An Experimental Study on a Pedestrian TrackingDevice

Sailesh Kumar G1

Electrical Engg. Dept.Shiv Nadar UniversityNoida, 201314, India

Shyam Sundar S1

Electrical Engg. Dept.Shiv Nadar UniversityNoida, 201314, India

Amit K GuptaGT Silicon Pvt Ltd

Kanpur, 208016, Indiaamitg@gt-silicon.com

Peter HandelDept. of Signal Processing

KTH Royal Institute of TechnologyStockholm, Sweden

Abstract—The implemented navigational algorithm of an in-ertial navigation system (INS), along with the hardware con-figuration, decides its tracking performance. Besides, operatingconditions also influence its tracking performance. The aim ofthis study is to demonstrate robust performance of a multipleInertial Measurement Units (IMUs) based foot-mounted INS,The Osmium MIMU22BTP, under varying operating conditions.The device, which performs zero-velocity-update (ZUPT) aidednavigation, is subjected to different conditions which couldpotentially influence gait of its wearer, its hardware configurationetc. The gait-influencing factors chosen for study are shoe type,walking surface, path profile and walking speed. Besides, thetracking performance of the device is also studied for differentnumber of on-board IMUs and the ambient temperature. Thetracking performance of MIMU22BTP is reported for all thesefactors and benchmarked using identified performance metrics.We observe very robust tracking performance of MIMU22BTP.The average relative errors are less than 3 to 4% under all theconditions, with respect to drift, distance and height, indicatinga potential for a variety of location based services based on footmounted inertial sensing and dead reckoning.

I. INTRODUCTION

The advancements in MEMS technology have paved theway for low cost sensors which can be integrated into anInertial Navigation System (INS). These sensors are also lesspower craving and occupy far lesser size, than before. INSsare self-contained systems that can operate in harsh radioenvironments without pre-installed infrastructure and priorknowledge of the environment. This opens up applications inemergency response situations where the system has to be self-sufficient at least for a short period of time [1], [2], [3].

Conventional INSs calculate the change in position throughdead reckoning. In pedestrian navigation with foot-mountedINSs, this means measuring the length of the steps andthe direction of the pedestrian given the initial position andheading is known. They calculate double integration of ac-celeration measurements and single integration of gyroscopemeasurements which are noisy and therefore suffer from hugeaccumulation of position and heading errors with time. This

1Sailesh Kumar G and Shyam Sundar S carried out the presented work aspart of their internship program at GT Silicon Pvt Ltd, Kanpur.

Fig. 1: The Osmium MIMU22BTP : A foot-mounted INS for ZUPT-aidedpedestrian navigation. Operating on a rechargeable Li-ion battery, it is capableof transmitting data over Bluetooth and USB interfaces, both. The deviceutilizes multi-IMU (MIMU) approach which enables data fusion from on-board redundant inertial sensors and hence improves the tracking performance.

is because the new estimates of position and heading rely onprevious estimates of the same [1].

Zero-velocity-update (ZUPT) is one of the popular methodsin literature to minimise the errors encountered in position andheading estimates for foot-mounted INSs. There is a standstillphase in walking, when the foot comes in complete contactwith ground. The instantaneous velocity of the foot, for thatmoment becomes zero. This essentially means that the foot-mounted sensor is stationary at standstill position and thereforeany non-zero estimated velocity or rotation is interpreted aserror and is used to reset the estimated velocity to zeroand correct the system's internal errors. ZUPT allows thefoot-mounted INSs to correct errors at every step occurrence(typically every second with normal gait). Without ZUPT thedrift error would have grown cubic with time. It increasesthe accuracy of conventional INS thus making it suitablefor a large number of promising applications in the area offoot-mounted indoor navigation, for example tracking in GPSdenied environments [4], [5].

Tracking performance of a foot-mounted pedestrian navi-gation device depends upon number of factors which couldinfluence the way hardware and the embedded algorithmoperate. Some of the factors which could influence the hard-ware operation are different number of on-board IMUs andthe ambiance temperature. Similarly the operating conditions

Copyright ©IEEE2015

which could influence the ZUPT based navigational algorithm,are shoes, wearers of the device, walking surface, walkingspeed and the path profile. These factors typically influencegait of a person and hence play important role in step detectionfor a ZUPT-aided INS.

In this paper, we present an experimental study on theZUPT-aided foot-mounted pedestrian navigation device Os-mium MIMU22BTP, shown in Fig. 1, to demonstrate influenceof various factors on its performance.

This paper is organized into the following sections. SectionI presents brief introduction to the device under test. Section IIoutlines the experimental design and the performance metricswhich are used for benchmarking the performance of thedevice. Experimental results are present in Section III. InSection IV conclusion of the study is outlined.

II. DEVICE UNDER TEST

The Osmium MIMU22BTP is a foot-mounted navigationdevice which implements ZUPT-aided inertial navigation al-gorithm [6]. Osmium MIMU22BTP is based on an open-source platform OpenShoe, first presented in [7] – a de-vice initially targeting cooperative localization by dual foot-mounted inertial sensors and inter-agent radio-frequency basedranging [8], [9]. The recent version presented in [6] containsmultiple IMUs each of which consists of MEMS sensorsaccelerometers, gyroscopes and magnetometers. This “wisdomof the crowd approach” not only reduces the errors by usingmultiple inertial sensors but also provides sensor redundancy.In simple words, MIMU22BTP detects step of its wearer,computes relative coordinates and heading of each detectedstep with respect to the previous one and transmit it overBluetooth interface to the application platform for constructionof the tracked path. Presence of IMU arrays in MIMU22BTPenables advanced motion sensing by using sensor fusionand array signal processing methods. The presence of on-board microcontroller with floating point processing capabilitysimplifies the output data interface and hence very low rate oftransmission (∼ 1Hz) is achieved [6]. The device is supportedby open source embedded code in C which implements ZUPT-aided navigation.

The accelerometers in these systems measure the linearacceleration when the system-in-motion is subjected to a force.The gyroscopes measure the angular rotation of the systemin terms of roll, pitch and yaw. Since these devices canmeasure in a particular orthogonal axis of motion, there arethree accelerometers and three gyroscopes in a single IMUto measure acceleration and angular velocity respectively inall the three axes. Magnetometer is not used for navigation inOsmium MIMU22BT because the device is targeted towardsindoor navigation and the presence of ferrous objects in thetracking path such as the computers, electric wires aroundthe building may cause magnetic interference. Calibration isrequired to be performed to compensate the errors whichoccur due to the fabrication process. Calibration under staticconditions is carried out by placing the device inside atwenty faced polyhedron (icosahedron) different orientations.

Fig. 2: The foot-mounted Osmium MIMU22BTP. The experiments areperformed with a single device mounted on the shoe front.

Fig. 3: The data recording Android application DaRe. MIMU22BTP com-municates with DaRe via Bluetooth. DaRe constructs the estimated data pathand records step coordinates, step number, time stamp of each step and otheruseful information.

Inter-IMU misalignment, gain, bias and sensitivity axis non-orthogonality of the accelerometers are then estimated byplacing the icosahedron in different positions [10].

The MIMU22BTP comes equipped with four 9-axis IMUs,32-bits floating point microcontroller, Bluetooth and USB con-nector for data communication and an on-board Li-ion batterypower management circuitry. This configuration makes it arobust embedded system for possible wearable applications,tracking and motion detection needs. The tracking deviceis also equipped with an on-board pressure sensor, a flashmemory and JTAG programming capability. The device canbe attached to the shoe as shown in Fig. 2 and with thehelp of an application on a processing platform, the user canfind coordinates of the estimated path along with the distancecovered. Android based data recording application DaRe isone such application which receives pedestrian dead reckoningdata via Bluetooth and constructs the estimated path as shownin Fig. 3. Prior to mounting, the device is switched on andconnected to DaRe via Bluetooth. When the step is detected,i.e when the foot-mounted device experiences zero velocity,

TABLE I: Summary of the test tracks. Three different tracks are used forexperiments.

Rectangular Field Straight Path 1 Straight Path 2Perimeter: 129 m Length: 26 m Length: 100 mClosed loop path Sharp 180° turns Sharp 180° turn

3 laps 2 laps 1 lapTotal distance: 387m Total distance: 104 m Total Distance: 200m

Fig. 4: Bird eye view of the rectangular (left) and straight (right) tracks.Arrows show the walking directions.

the dead reckoning updates are sent from the device to DaRe.

III. EXPERIMENTS

A. Design of experiment

The experiments were carried out using a marked track inthe path profiles given in Tab. I. Pictures of the test sitesare shown in Figs. 4-5. The mounting point on the foot (orshoe) was carefully chosen so that the device was firmlymounted on the shoe. The device was allowed to reach a steadytemperature before the tests were carried out by allowing awarm-up time of two to three minutes. The device wearer's gaitand certain conditions potentially affect the path estimation.Therefore these variables have been chosen in the analysisof the device. The device wearer's gait is affected by thetype of shoe, path profile, walking speed, walking surfaceetc. The other conditions refer to varying temperatures andvarying number of on-board IMUs. Achieving right mountingis an iterative process. The experiments were performed withthe device mounted on shoe front as shown in Fig. 2. Forevery experiment, single device was attached to wearer's foot.GPS, knowledge of the environment and any other kind ofpre-installed infrastructure were not used for navigation.

Details of the experiments conducted under different con-ditions are presented in Tab. II and III with the total elapseddistance and average speeds for each set of experiment.

B. Performance metrics

The performance metrics used to benchmark the perfor-mance of the device are as follows: The drift error (Drifterror):

Drifterror =1

N

N∑i=1

√(xi,start − xi,end

)2+(yi,start − yi,end

)2di,act

(1)where xi,start and yi,start are the estimated ith start point inx axis and y axis of the user's reference frame, respectively.

Fig. 5: Satellite view of the rectangular test site. Path traversed is shown bydots. Arrows show the walking direction.

TABLE II: Details of the tests conducted with varying operating conditionswhich could influence gait.

S. No. Gait Influencing Factors Experimental DetailsTotalDis-

tance(km)

AverageSpeed

(kmph)

1 Shoe and UserRunning Shoe

(User#1) 7,27 4.41

Formal Shoe(User#2) 7.97 4.51

2 Surface Pavement 8.70 5.43Grass 6.54 4.58

3 SpeedSlow 4.60 3.51

Medium 5.56 4.52Fast 5.07 5.81

4 Path profile Rectangular 8.00 4.16Straight 7.13 4.28

Similarly, xi,stop and yi,stop are the estimated ith stop pointin x axis and y axis, respectively. The total number of testcases is denoted by N and di,act denotes the actual distancecovered in ith test case. Drifterror in percentage provides themagnitude of displacement between estimated start and stoppoints, which are coinciding in reality, per 100 m distancecovered.

The distance error (Distanceerror):

Distanceerror =1

N

N∑i=1

√(di,meas − di,act

di,act

)2

(2)

where di,est denotes the distance estimated by the OsmiumMIMU22BTP in ith test case. Distanceerror in percentageprovides the root-mean-square of distance estimation error per100 m distance covered.

The height error (Heighterror):

Heighterror =1

N

N∑i=1

√(zi,start − zi,end

di,act

)2

(3)

where zi,start and zi,end are the estimated ith start and endpoints in the z axis respectively. Heighterror in percentageprovides the root-mean-square height estimation error per 100m distance covered.

The experiments were conducted on plane surfaces, for allthe path profiles. Therefore only x-y coordinates are consid-ered in computing distance and drift errors.

TABLE III: Details of the tests conducted with varying factors which couldinfluence hardware performance.

S. No. External Factors Experimental DetailsTotalDis-

tance(km)

AverageSpeed

(kmph)

1 Temperature 25.7°C-34.0°C 21.20 4.6734.1°C-37.7°C 17.90 4.66

2 Number of IMUs4 2.00 4.452 2.97 4.591 3.00 4.56

Fig. 6: Rectangular path as estimated by MIMU22BTP. Dots correspondto the detected steps. The coordinate axes indicate the distance covered inmeters.

IV. RESULTS & DISCUSSION

A. Experimental results

The quality of data depends on the mounting of the deviceon the shoe. When the output data from the device is plotted,the estimated rectangle and straight paths are observed asshown in Figs. 6-7. Manual mounting of the device resultedin the slight misalignment of the plotted path with the globalx-y axes.

The results with respect to shoe-type are presented in Tab.IV, type of walking surface in Tab. V, walking speed in Tab.VI, path profile in Tab. VII, number of enabled IMUs in theMIMU22BTP in Tab. IX and ambient temperature in Tab. VIII.

TABLE IV: Performance versus shoe-type.

Performance Metric Formal RunningDrift Error (%) 0.98 1.20

Distance Error (%) 1.61 1.69Height Error (%) 1.61 3.39

TABLE V: Performance versus type of walking surface.

Performance Metric Pavement GrassDrift Error (%) 1.22 0.89Distance Error (%) 1.75 1.45Height Error (%) 2.80 3.80

Fig. 7: Straight path as estimated by MIMU22BTP. Dots correspond to thedetected steps. The coordinate axes indicate the distance covered in meters.

TABLE VI: Performance versus walking speed.

Performance Metric Slow Medium FastDrift Error (%) 1.16 1.09 1.01Distance Error (%) 1.06 0.83 2.53Height Error (%) 2.26 1.65 4.61

TABLE VII: Performance versus path profile.

Performance Metric Rectangle StraightDrift Error (%) 0.86 1.15

Distance Error (%) 1.14 1.78Height Error (%) 1.55 3.44

TABLE VIII: Performance versus ambient temperature.

Performance Metric 25.7°C-34°C 34.1°C-37.7°CDrift Error (%) 1.64 2.54Distance Error (%) 0.73 0.81Height Error (%) 2.36 2.73

TABLE IX: Performance versus number of enabled IMUs.

Performance Metric 4 IMUs 2 IMUs 1 IMUDrift Error (%) 1.30 2.04 2.05Distance Error (%) 0.84 1.85 1.51Height Error (%) 1.76 1.82 1.54

B. Discussion

For all the experiments, we have observed that the drift anddistance errors are within 3%. Under all the conditions, errorsin height estimation are higher as compared to the errors (driftand distance) in walking plane x-y. This is due to the reasonthat fabrication process is more controlled for the sensors usedfor x and y motion than for z motion. The height error and drifterror are almost independent of each other, with a coefficientof correlation around 0.06.

From the results in Tab. IV, one may note that a formalshoe performs better tracking compared to a running shoe.

This can be explained by the fact that running shoes haveflexible structure and are elastic in nature, which will influencethe inertial navigation device’s ability to detect standstill.Conversely, formal shoes have rigid structure which makesit easier for the device to detect standstill events.

Walking surface with grass gives somewhat better trackingperformance than pavement with respect to drift and distancemeasurement as indicated in Tab. V. Walk on pavement turnsout to be somewhat better in terms of height error.

In the ZUPT-aided device, errors are corrected only whena step is detected. Since the velocity thresholds are optimizedfor giving best performance for a normal walking speed (thatis, around 4 - 5 kmph), certain steps go undetected by thedevice at higher speed. In spite of that, the drift error isalmost the same for all walking speeds, as indicated by theresults presented in Tab. VI. If the MIMU22BTP is mountedproperly onto the shoe, it delivers high quality data upto 5kmph. Though data starts deteriorating beyond that, it shouldbe fine for tracking upto 5.5 to 6 kmph, as demonstrated.

Walk on the rectangular path has resulted in somewhat bettertracking performance than on straight line walk as indicatedin Tab. VII. The 100 m straight line walk consisted of sharpu-turns, whereas rectangular path consisted of round edges.

Another interesting observation is seen when experimentswere conducted at different ambient temperatures (See TableVIII). The scale 34.1°C-37.7°C indicates data collected duringafternoon of the April month (at Kanpur, India) for which thedrift and height error are higher, though within acceptablelimits, compared to the 25.7°C-34.0°C scale which repre-sents data collected in early morning, forenoon and evening.Tracking performance deteriorates a bit at higher ambienttemperature.

A clear difference in performance is seen when the numberof IMUs are reduced from four to two or one (See TableIX). Even though, the height error is comparable for all thethree cases, the distance and drift errors are higher in casethe number of IMUs is two or one. This demonstrates trade-off in performance for reduced cost as the number of IMUsare reduced. This maybe interesting to note that number ofIMUs are changed without making any change in the algorithmand without changing any important parameters which wereinitially fine tuned for four IMUs. We hope to achieve resultssomewhat better than reported, by fine tuning the parametersfor one and two IMUs configuration.

V. CONCLUSION

The Osmium MIMU22BTP was tested under various con-ditions (type of shoe, walking surface, walking speed, pathprofile, ambient temperature and number of on-board inertialsensors) which influence the tracking performance of a foot-mounted inertial navigation device. Errors in drift, distanceand height measurement were chosen to benchmark the per-formance. Experiments were conducted on plane surfaces.For every experiment, single device was attached to wearer'sfoot. GPS, environmental information and any other kind pre-installed infrastructure were not used for tracking.

Results obtained from the experiments ascertain the robustperformance of the navigation device under different operatingconditions. Very small variation in errors is observed for allthe considered cases. Drift and distance errors are alwayswithin 3% irrespective of type of shoe, nature of walkingsurface, wearer's walking speed, type of path and ambienttemperature. Whereas height error is within 4% for most of thecases. This means that Osmium MIMU22BTP is capable ofdelivering more than 96% tracking accuracy. There is hardlyany correlation observed between drift and height errors.This is also experimentally demonstrated that the trackingperformance improves by increasing the number of on-boardinertial sensors which is a highlighting feature of OsmiumMIMU22BTP.

In very simple words, one may infer from the presentedexperimental study that the multiple-IMU based foot-mountednavigation device Osmium MIMU22BTP is capable of locat-ing a pedestrian who has walked for 100 m on a plane surface,in a circle of radius 3 m. This performance expectation iswithout any aid of GPS data, environmental information orany other pre-installed infrastructure.

VI. ACKNOWLEDGEMENT

The authors acknowledge GT Silicon Pvt Ltd for providinglogistical support to carry out the study. They also acknowl-edge Swedish Governmental Agency for Innovation Systemsfor supporting work of Peter Handel.

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