on

environment variables such as Wi-fi routers and

markers, and some require data collection prior to

system use. This would mean that a significant

change in the environment or the variables would

affect the performance of these navigation systems.

INSs, on the other hand, uses data from inertial

sensors such as gyroscopes and accelerometers to

determine the path a device has travelled. Smart

phones currently already have these sensors as

micro-electrical-mechanical systems (MEMS)

devices, making it possible for INSs to be applied in

smart devices and possible for ubiquitous use.

Compared to other navigational systems, INSs are

independent of its environment, requiring less cost

that otherwise would have incurred with the need of

access points. This also implies less environment

set-up as access points do not need to be installed for

the navigation system to operate. Considering that it

is a cheaper and simpler alternative, INS appears to

be a more attractive approach to building navigation

systems.

2 CHALLENGES

Using INSs in real-world situations, however, is

limited because its MEMS devices are susceptible to

noise and gradual drifts that cause cascading errors.

Because of this, most existing INSs integrate regular

checking with access points with known positions

such as satellites and Wi-fi routers to calculate the

position of the mobile unit to compensate for these

inaccuracies (Martin et al., 2006).

Another problem, which this study intends to

address, is correctly classifying irregular move-

ments. In this research, positive pedestrian

202

Ngo C., See S. and Legaspi R..

Improving Inertial Navigation Systems with Pedestrian Locomotion Classiﬁers.

DOI: 10.5220/0005242802020208

In Proceedings of the 5th International Conference on Pervasive and Embedded Computing and Communication Systems (PECCS-2015), pages

202-208

ISBN: 978-989-758-084-0

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

locomotion is defined as movements that include

moving from one physical position to another on

foot. Examples of these are walking, jogging,

running, and climbing up and down the stairs. False

pedestrian locomotion are movements that do not

require moving from a position, such as standing.

There are, however, some false pedestrian

locomotion movements that can simulate movement

from position, and these presents a problem to some

existing INSs. These movements include walking-

in-place, jogging-in-place, and running in place. It is

important future INSs can correctly disregard false

pedestrian locomotion movements to avoid

cascading errors as the modules depend on each

other as displayed in Figure 1. Similarly, it cannot be

expected that users would not exhibit any form of

negative pedestrian locomotion movements in real-

world applications. An INS that considers in these

negative movements will better suit mobile

applications that plan to map user paths in an area.

Figure 1: Conventional system flow of inertial navigation

systems.

The main objective of this research is to solve

this problem by creating an additional module in an

INS whose role is to classify whether a user is

making a positive or negative pedestrian locomotion

movement. In the proposed solution, false pedestrian

locomotion movements will be properly detected,

thus false steps would be avoided. This will

consequently affect the estimated path length of the

user and is hypothesized to improve the outputs of

the INS.

3 REVIEW OF RELATED

LITERATURE

3.1 Pedestrian Locomotion Heuristics

There are currently no studies that have a separate

module to classify positive from negative pedestrian

locomotion, but there are some that have integrated

similar measures in their step detection algorithms.

In some studies like (Lee and Mase, 2001) and (Li et

al., 2012), additional heuristics were implemented to

prevent allowing false positive steps. These

heuristics are hard-coded based on each study's

preliminary data. As it is, more heuristics will need

to be added to allow more movements.

Although the following research did not take into

consideration a wider range of movements compared

to this study, their heuristics were able to prevent

certain negative pedestrian locomotion movements

as positive.

3.1.1 Lag Parameter

In a study conducted by (Lee and Mase, 2001), a lag

parameter was added in their step detection

algorithm. With the lag, the system can supposedly

check if the step taken is not a step but another body

movement. It involves getting the z-axis of the

accelerometer that is indicative of upward

movements of the leg. The lag parameter is as

follows:

lag=min

…

z







nj









(1)

where lag is the lag parameter, N is the window size,

and z(n) is the z-axis value of the accelerometer at

time j, which usually changes as the subject raises

his leg.

The lag must be greater than a threshold to pass

the heuristic. As can be seen in the equation, the

study assumed that other body movements would

have less activity in the accelerometer's z-axis, and

that walking would induce peaks in the z-axis.

However, walking-in-place would also express a

high activity in the z-axis even though it is truly a

false pedestrian locomotion movement.

3.1.2 Dynamic Time Warping

In (Li et al., 2012)’s study further used dynamic time

warping (DTW) as an added filter to detect false

steps. Aside from (1) checking if peaks and valleys

pass a certain threshold, (2) peaks and valleys must

also not be too short, or (3) too long (maximum of 1

second). Acceleration's peak and valley's magnitudes

are also considered, where (4) the magnitude must

be within a minimum of 0.2g, and a maximum of

2.0g.

With DTW, two more heuristics were formed. A

fifth heuristic uses DTW to calculate the similarity

of steps taken with the right leg, and similarity of

steps taken with the left leg. In this condition, the

ImprovingInertialNavigationSystemswithPedestrianLocomotionClassifiers

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similarity of the last step taken with the left/right

foot and the current step taken with the left/right foot

must be greater than a threshold. If the result is

negative, a sixth heuristic compares the current left

step with the next left step. If these two signal's

similarity passes the threshold, the current left step

would be considered a step. With this method, their

step detection algorithm can tell the difference

between a step taken while walking and a step taken

while walking-in-place given that the two steps are

taken after the other and the false step is just a

momentary gap from a series of true pedestrian

locomotion movements. However, their system can

still possibly fail if the user continues to perform a

false pedestrian locomotion movement.

After adding the DTW heuristic, the research

recorded a drop in false positives (incorrectly

processed false steps) from 29 to 14.

In the study, false negatives are more important

than false positives. False positives can be further

checked with the step detection algorithm. Even if a

false step was considered a step in the pedestrian

locomotion model, there is still the possibility that

the false step would be detected as false by the step

detection algorithm. The false negatives increased

from 0.4 to 0.5. But as stated in the study, the

benefits outweighed the disadvantages.

4 PROPOSED SOLUTION

This research proposes to create a separate module

in the standard INS framework that will focus on

classifying a movement as either false or true

pedestrian locomotion movement. As shown in Fig.

2, the new module would operate first before the

step detection module. If the module identifies a

window of movement as false pedestrian

locomotion, the succeeding modules would not

process that window. If it does detect the window as

true, the succeeding modules would operate

normally. This would imply that the INS could

Figure 2: Proposed system of the research.

perform more efficiently should the new module

classify well. On the other hand, a cascading error

can transpire instead.

4.1 Inertial Navigation System

A simple INS would be created to compare the

performance of a conventional INS against an INS

with the pedestrian locomotion detection module.

The modules are discussed below along with the

algorithms and heuristics used in each.

4.1.1 Step Detection Module

The step detection module would detect steps from

accelerometer signals once the pedestrian

locomotion model determines that the user is

performing a positive pedestrian locomotion

movement.

The accelerometer signals would be scoured for

a value greater than threshold α. In order to discard

false peaks, a second threshold βis introduced.

Threshold βis the minimum time gap between two

steps. Before a step is identified, the time gap

between the said step and the previous step must be

greater than threshold β.

Both thresholds were determined after collecting

user data.

4.1.2 Stride Length Estimation Module

The stride length estimation module would start

calculating for the step length once the Step

Detection Module has determined the user made a

step. A linear model would be created as previous

studies such as (Li et al., 2012) have shown before

that a linear relationship exists between stride length

and step frequency. This module would update the

step frequency along with the Step Detection

Module. A linear model would be generated after

collecting data.

4.1.3 Heading Determination Module

This module would work side-by-side with the

Stride Length Estimation Module after the Step

Detection Module determines a step has been taken.

It is responsible of approximating the direction the

user is heading. In this research, the orientation y-

axis data would be used to determine the heading.

The values can range between 0° and 359°.

4.1.4 Mapping Module

The mapping module outputs a series of points

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indicating a user's traversal across a space. It would

receive inputs from the stride length estimator

module and heading determination module, and

would have knowledge of the coordinates of the

previous point. The coordinates of the initial point

would be set to (0,0).

The new point would be calculated as:



=l∗cos





+x



(2)



=l∗sin





+



(3)

where 



is the x-coordinate of the current point,





is the y-coordinate of the current point, 



the x-coordinate of the previous point, and 



the y-coordinate of the previous point, l is the stride

length, and  is the heading.

4.2 Pedestrian Locomotion Model

As the main component of the pedestrian locomotion

module, the pedestrian locomotion model is a

classifier that identifies movements as either positive

or negative pedestrian locomotion movements. A

discussion of how the model was created is written

below.

4.2.1 Data Collection

In this research, 30 subjects will participate by

performing 12 movements for data collection. Each

subject should be at the age range of 19 to 49 years

old, as a stable gait has been found across that age

range (Thanh et al., 2012). On a similar note, the

subjects should also be able-bodied. Every subject

will perform each of the 12 movements for 5

minutes each. The 12 movements are composed of 3

positive pedestrian locomotion movements: (a)

walking, (b) climbing down stairs, and (c) climbing

up stairs; and 9 negative pedestrian locomotion

movements: (d) turning, (e) standing, (f) swinging

one's legs, (g) sitting, (h) twisting, (i) walking in

place, (j) leaning on the heels and balls of one's feet,

(k) doing random movements in place, and (l)

bending. The random activity can be used to test the

robustness of the model in terms of classifying

unlisted movements in future research.

A Samsung Galaxy S2 phone was used to collect

data. For this purpose, a mobile application was

developed to collect sensor readings from the tri-

axial gyroscope and tri-axial accelerometer at a rate

of 100Hz. The phone was placed in the subjects's

right-side pockets at the front. Placing the phone in

the mid-section of the subject is strategic as it is the

person's center of gravity, making it sensitive to

movements made with the limbs. The position is

also a typical location phones are placed in. The

phone is limited to a specific orientation that faces

the phone screen towards the thigh of the subject,

and the top of the phone is pointed down.

4.2.2 Feature Modelling

The data entries would be grouped into windows of

size 100. This window size is equivalent to a second

worth of records, and will have an overlap of 50%.

Three features were extracted from each of the

sensors's axes: mean, standard deviation, and energy.

These features were extracted without removing the

gravity factor from the readings, or applying any

filter.

4.2.3 Model Generation

A C4.5 model and a support vector machines (SVM)

model would be generated using WEKA's J48 and

sequential minimal optimization (SMO) algorithms.

The model would be used in the pedestrian

locomotion detection module, and would determine

if the person is performing a positive or negative

pedestrian locomotion movement.

5 RESULTS AND DISCUSSION

5.1 Tests

Two kinds of test were conducted to evaluate the

INS with and without the prediction module: the

square route test, and the multi-activity square route

test. The tests were carried out by six subjects,

wherein they were limited to follow a marked route,

to execute movements as instructed to them, and to

only bring the phone out at the beginning and end of

each test. The subjects were allowed to walk on their

own natural regular pace.

Square Route Test: The square route test is a

20m walk that is composed of four five-meter

sections that are orthogonal after one another. The

route is purely positive pedestrian locomotion, and is

intended to test the prediction model's performance

in a situation where an INS without a prediction

module will perform perfectly. Another factor to

analyse is the model's ability to classify "walking

while turning" from "turning in place".

Multi-Activity Square Route Test: The multi-

activity square route test is similar to the square

route test but introduces negative pedestrian

locomotion in every corner. The routine, which is

presented in Figure 3 begins with 1) a five meter

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walk, 2) five seconds of standing, 3) five meter walk

after a perpendicular turn, 4) five seconds of

walking-in-place, 5) five meter walk after a

perpendicular turn, 6) five seconds of bending, 7)

five meter walk after a perpendicular turn, and 8)

five second of twisting. Since walking five meters

usually takes 3.5 to 5 seconds, this test is the more

balanced in terms of number of the positive and

negative pedestrian locomotion movements. This

makes this test a good way to evaluate the INS with

a prediction module.

Figure 3: The multi-activity square route test is composed

of negative and positive pedestrian locomotion.

5.2 Prediction Module

For the square route test, the recall of positive and

accuracy are the same because it is a purely positive

activity. The INS without a prediction module also

has a 100% recall and accuracy for the same reason.

As can be seen in Table 1, the J48 and SMO

models performed well. A closer look at the J48

results reveals that three subjects had data that were

all correctly classified, while the other three had one

misclassification each. The SMO model correctly

classified all instances as positive except for one

misprediction.

The multi-activity square route shows a lower

accuracy and recall for both models. It is, however,

important to note that both models have an

acceptable recall on positive. In this research, the

recall on positive locomotion is more important.

Accidentally predicting a negative locomotion as

positive does not automatically mean that steps will

be detected; the possibility of the step detection

module to not detect steps is still open.

Table 1: Prediction module results for INSs with and

without a prediction module.

Recall on

Positive

Recall on

Negative

Accuracy

Square

Route

J48 97.03% 97.03%

SMO 99.01% 99.01%

Multi-

Activity

Square

Route

w/out 100.00% 0.00% 48.51%

J48 87.72% 67.77% 77.45%

SMO 90.35% 71.07% 80.43%

5.3 Step Detection Module

It is expected that the step count error in the step

detection module will decrease if the INS will use a

prediction module. Table 2 shows the step count

error produced by the INS with and without a

prediction module.

The square route test reveals that the INS without

a prediction module performs better, only

mispredicting seven steps. The INSs with the

prediction modules had a higher error, with the J48

model missing 12 steps and the SMO eight steps.

This suggests that the additional module allowed

more false negatives than an INS without a

prediction module.

But the multi-activity square route test presents a

different outcome where the INSs with the

prediction module now performs more accurately in

terms of step count error. Both prediction models

elicited a significantly lower step count error. This

indicates in exchange of versatility when it comes to

negative locomotion, some false negatives were

allowed to be made. But given the difference in

errors, the benefits of having a prediction module

outweigh the disadvantages. INSs with the module

are more adaptable in terms of allowing the subject

to perform negative pedestrian locomotion.

Table 2: Step detection module results for INSs with and

without a prediction module.

Actual #

of Steps

Estimated #

of Steps

Error

Square

Route

without

188

181 3.72%

J48 176 6.38%

SMO 180 4.26%

Multi-

Activity

Square

Route

without

193

296 34.80%

J48 233 17.17%

SMO 233 17.17%

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5.4 Stride Length Estimation Module

As with the step detection module, the error in total

distance travelled will also be assessed for the stride

length estimate module. The length errors of the

INSs with and without a prediction module are

presented in Table 3.

Similar to the results of the step detection

module, the error result is based on the kind of test

the INS undergoes. This is because the stride length

estimation module's result is also based on the

preceding step detection module; that is the total

distance travelled is directly proportional to the step

count. Given this, the INS without the prediction

module performed better in the square route test,

while the INS without it had a better accuracy in the

multi-activity square route test. Though there is a

clear advantage when the basic INS is used in a

purely positive activity, using a classification model

introduces versatility to the system.

Table 3: Stride length estimation module results for INSs

with and without a prediction module.

Actual

Length (m)

Estimated

length (m)

Error

Square

Route

without

120

117.98 1.69%

J48 114.70 4.41%

SMO 117.29 2.26%

Multi-

Activity

Square

Route

without

120

193.67 61.39%

J48 151.97 26.64%

SMO 161.65 34.71%

5.5 Mapping Module

The final output of the INS is the route the user has

traversed. For both tests, the INS with the prediction

module fared well. Both J48 and SMO prediction

Figure 4: Map generated using J48 and SMO as pedestrian

locomotion classification models.

modules came up with close results, one of which is

shown in Figure 4. In the figure, the route in black is

the actual route and the route in yellow is the

estimated route of the INS. Although the distance

travelled was accurately measured, the orientation

was not determined properly, thus resulting to an

incorrect map.

It is also important to note that the INS used in

this research worked well with the given route

because it is limited to perpendicular orientations.

The heading determination module used in this

research is especially basic and still needs

improvement.

6 CONCLUSIONS

This research was able to present that a J48 and

SMO pedestrian locomotion classifier can increase

the over-all performance of an INS. The step

detection and stride length estimation module also

benefited from the prediction model especially with

experiments that have negative pedestrian

locomotion activities.

In conclusion, the results have shown that adding

a pedestrian locomotion module allows an INS to be

more versatile. An INS with a prediction module can

handle negative pedestrian locomotion activity,

while a normal INS will require users to walk

continuously and maintain a low sensor activity to

prohibit a negative pedestrian locomotion activity to

be falsely considered a step. And as negative

pedestrian locomotion activities are inevitable in real

scenarios, a prediction module presents an adequate

solution to this INS problem.

Further research can delve into further testing the

system for its capabilities and weaknesses.

Additional work still needs to be done to improve

the heading determination module of the system,

which is currently limiting the system to specific

routes. Future studies can also focus on employing

additional sensors to improve and compensate for

the MEMs inherent noise.

ACKNOWLEDGEMENTS

This research is made possible with funding from

the Philippine government. The researchers would

like to thank the Department of Science and

Technology's Engineering Research and

Development for Technology (DOST-ERDT)

program for its incalculable support for this study.

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