activity sequence-based indoor pedestrian localization using smartphones

This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS 1

Activity Sequence-Based Indoor PedestrianLocalization Using Smartphones

Baoding Zhou, Student Member, IEEE, Qingquan Li, Qingzhou Mao, Member, IEEE, Wei Tu, Member, IEEE,and Xing Zhang

Abstract—This paper presents an activity sequence-based in-door pedestrian localization approach using smartphones. The ac-tivity sequence consists of several continuous activities during thewalking process, such as turning at a corner, taking the eleva-tor, taking the escalator, and walking stairs. These activities takeplace when a user walks at some special points in the building,like corners, elevators, escalators, and stairs. The special pointsform an indoor road network. In our approach, we first detectthe user’s activities using the built-in sensors in a smartphone. Thedetected activities constitute the activity sequence. Meanwhile, theuser’s trajectory is reckoned by Pedestrian Dead Reckoning (PDR).Based on the detected activity sequence and reckoned trajectory,we realize pedestrian localization by matching them to the indoorroad network using a Hidden Markov Model. After encounteringseveral special points, the location of the user would converge on thetrue one. We evaluate our proposed approach using smartphonesin two buildings: an office building and a shopping mall. The re-sults show that the proposed approach can realize autonomouspedestrian localization even without knowing the initial point inthe environments. The mean offline localization error is about1.3 m. The results also demonstrate that the proposed approachis robust to activity detection error and PDR estimation error.

Index Terms—Activity sequence, hidden Markov model (HMM),indoor localization, smartphone.

I. INTRODUCTION

WHILE outdoor localization via global positioning system(GPS) is widely used, indoor localization remains a

challenge due to the limited visibility of GPS satellites. Peoplespend the majority of time indoors [1], which enables indoorpedestrian localization to become a key technique in location-based services.

Manuscript received January 20, 2014; revised September 14, 2014;accepted November 1, 2014. This work was supported by ShenzhenDedicated Funding of Strategic Emerging Industry Development Program(JCYJ20121019111128765), Shenzhen Scientific Research and DevelopmentFunding Program (ZDSY20121019111146499, JSGG20121026111056204,JCYJ20120817163755063, JCYJ20140418095735587), Major State Basic Re-search Development Program (2010CB732100), National Natural ScienceFoundation of China (41201483, 41301511, 41401444), China Postdoctoral Sci-ence Foundation (2013M542199, 2014M560671), Navinfo Innovation FundingProgram. This paper was recommended by Associate Editor D. Monekosso.(Corresponding author: Q. Li and Q. Mao)

B. Zhou, Q. Li, and Q. Mao are with the Department of State Key Labora-tory of Information Engineering in Surveying, Mapping, and Remote Sensing,Wuhan University, Wuhan 430079, China (e-mail: [email protected]; [email protected]).

Q. Li, W. Tu, and X. Zhang are with the Department of ShenzhenKey Laboratory of Spatial Smart Sensing and Services, Shenzhen Univer-sity, Shenzhen 518060, China (e-mail: [email protected]; [email protected];[email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/THMS.2014.2368092

Many indoor localization methods are based on wireless radiofacility, such as WiFi [2], radio-frequency identification (RFID)[3], Bluetooth [4], and Ultrawide Band (UWB) [5]. These local-ization methods can be categorized into two types: triangulationand fingerprinting [6]. The former relies on installed expensivehardware, making it neither scalable nor universal. The latterrequires pretraining, which is time-consuming.

In addition to wireless radio-based methods, dead reckoning(DR) techniques relying on inertial sensors are another way forindoor localization. These methods derive the current locationby adding the estimated displacement to the previous estimatedone. The biggest advantage of DR method is independence fromexternal infrastructure. DR techniques, widely used for pedes-trian localization, known as Pedestrian Dead Reckoning (PDR)[7], leverage lightweight and inexpensive inertial sensors forportable devices, such as accelerometers, gyroscopes, and mag-netometers. The devices for PDR include wearable IMU [8],tablet PC, and smartphone [9]. The principle of PDR is integrat-ing inertial sensor measurements over time; therefore, its majordrawback is that even small errors in inertial sensors will bemagnified by integration [7].

Several solutions have been proposed to prevent the accumu-lative errors of PDR [10]–[12]. One approach Activity-basedMap Matching (AMM) uses activity-related locations as virtuallandmarks to eliminate the accumulation of errors [11]–[13].For example, when a user takes the elevator, there would bean overweight/weightlessness moment and another subsequentweightlessness/overweight moment. These two moments canbe detected by the accelerometer, and then, the location of theelevator could be used as the virtual landmark. With the built-inMEMS inertial sensors, smartphones can be considered as pri-mary motion capture sensors, and human activity detection (AD)algorithms based on smartphone have been proposed [14]–[21],which makes AMM a promising method for pedestrian indoorlocalization.

The AMM comprises two basic modules: AD and MapMatching (MM) [22]. The function of the AD module is todetect what a person is doing at a particular instant, such asusing an elevator, turning at a corner, or walking upstairs. TheMM module identifies the special point on the map where theuser is passing based on the detected activity and then matchesthe estimated position of PDR to the location of the identifiedspecial point. Both modules may cause errors. The AD maymiss detecting an activity when the activity actually takes place,and it may confuse two different activities and incorrectly de-tect one when actually the other has taken place. With respect toMM, the exact location of the user in a large indoor environment

2168-2291 © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications standards/publications/rights/index.html for more information.


2 IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS

cannot be determined by the detected activity since there maybe more than one special point with the same activity feature.Another neglected factor of the current AMM approaches isthe constraint imposed by the topology of the indoor map. Forexample, a user cannot walk through a wall or other barriersmarked on the map.

In this paper, we propose a novel activity sequence-basedindoor pedestrian localization approach using smartphones.1

To the best of our knowledge, this paper is the first that usesactivity sequence for indoor pedestrian localization. The ac-tivity sequence consists of several consecutive activities whenthe pedestrian passes the special points of a building. The ap-proach realizes pedestrian localization by matching the activitysequence to several special points using Hidden Markov Model(HMM). The proposed approach can realize autonomous local-ization based on PDR even without knowing the initial point.The main contribution is to propose the activity sequence-basedmap matching (ASMM) model and the ASMM model-basedlocalization approach that takes into account the inertial sensorserror and AD accuracy and is robust to a certain degree of error.

The remainder of this paper is organized as follows. Section IIreviews the related work. Section III presents the activity se-quence detection method with an example activity sequence.Section IV introduces the activity sequence-based localizationapproach. Results and analysis are in Section V. Section VI dis-cusses the proposed method. Section VII concludes the paper.

II. RELATED WORK

PDR has been applied in indoor localization [23]–[25], whichestimates position by accumulating length and heading of eachstep. The major problem with PDR is that dead-reckoned trajec-tory accuracy degrades rapidly over time [26]. Therefore, PDRcannot be used on its own for long indoor trips. Some additionalmechanisms are required to recalibrate. These approaches can beclassified into three generic categories: infrastructure assisted,AMM and indoor map assisted.

A. Infrastructure Assisted

GPS is one kind of common infrastructure for recalibratingPDR. CompACC [10] triggers periodic GPS measurements torecalibrate the user’s estimated location. Another localizationsystem using GPS as the means to recalibrate and validate thePDR technology is proposed in [27]. However, GPS is unre-liable indoors, making it inappropriate for indoor localization.Another alternative approach uses radio frequency devices asthe infrastructure to recalibrate the PDR errors. The system pro-posed in [28] uses RFID technology to recalibrate the PDR er-rors by placing RFID tags in the environment. RFID technologyis also combined with inertial navigation system techniques foraccurate pedestrian indoor navigation in [29]. In [30], a systemthat utilizes HMM to combine WiFi fingerprints localizationand DR is proposed. In [31], a constraint approach for PDR andUWB fusion is proposed, which fuses the information of PDR

1In this paper, indoor localization refers to localization in indoor publicspaces, such as office building and shopping mall.

and UWB using a constraint filter with an upper bound in thedistance between the estimated positions of both sensors.

The proposed infrastructure-assisted approaches for recali-brating PDR can improve the positioning accuracy. However,these solutions rely on infrastructure. Some infrastructure iscostly. Others, such as RFID and UWB, have not been in-stalled on smartphones. WiFi fingerprinting is time-consumingand would not work in an environment without WiFi.

B. Activity-Based Map Matching

An AMM method recalibrates a PDR system by monitoringusers’ activities and matching their activities to correspond-ing specific points. In [11], an indoor positioning approach isproposed based on a combination of Global Navigation Satel-lite System where available, combining with PDR and AMM.The matching method used in [11] is Nearest Object Matching(NOM), which matches the current estimated location to thenearest object. Inertial sensor features are used as virtual land-marks to prevent accumulation of PDR errors in UnLoc [13].The matching method in UnLoc is also based on NOM. An-other pedestrian tracking system is proposed relying on AMMin [32]. The proposed system uses HMM to estimate pedestrianlocation and uses detected corners as landmarks to correct theuser’s location. In [32], when landmarks (corners) are detected,the pedestrian location is updated with the information at themost similar landmark. The similarity between landmarks andcurrent sensor data is determined based on the distance andheading difference between each landmark and the current lo-cation. The criteria of similarity would be invalid, if the sensorerror is too large or the distance between different landmarks istoo small. Furthermore, the proposed HMM in [32] treats thecurrent location as a hidden state, regarding magnetometer andaccelerometer data at the current location as observations. Thisis different from our system, which uses HMM for activity MM.

Because of the sensor’s error, the nearest object is always notthe actual one. Therefore, these AMM approaches using NOMas a matching method would encounter mismatch problems.To analyze the mismatch probability of AMM in indoor posi-tioning, a closed-form expression for mismatch probability as afunction of PDR sensor error and proximity between two facil-ities is proposed in [22]. However, this paper only estimates themismatch probability for a given map and PDR error and doesnot take into consideration the topology of the interior. Has-san developed a performance model of PDR with activity-basedlocation updates in [33]. He demonstrated that the distance apedestrian is expected to travel before the PDR is recalibrated isthe reciprocal of the density of activity switching points in theindoor environment [33].

ActionSLAM [34] is another approach to activity-basedpedestrian localization, which iteratively builds a map of theenvironment using location-related actions (activities) as land-marks and localizes the user within this map. ActionSLAMis a novel Simultaneous Localization and Mapping (SLAM)method for pedestrian indoor tracking that makes use of body-mounted sensors. ActionSLAM is extended in [35] by intro-ducing heading drift compensation, stance detection adaptation,and ellipse landmarks. The experiments show that the improved


ZHOU et al.: ACTIVITY SEQUENCE-BASED INDOOR PEDESTRIAN LOCALIZATION USING SMARTPHONES 3

ActionSLAM is robust and capable of accurately tracking auser in daily life. SmartActionSLAM [36], another extension ofActionSLAM, uses the integrated motion sensors of the smart-phone and an optional foot-mounted inertial measurement unitto track a person. Similar to ActionSLAM, Grzonka et al.[37]incrementally determines the trajectory of a person in a 3-D envi-ronment based on motions and activities and is able to accuratelyrecover the trajectory of the person. SLAM technology has beensuccessfully used in real-time smartphone-based indoor naviga-tion [38]. These SLAM-based approaches are different fromours.

C. Indoor Map Assisted

In pedestrian localization system, the user’s trajectory is re-stricted by the indoor map and brings opportunities for indoorlocalization. One common method for localization based on mapinformation is MM, an effective means to improve the accuracyof GPS [39], WiFi [40], and GSM [41] based localization. TheseMM methods depend on the observations obtained from othermeans, e.g., GPS, WiFi, and GSM. This is different from ourproposed approach, which uses AD as the observation.

Other approaches utilize the constraint imposed by the mapfor independent indoor pedestrian localization. Woodman andHarle proposed a localization approach based on MM using par-ticle filter, which is entirely self-contained and does not rely oninfrastructure [42]. An indoor map-assisted pedestrian indoorlocalization approach is proposed in [43], where the constraintof the indoor map is used to filter out infeasible locations overtime. These indoor map-assisted localization approaches use thetopology of the map to restrict the pedestrian’s trajectory basedon particle filter. Particle filter-based approaches mainly utilizeindoor map information for localization. Differently, the pur-pose of this study is to leverage AD information for pedestrianlocalization.

III. ACTIVITY SEQUENCE

Activity sequence implies several consecutive activities whena pedestrian passes the special points of a building, such as a cor-ner, an elevator, an escalator, and a stair, where the pedestrian’sactivities are different from walking. These different activitiescan be detected using AD techniques based on the readings ofthe built-in sensors in a smartphone.

A. Activity Detection

Here, we restrict ourselves to structured environments suchas office buildings where there are many specific points wherepedestrians complete different activities. These activities can bedetected using the built-in sensors in smartphones [14]–[21].In this paper, five types of activities are considered: turningat a corner (normal turn), turning around (U-Turn), taking theelevator, taking the escalator, and walking on the stairs. Someactivities only occur at the specific points, which are called“location-related activity” (including turn,2 taking the elevator,

2Generally, a turn would occur anywhere; in this paper, only the sharp turnis considered, since it usually occurs at a corner.

taking the escalator, and walking stairs). Others would occuranywhere, which are called “location-unrelated activity,” suchas U-Turn.

All activities can be detected by the built-in sensors of thesmartphone. Taking the elevator, taking the escalator, and walk-ing on the stairs can be detected using the accelerometer andbarometer as pressure changes with altitude, and the accelera-tion patterns of these activities are different. Normal turn andU-Turn can be detected by the gyroscope and digital compass.

The decision tree for AD and signal features of each activityis shown in Fig. 1. The top level classifies the activities intowalking and nonwalking based on the standard deviation ofacceleration (STD ACC). Walking activity includes walking onthe stairs (down and up) and walking normally (on flat ground).Nonwalking activity includes taking the elevator (down and up),taking the escalator (down and up), and keeping still. STD ACCis calculated over a sliding window of size STDwin . A threshold(STD TH) is used to categorize the activity: If STD ACC >STD TH, the activity belongs to walking; otherwise, it belongsto nonwalking. The STDwin is set to 0.8 s, and STD TH is setto 0.5 [44].

The second level divides these two types of activities intopressure changed activities and pressure unchanged activitiesbased on the pressure (Pre.) value measured by the barometer.

1) For walking activities, if the pressure changed, the activityis detected as walking stairs (if the pressure increases, itis walking downstairs; otherwise, it is walking upstairs).Otherwise, it is walking normally.

2) For nonwalking activities, if the pressure changed, theactivity is detected as taking the elevator or taking theescalator (if the pressure increases, it is down; otherwise,it is up). To distinguish taking the elevator and taking theescalator, the unique acceleration pattern of taking the el-evator is used as the feature. The elevator pattern is causedby the elevator usage, including an overweight period anda weightless period (see Fig. 1). By detecting the over-weight and weightless patterns, taking the elevator can bedifferentiated from taking escalator [45]. If the pressuredoes not change, the activity is detected as keeping still.Keeping still is not considered in this paper.

During the walking process, turns are common activities,including normal turn and U-Turn. Normal turn means turn at acorner, and U-Turn means turn around. Fig. 2 shows the changeof the heading and angular velocity when a pedestrian makesa normal turn and U-Turn. The heading value is measured bythe digital compass, and angular velocity is measured by thegyroscope. The values of these two sensors change dramaticallyat the corner. In this paper, the angular velocity measured bygyroscope is used for turn detection, and the heading valuemeasured by digital compass is used to differentiate normalturn or U-Turn.

When a pedestrian turns, the turning axis is along the direc-tion of gravity. Hence, angular velocity around the direction ofgravity will be generated, detected by the gyroscope. Generally,the acceleration generated when a pedestrian walks normallyis much less than gravity. Therefore, the angular velocity ofthe direction of maximum acceleration can be used to reflect



Fig. 1. Decision tree for AD and signal feature (acceleration and pressure) of each activity.

Fig. 2. Change of heading and angular velocity when a pedestrian makes turnand U-Turn.

that of the direction of gravity.3 The direction of the maximumacceleration can be obtained by the following equation [32]:

axismax = arg max(accx , accy , accz ) (1)

where axismax is the axis of the maximum acceleration, accx ,accy , and accz are namely the acceleration of x-, y-, and z-axis.

Turn is detected using the peak detection algorithm proposedin [46], and the threshold is set to 50 in this paper. The peakdetection algorithm is used to find the local maximum or mini-mum during a period of time [46]. The impact of the thresholdto the turn detection result is detailed in [32].

When a turn is detected, there are two possibilities: normalturn and U-turn. The difference between normal turn and U-Turnis the heading change value (see Fig. 2). Therefore, the heading

3If the angular velocity around direction of gravity was used to calculatethe heading change magnitude, the angular velocity measured of the direc-tion of maximum acceleration should be transformed from local coordinate toglobal coordinate system by multiplying the rotation matrix. However, duringour experiments, we detect normal turn and U-Turn using the peak detectionalgorithm to find the maximum angular velocity rather than using the headingchange magnitude. Therefore, we did not use the coordinate transformation.

change measured by the digital compass is used to distinguishthese two activities:{

(normal)Turn, if ΔH < H TH

U-Turn, otherwise(2)

where ΔH = abs(mean(H(T − twin : T )) − mean(H(T :T + twin))), H is the heading measured by the digital compass,T is the turning moment, twin is the time window, which isset to 1.5, mean(H(T − twin : T )) is the average headingvalue between T − twin and T , H TH is the threshold fordistinguish normal turn (called turn) and U-Turn, which is setto 135◦ based on the experiments.

B. Example of Activity Sequence

An activity sequence consists of several activities in chrono-logical order. These activities can be detected by the smartphonecarried with the pedestrian. An example activity sequence isshown in Fig. 3. To eliminate the influence of the noise causedby the jitter of the human body, a Butterworth filter of order 4is used, with a cutoff frequency of 10 Hz. In Fig. 3, the activitysequence includes seven turns, walking down the stair, takingthe elevator up, and a U-turn. Based on the detected activities,the pedestrian’s position can be determined by matching theseactivities to the corresponding special points.

IV. ACTIVITY SEQUENCE-BASED LOCALIZATION

To eliminate the accumulation of PDR errors, the proposedapproach utilizes MM method to find the most likely sequenceof special points based on the detected activity sequence. TheHMM is used as the MM algorithm. We next introduce PDRand the definition of Indoor Road Network.

A. Pedestrian Dead Reckoning

PDR is a pedestrian localization scheme that derives the cur-rent location by adding the estimated displacement to the pre-vious one. The displacement is obtained from the informationof step count and heading. If the previous location is (x, y), the



Fig. 3. Example of an activity sequence.

Fig. 4. Step detection result.

next location is calculated as

(x + sl · sc · cos(h), y + sl · sc · sin(h)) (3)

where sl stands for the step length, sc the step count, and h theheading. Step count is obtained by the peak detection algorithmin [46]. Before peak detection, the raw acceleration data shouldbe preprocessed to filter out irrelevant data. For filtering, a But-terworth low pass of order 4 is used, with a cutoff frequency of10 Hz [46]. The step detection result is shown in Fig. 4. Theheading is measured by the compass in the smartphone. Thestep length is set to a default value added with a random error[30], [43].

B. Indoor Road Network

In this paper, each special point where the pedestrian wouldexecute different activities other than walking is defined as the

Fig. 5. Node example in the indoor road network.

node. An indoor road network consists of all nodes. The nodes inan office building mainly include corners, elevators, escalators,and stairs. The node attribute is defined as follows:

1) coordinate, coordinate of the node;2) neighbor nodes;3) accessible direction (AD);4) accessible distance of corresponding accessible direction

(ADCAD);5) node type (NT).Fig. 5 is an example of the node; the attribute of node 2 is

(x2 , y2); {1, 3}; {E,S,W,N}; {dE , dS , dW , dN }; Corner.

C. Hidden Markov Model

HMM is used to match the activity sequence to the specialpoints of the indoor map, that is, the node of the Indoor RoadNetwork (called node). In this section, we introduce HMM foractivity sequence-based localization. The HMM is representedby a finite set of states, each of which is associated with a prob-ability distribution. Transitions among the states are determined



Fig. 6. Example of indoor road network and corresponding transitionprobabilities.

by a set of transition probabilities. In a specific state, an outcomeor observation can be generated by the associated probabilitydistribution. The state is not directly observable to an externalobserver [47].

An HMM can be represented as λ = (S, V,A,B, π), where1) S = (S1 , S2 , . . . , SN ) is the set of possible states, N is

the number of states in the model;2) V = (v1 , v2 , . . . , vM ) is the set of observations from the

model, M is the number of distinct observation symbolsper state;

3) A = {aij} is the state transition probability distribution,aij = pr {qt+1 = Sj |qt = Si} , 1 ≤ i, j ≤ N , where qt

denotes the state at time t;4) B = {bj (k)} is the observation probability distribution

in each of the states, bj (k) = pr {vk at t|qt = Sj} , 1 ≤j ≤ N, 1 ≤ k ≤ M ;

5) π = {πi} is the initial state distribution, πi =pr {q1 = Si} , 1 ≤ i ≤ N .

Therefore, under a sequence of observations O =(O1 , O2 , . . . , OT ) where each observation Oi ∈ V, 1 ≤ i ≤ Tand T is a system parameter, we want to find the most proba-ble sequence of states Q = (q1 , q2 , . . . , qT ), where qi ∈ S, 1 ≤i ≤ T .

We present our HMM as follows:1) Hidden States: The hidden states in our HMM are nodes

of the Indoor Road Networks. The node is defined as the specialpoints that would make pedestrian complete different activitiesother than walking. The node attribute includes coordinate andtype, such as corner and elevator.

2) Observations: There are two observations in our HMM.The first is the displacement traveled during two consecutiveactivity moments. The second is the AD result using the ADalgorithm.

3) Transition Probabilities: A transition between hiddenstates is signaled when an activity is detected. To estimate thetransition matrix, the indoor road network structure is utilized.Since a pedestrian can only move between adjacent nodes, andeach state represents a node, the transition probability is as-sumed to be uniform over all neighbors of a given node. Anexample for transition probability estimation is in Fig. 6.

4) Emission Probabilities: The emission probability de-scribes the observation probability distribution at each hiddenstate. Due to the two observations in our HMM, namely posi-tion and activity type, the emission probability consists of twoparts: position emission probability and activity type emission

Fig. 7. Schematic diagram for position emission probability estimation.

probability. As these two observations are independent, theemission probability can be defined as

p (zt ,mt |ri) = p (zt |ri) · p (mt |ri) (4)

where p (zt |ri) is the position emission probability, which de-scribes the probability distribution of position observation in aspecific hidden state. p (mt |ri) is the activity detection emis-sion probability, which describes the probability distribution ofan activity type given a specific hidden state.

According to the principle of PDR, position error is producedby distance estimation error and angle estimation error. There-fore, p (zt |ri) is made up of two parts: distance observationprobability distribution and angle observation probability dis-tribution. Here, these two probability distributions are assumedto be Gaussian distributions [30], [43]. Since distance measure-ment and angle measurement are independent, the observationprobability distribution is defined as

p (zt |ri) = p (dt |di) · p (φt |φi) =1√

2πσd

e− 1

2 σ 2d

(dt −di )2

· 1√2πσφ

e− 1

2 σ 2φ

(φt −φi )2

. (5)

Here, σd is the standard deviation of the measured distance,and σφ is the standard deviation of the measured angle. Basedon the distance calculation method of PDR, the distance is indirect proportion to step length; therefore, σd is equal to thestandard deviation of step length. dt is the distance betweenobservation and the last matched (determined) state. di is thedistance between the i-th state and the last matched (determined)state. φt is the intersection angle between �dt and �di , as shownin Fig. 7.

p (mt |ri) describes the probability of correct AD for a givenhidden state, which is also known as AD confusion matrix.

5) Initial State Distribution: When the first activity is de-tected, based on the activity type, the initial state distributionis uniform in all candidate nodes. If the start point is unknown,the candidate nodes are the nodes with the same type in theenvironment; otherwise, the candidate nodes are selected fromthe neighboring nodes of the start point.

6) Viterbi Algorithm: The Viterbi algorithm is adoptedto search for the most probable sequence of hidden statesQ = (q1 , q2 , . . . , qT ) for the given observation sequence O =(O1 , O2 , . . . , OT ), a Viterbi variable is defined as

δt+1 (j) =[max

i(δt (i) · aij )

]· bj (Ot+1) , 1 ≤ t ≤ T (6)



where δt (j) is the highest probability along a single path, atstate t, aij is the state transition probability from i to j, andbj (Ot+1) is the observation probability at state j. To get themost probable state, ϕt+1 (j) is defined as

δt+1 (j) = arg max (δt(i) · aij ), 1 ≤ t ≤ T (7)

D. Localization Scheme

Given the detected activity sequence, our approach aims tofind all nodes where the user completes the activities in theactivity sequence. The nodes are named “Node Chain” corre-sponding to the detected activity sequence. During the processof the proposed HMM algorithm, if the number of states is toosmall, the Node Chain with the highest probability is not alwaysthe correct one. Therefore, we adopt a novel method by calcu-lating the probability of every NodeChain candidate using thefollowing equation modified from (6):

pt+1 (j) = pt (i) · aij · bj (Ot+1) , 1 ≤ t ≤ T (8)

where pt (j) is the probability of a NodeChain candidate at statet. We adopt the following criteria to select the correct NodeChainfrom the candidates:

phighest/psecondhighest = C (9)

where phighest means the highest probability of the NodeChaincandidate, psecondhighest means the second highest probability,and C is a constant, set to 4 herein.

After the correct NodeChain is determined by (9), the user’slocation is determined by matching the estimation locationof each activity in the activity sequence to the determinedNodeChain. The subsequent location can be derived by PDRusing the determined node as the starting point. The bias of thesmartphone sensors can be inferred from the previous localiza-tion process. The step length of the user can be estimated by thedetected step number and the distance between the nodes of thedetermined NodeChain.

To estimate the user’s location during the walking process(online localization), (10) is used

pest =N∑

i=1

(pi · pri) (10)

where pest is the position estimated by the proposed scheme,pi is the position estimated by every NodeChain candidate, priis the probability of each NodeChain candidate, and N is thenumber of the NodeChain candidates.

E. System

Our approach is summarized with the pseudocode in Algo-rithm 1. Given the indoor road network and the sensor readings,we first detect the current activity using the AD approach (seeline 1). If the activity is a step, we update the position changedafter the last activity and calculate the distance traveled after thelast activity. The current heading is estimated by averaging theheading data from the detection moment of the last activity andcurrent moment. Based on the distance between the last activityand current heading, the point chain is updated according to the

Algorithm 1: Systeminput: Indoor Road Network of the building: IRN

input: Sensor readings up to current time t: d1:t

output: The activity sequence consisted of all detectedactivities: ActivitySequence.output: The key point chain consisted of all chaincandidates: PointChain.definition: nchain=number of chain candidates in thePointChain

definition: nactiv ity=number of location related activitiesin the ActivitySequence

1: Γ=detectCurrentActivities(d1:t )2: if stepActivity ∈ Γ then3: (Δx,Δy)=estimatePositionChangeAfterLastActivity

(dt)4: DistanceTraveledAfterLastActivity=‖Δx,Δy‖5: DTALA=DistanceTraveledAfterLastActivity //for

better readability6: CurrentHeading=avg(heading(tlastAcitiv ity : t)))7: CH=CurrentHeading //for better readability8: if nactiv ity > 0 then9: for i=1:nchain do10: if DTALA · c > ADCAD || CH �= AD

then11: delete PointChain(i)12: end if13: end for14: end if15: end if16: if location_related_activity ∈ Γ then17: AT=ActivityType //for better readability18: if nactiv ity== 1 then //first detect activity19: point_candidate=getInitialPoint(DTALA, CH ,

AT )20: pr_point_candidate=calculatePointCandidate

Probabilities21: addPointtoPointChain(point_candidate,pr_point_

candidate)22: else //detect more than one activity23: for i=1:nchain do24: Neightbors_of_last_points=getNeighbors

(PointChain{i})25: for j=1:length(Neighbors) do26: pr_Neightbors=calculateNeighbor

Probabilities(IRN ,d1:t)27: addNeighbortoPointChain(Neightbors,

pr_Neightbors)28: end for29: end for30: end if31: end if



TABLE ICONFUSION MATRIX OF AD

Turn U-Turn Elevator Stair Escalator

Turn 100% 0 0 0 0U-Turn 0 100% 0 0 0Elevator 0 0 100% 0 0Stair 0 0 0 100% 0Escalator 0 0 0 0 100%

constraint of indoor road networks. If the DTALA is greater thanthe ADCAD of the last node (a constant c is used as tolerancefor the PDR error, which is set to 0.5 here), or the CH is notequal to the AD of the last node, the NodeChain candidate isdeleted (see lines 2–15). As per Section IV-C, if the locationrelated activity is detected, and the activity is the first detectedone, the initial node candidates would be obtained based on theDTALA, CH, and AT. The probability of each node is calcu-lated as per Section IV-C5). Then, the node candidates with theprobabilities are added to the NodeChain (see lines 16–21). Ifthe activity is not the first detected one, the neighbors of the tailof each chain candidate (last node added when last activity isdetected) in the NodeChain is obtained as per Section IV-C5).The probability of each neighbor of the last node is calculatedas per Section IV-C6). Similarly, the point candidates with theprobabilities are added to the NodeChain (see lines 22–27).

V. EVALUATION

A. Activity Detection Performance Proof of Concept

To evaluate the AD method, a pilot study is conducted. Alldata were collected using an Android version 4.1.1 Galaxy IIIsmartphone, including accelerometer, gyroscope, magnetome-ter, and barometer data. The sampling frequency was set to100 Hz during data collection. Four participants (two femalesand two males) were asked to complete five activities, accord-ing to [18]. Each participant held the smartphone in a hand infront of the body. The sample size of each activity was 20 tracesfor each activity. For Turn and U-Turn, participants first walkedabout ten steps, made a turn (U-Turn), and then walked anotherten steps. For the elevator, stairs, and escalator, data collectionbegan and ended at two end points of the activity. For the eleva-tor, we collected data for different floors (first floor to the 14thfloor), since elevators are stopped by other users in the building.

The AD accuracy is calculated using the following equation:

Accuracy =Ti

Ni· 100% (11)

where Ti is the number of the activities that were correctlydetected of the i-th-type activities, Ni is the number of all thei-th-type activities.

The AD result is shown in Table I. The activity method fora natural track of one participant is shown in Fig. 3. The resultshows that the activities can be detected accurately based on theproposed AD approach using a smartphone.

Fig. 8. Experiment environments. (a) Office building. (b) Shopping mall.

B. Activity Sequence-Based Localization

1) Experiment Setup: To evaluate the overall system perfor-mance in real-world environments, we performed experimentsin two buildings: an office building, with a 52.5 m × 52.5 mfloor plan, and a shopping mall, with a 80 m × 60 m floor plan,as shown in Fig. 8. The proposed system was implemented onthe Android platform using the Galaxy S III smartphone, withan accelerometer, a gyroscope, a magnetometer, and a barome-ter. The participants were asked to walk along six representativeroutes at constant speed with the smartphone in the hand. Eachroute was repeated ten times by four participants (two males andtwo females). Route #1, Route #2, Route #3, and Route #4 are inthe office building; Route #5 and Route #6 are in the shoppingmall:

1) Route #1 starts from an arbitrary position of the corridor,passes several corners, and arrives at one seat in the office.In this case, the start point is unknown. The traditionalPDR scheme cannot work in this case.

2) Route #2 starts from a stair and passes an open area aroundthe elevator. Route #2 includes an open area, where theconstraint is poor.

3) Route #3 starts from an elevator and includes a U-Turnactivity. It is used to verify the impact of location unrelatedactivity.

4) Route #4 starts with waking the elevator up to the floor ofan office, walking to the office, sitting down for a periodof time, and walking to the wash basin.

5) Route #5 and Route #6 are two long routes in the shoppingmall: Route #5 starts from an elevator and Route #6 froman escalator.

To collect ground truth data, some markers with known coor-dinates were set along with the routes. When a user walks past amarker, another participant would record the time using anothersmartphone, which is synchronized with the smartphone used aslocalization device. Between two markers (the distance is about10 m), the ground truth is obtained by interpolating using stepcount.

The online localization error is obtained by calculating the Eu-clidean distance between the estimated position and the groundtruth. For offline localization, the error is calculated as follows:

Error =∑N

i=1 |pei , pgi |N

(12)



Fig. 9. Online localization error results for each route. (a) Route #1. (b) Route #2. (c) Route #3. (d) Route #4. (e) Route #5. (f) Route #6.

where N is the number of the ground truth, pei is the i-thestimated position, pgi is the position of the of the i-th groundtruth, |pei, pgi | is the Euclidean distance of pei and pgi .

The standard deviation of step length estimation σd is setto 0.1. The standard deviation of measured heading σφ is setto 10◦.

2) Online Localization Performance: Online localization re-sults of all routes are shown in Fig. 9 for the proposed approach,the proposed approach with known initial point, and the pro-posed approach with known initial activity. Without initial ac-tivity, at the beginning, the average error is high. This is becausethe initial location is unknown, and the initial position is as-sumed as a uniform distribution. With increasing step number,the localization error decreases gradually. As the number of en-countered activities also increases, after passing a number ofsteps, the NodeChain consists of the passed nodes determinedby the proposed approach (except Route #3), and the location isalso determined. For Route #3, if the initial activity is unknown,the trace cannot be determined because the number of activi-ties is insufficient; Fig. 8 shows there are only three turns inRoute #3.

If the initial activity is known, the localization error decreasesfaster, as seen in Fig. 9 (there is no initial activity in Route #1).For example, for Route #2, if the initial activity is unknown, thelocalization error decreases after about 40 steps; if the initialactivity is known, it only needs about ten steps for localizationerror decreasing. This is because the initial activity is special,in Route #2, it is taking the stairs; in Route #3, #4, and #5, it istaking the elevator; in Route #6, it is taking the escalator. Thenumber of these three activity-related nodes is much smaller

than that of the turn. From Fig. 9(f), the location is immedi-ately determined when the initial activity is detected. This isbecause there is only one up escalator in the shopping mall.Using the special activity-related nodes would help to improvethe convergence speed. The fewer the number of activity-relatednodes, the faster is the convergence speed. If the initial point isknown, based on the AD result, the accumulative error can beeliminated by matching the estimated position of the PDR to thecorresponding activity point.

There are some special cases in the routes. In Route #2, thereis an open area, where turns cannot be detected. In Route #3,there is a U-Turn activity, which is location-unrelated. In Route#4, there is a period of sitting still. The results in Fig. 9 revealthat these cases were addressed.

3) Convergence: Distance traveled before converging to aunique activity chain reflects the convergence speed. The greaterthe traveled distance, the slower is the convergence speed.Fig. 10 shows the distance traveled of the different routesuntil the algorithm converges, with and without initial activ-ity. Mostly, with initial activity, the traveled distance is muchshorter than without initial activity. Fig. 10 shows that by usingAD information, the converge speed on the true location wouldincrease.

4) Performance Versus Activity Detection Accuracy: To an-alyze the influence of AD accuracy and inertial sensor errorto the activity sequence matching result, Route #2 is taken asan example. In Route #2, there are seven activities, includingwalking upstairs, and six turns. We suppose that walking up-stairs would not be detected accurately. In fact, if the barometeris not used (there is no built-in barometer in some smartphones),



Fig. 10. Distance traveled before converging to unique activity chain. Route#1 does not include initial activity, ∞ means Route #3 cannot be converged,0 means Route #6 can be immediately converged when the initial activity isdetected.

Fig. 11. Matching accuracy as a function of AD accuracy. (a) Step lengtherror. (b) Heading error.

it would be difficult to distinguish walking stairs from walkingnormally. Fig. 11 shows the activity sequence matching resultas a function of activity (walking upstairs) detection accuracywith different inertial sensors error, expressed by the standarddeviation of step length and heading. The matching accuracy iscalculated after passing four activities.

From Fig. 11(a), when step length estimation error is small,the matching accuracy is not influenced by the AD accuracy.When σd = 0.1, the matching accuracy is near 100, and the ADaccuracy is 0. With increases in step length estimation error, theinfluence of AD accuracy to the matching result is enhancedgreatly. The same trend is shown in Fig. 11(b), reflecting theinfluence of heading error. If the sensor error is small, the activitysequence can match the point well only using turning activity.If the sensor error is large, without the walking upstairs activity,the matching accuracy is low. Fig. 11 shows that activity witha high degree of uniqueness (walking upstairs) is beneficial toactivity sequence matching.

From Fig. 11, the proposed approach is robust to a certaindegree of inertial sensors and AD error. From Fig. 11(a), if theAD accuracy is 100%, the matching accuracy is more than 60%

when the standard deviation of step length estimation changesfrom 0.1 to 0.5. As a result of AD error, if σd = 0.1, the match-ing accuracy is near 100% even if the AD accuracy is 0. Theinfluence of heading error and AD accuracy on matching accu-racy is similar to that of step length error, which can be seenfrom Fig. 11(b).

5) Offline Localization (Tracking) Performance: The offlinelocalization result is obtained by matching the activity posi-tion to the NodeChain determined by the proposed approach inSection IV. If the initial position is unknown (Route #1), thetrace before first activity is derived retrospectively from the po-sition of the first activity. The tracking trajectory is shown inFig. 12. The proposed approach tracked pedestrian’s trajectoryaccurately in the experiment environments. The outcome of theexperiments is summarized in Table II (tracking error is themean of ten trials), and the mean location error of the offlinelocalization is about 1.3 m.

VI. DISCUSSION

In contrast with our study, WiFi fingerprinting-based localiza-tion requires precalibration of the fingerprints, which is labor-intensive and time-consuming [2], [6]. Although autonomousfingerprinting construction approaches are proposed [48], thelocalization performance based on the autonomous constructedfingerprints is poor. Moreover, WiFi fingerprinting-based ap-proaches rely on WiFi access points. Our approach does not relyon any infrastructure, which can realize autonomous pedestrianlocalization.

We discuss our approach as compared with two state-of-the-art calibration-free localization systems: UnLoc [13] and Zee[43]. UnLoc [13] proposed the idea to use inertial sensor fea-tures as virtual landmarks to prevent accumulation of PDR er-rors. However, UnLoc does not consider the ambiguity of thevirtual landmark; if there are more than one virtual landmarkwith the same inertial feature, UnLoc would encounter the mis-matching problem. Our proposed approach can avoid the mis-matching problem by using the ASMM model. Zee [43] is anindoor map-assisted localization approach which leverages thetopology of the map to restrict pedestrian’s trajectory based on aparticle filter. However, it is known that particle filtering is time-consuming, which may be not suitable for online localizationbased on a smartphone.

A. Limitations

The proposed approach is based on the assumption that alllocation-related activities take place at the nodes of the indoorroad network. All the nodes are labeled with coordinates. Onlythe labeled nodes are considered during the localization pro-cess. Actually, some activities may take place away from la-beled nodes. Our system detects the activity but cannot correctlymatch the location. This is a limitation of this study, and we arecurrently investigating methods to address it.

The proposed approach works well for structured indoor en-vironments. In these buildings, there are many specific pointswhere pedestrians would complete different activities. More-over, the pedestrian’s trajectory is restricted by the indoor road



Fig. 12. Offline localization results. (a) Route #1. (b) Route #2. (c) Route #3. (d) Route #4. (e) Route #5. (f) Route #6.

TABLE IIEVALUATION RESULTS

Activity No.

Route No. Route Length (m) Detected Undetected Location-unrelated Tracking Error (m)

1 124.50 6 0 0 0.9322 106.70 6 2 0 1.1233 73.25 5 0 1 1.0124 84.18 13 0 1 1.2355 161.40 7 0 0 1.8976 104.50 6 0 0 1.581

network in these buildings. The future work needs to addresspedestrian localization in open indoor spaces (e.g., lobby).

In this paper, step length and heading errors are assumed tobe Gaussian distributions [30], [43]. A more realistic PDR errormodel should be considered.

VII. CONCLUSION

This paper proposed a novel activity sequence-based pedes-trian indoor localization approach using smartphones. Theactivity sequence is first detected using an AD algorithm. Then,the HMM is used to match the activities in the activity se-quence to the corresponding nodes of the indoor road network.During the matching process, the constraint of the indoor roadnetwork is also taken into account. By the activity sequence-based MM, the proposed approach can realize pedestrian local-ization even without knowing the starting point in the structuredenvironments. The performance of the proposed approach hasbeen evaluated by experiments in two structured indoor envi-ronments. The results show that the proposed pedestrian indoorlocalization approach can work in these environments usingsmartphones.

ACKNOWLEDGMENT

The authors would like to thank Danli Li, the editors and theanonymous reviewers for their constructive comments.

REFERENCES

[1] Y. Chon, E. Talipov, and H. Cha, “Autonomous management of everydayplaces for a personalized location provider,” IEEE Trans. Syst., Man,Cybern., C, Appl. Rev., vol. 42, no. 4, pp. 518–531, Jul. 2012.

[2] P. Bahl and V. N. Padmanabhan, “Radar: An in-building RF-based user lo-cation and tracking system,” in Proc. 9th Annu. Joint Conf. IEEE Comput.Commun. Soc., 2000, vol. 2, pp. 775–784.

[3] L. M. Ni, Y. Liu, Y. C. Lau, and A. P. Patil, “Landmarc: Indoor locationsensing using active RFID,” Wireless Netw., vol. 10, no. 6, pp. 701–710,2004.

[4] A. Kotanen, M. Hannikainen, H. Leppakoski, and T. D. Hamalainen,“Experiments on local positioning with bluetooth,” in Proc. Int.Conf. Inf. Technol., Coding Comput. [Comput. Commun.]., 2003,pp. 297–303.

[5] R. J. Fontana and S. J. Gunderson, “Ultra-wideband precision asset lo-cation system,” in Proc. IEEE Conf. Ultra Wideband Syst. Technol. Dig.Papers, 2002, pp. 147–150.

[6] H. Liu, H. Darabi, P. Banerjee, and J. Liu, “Survey of wireless indoorpositioning techniques and systems,” IEEE Trans. Syst., Man, Cybern., C,Appl. Rev., vol. 37, no. 6, pp. 1067–1080, Nov. 2007.

[7] S. Beauregard and H. Haas, “Pedestrian dead reckoning: A basis for per-sonal positioning,” in Proc. 3rd Workshop Positioning, Navigat. Commun.,2006, pp. 27–35.



[8] J. Bird and D. Arden, “Indoor navigation with foot-mounted strapdowninertial navigation and magnetic sensors [emerging opportunities for lo-calization and tracking],” Wireless Commun., vol. 18, no. 2, pp. 28–35,2011.

[9] A. R. Pratama, R. Hidayat et al., “Smartphone-based pedestrian deadreckoning as an indoor positioning system,” in Proc. Int. Conf. Syst. Eng.Technol., 2012, pp. 1–6.

[10] I. Constandache, R. R. Choudhury, and I. Rhee, “Towards mobile phonelocalization without war-driving,” in Proc. IEEE INFOCOM, 2010,pp. 1–9.

[11] D. Gusenbauer, C. Isert, and J. Krosche, “Self-contained indoor position-ing on off-the-shelf mobile devices,” in Proc. Int. Conf. Indoor PositioningIndoor Navigat., 2010, pp. 1–9.

[12] J. A. B. Link, P. Smith, N. Viol, and K. Wehrle, “Footpath: Accurate map-based indoor navigation using smartphones,” in Proc. Int. Conf. IndoorPositioning Indoor Navigat., 2011, pp. 1–8.

[13] H. Wang, S. Sen, A. Elgohary, M. Farid, M. Youssef, and R. R. Choudhury,“No need to war-drive: Unsupervised indoor localization,” in Proc. 10thInt. Conf. Mobile Syst., Appl. Services, 2012, pp. 197–210.

[14] J. R. Kwapisz, G. M. Weiss, and S. A. Moore, “Activity recognitionusing cell phone accelerometers,” ACM SIGKDD Explorations Newslett.,vol. 12, no. 2, pp. 74–82, 2011.

[15] T. Brezmes, J.-L. Gorricho, and J. Cotrina, “Activity recognition fromaccelerometer data on a mobile phone,” in Distributed Computing, Arti-ficial Intelligence, Bioinformatics, Soft Computing, and Ambient AssistedLiving. New York, NY. USA: Springer, 2009, pp. 796–799.

[16] N. Gyorbıro, A. Fabian, and G. Homanyi, “An activity recognition systemfor mobile phones,” Mobile Netw. Appl., vol. 14, no. 1, pp. 82–91, 2009.

[17] K. Ouchi and M. Doi, “Indoor-outdoor activity recognition by a smart-phone,” in Proc. ACM Conf. Ubiquitous Comput., 2012, pp. 600–601.

[18] S. Khalifa, M. Hassan, and A. Seneviratne, “Adaptive pedestrian activ-ity classification for indoor dead reckoning systems,” in Proc. Int. Conf.Indoor Positioning Indoor Navigat., 2013, pp. 1–7.

[19] S. Khalifa, M. Hassan, and A. Seneviratne, “Feature selection for floor-changing activity recognition in multi-floor pedestrian navigation,” inProc. 7th Int. Mobile Comput. Ubiquitous Netw., 2014, pp. 1–6.

[20] L. Pei, R. Guinness, R. Chen, J. Liu, H. Kuusniemi, Y. Chen, L. Chen,and J. Kaistinen, “Human behavior cognition using smartphone sensors,”Sensors, vol. 13, no. 2, pp. 1402–1424, 2013.

[21] G. Panahandeh, N. Mohammadiha, A. Leijon, and P. Handel, “Continu-ous hidden Markov model for pedestrian activity classification and gaitanalysis,” IEEE Trans. Instrum. Meas., vol. 62, no. 5, pp. 1073–1083,May 2013.

[22] S. Khalifa and M. Hassan, “Evaluating mismatch probability of activity-based map matching in indoor positioning,” in Proc. Int. Conf. IndoorPositioning Indoor Navigat., 2012, pp. 1–9.

[23] D. Alvarez, R. C. Gonzalez, A. Lopez, and J. C. Alvarez, “Comparisonof step length estimators from weareable accelerometer devices,” in Proc.28th Annu. Int. Conf. Eng. Med. Biol. Soc., 2006, pp. 5964–5967.

[24] Y. Jin, H.-S. Toh, W.-S. Soh, and W.-C. Wong, “A robust dead-reckoningpedestrian tracking system with low cost sensors,” in Proc. IEEE Int. Conf.Pervasive Comput. Commun., 2011, pp. 222–230.

[25] U. Steinhoff and B. Schiele, “Dead reckoning from the pocket-an exper-imental study,” in Proc. IEEE Int. Conf. Pervasive Comput. Commun.,2010, pp. 162–170.

[26] R. Harle, “A survey of indoor inertial positioning systems for pedestrians,”IEEE Commun. Surveys Tut., vol. 15, no. 3, pp. 1281–1293, Jul.–Sep.2013.

[27] S. Beauregard, “A helmet-mounted pedestrian dead reckoning system,” inProc. 3rd Int. Forum Appl. Wearable Comput., 2006, pp. 1–11.

[28] S. House, S. Connell, I. Milligan, D. Austin, T. L. Hayes, and P. Chiang,“Indoor localization using pedestrian dead reckoning updated with RFID-based fiducials,” in Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc.,2011, pp. 7598–7601.

[29] A. R. Jimenez Ruiz, F. Seco Granja, J. C. Prieto Honorato, and J. I. GuevaraRosas, “Accurate pedestrian indoor navigation by tightly coupling foot-mounted IMU and RFID measurements,” IEEE Trans. Instrum. Meas.,vol. 61, no. 1, pp. 178–189, Jan. 2012.

[30] J. Seitz, T. Vaupel, S. Meyer, J. G. Boronat, and J. Thielecke, “A hiddenMarkov model for pedestrian navigation,” in Proc. 7th Workshop Posi-tioning Navigat. Commun., 2010, pp. 120–127.

[31] F. Zampella, A. De Angelis, I. Skog, D. Zachariah, and A. Jimenez, “Aconstraint approach for UWB and PDR fusion,” in Proc. Int. Conf. IndoorPositioning Indoor Navigat., 2012, pp. 1–9.

[32] K. Park, H. Shin, and H. Cha, “Smartphone-based pedestrian tracking inindoor corridor environments,” Pers. Ubiquitous Comput., vol. 17, no. 2,pp. 359–370, 2013.

[33] M. Hassan, “A performance model of pedestrian dead reckoning withactivity-based location updates,” in Proc. 18th IEEE Int. Conf. Netw.,2012, pp. 64–69.

[34] M. Hardegger, D. Roggen, S. Mazilu, and G. Troster, “Actionslam: Usinglocation-related actions as landmarks in pedestrian slam,” in Proc. Int.Conf. Indoor Positioning Indoor Navigat., 2012, pp. 1–10.

[35] M. Hardegger, G. Troster, and D. Roggen, “Improved actionslam for long-term indoor tracking with wearable motion sensors,” in Proc. 17th Annu.Int. Symp. Int. Symp. Wearable Comput., 2013, pp. 1–8.

[36] M. Hardegger, S. Mazilu, D. Caraci, F. Hess, D. Roggen, and G. Troster,“Actionslam on a smartphone: At-home tracking with a fully wearablesystem,” in Proc. Int. Conf. Indoor Positioning Indoor Navigat., 2013,pp. 1–10.

[37] S. Grzonka, A. Karwath, F. Dijoux, and W. Burgard, “Activity-basedestimation of human trajectories,” IEEE Trans. Robot., vol. 28, no. 1,pp. 234–245, Feb. 2012.

[38] R. Faragher, C. Sarno, and M. Newman, “Opportunistic radio slam forindoor navigation using smartphone sensors,” in Proc. IEEE/ION PositionLocation Navigat. Symp., 2012, pp. 120–128.

[39] P. Newson and J. Krumm, “Hidden markov map matching through noiseand sparseness,” in Proc. 17th ACM SIGSPATIAL Int. Conf. AdvancesGeographic Inform. Syst., 2009, pp. 336–343.

[40] A. Thiagarajan, L. Ravindranath, K. LaCurts, S. Madden, H. Balakrishnan,S. Toledo, and J. Eriksson, “Vtrack: Accurate, energy-aware road trafficdelay estimation using mobile phones,” in Proc. 7th ACM Conf. EmbeddedNetw. Sens. Syst., 2009, pp. 85–98.

[41] H. Wang, Z. Wang, G. Shen, F. Li, S. Han, and F. Zhao, “Wheelloc:Enabling continuous location service on mobile phone for outdoor sce-narios,” in Proc. IEEE INFOCOM, 2013, pp. 2733–2741.

[42] O. Woodman and R. Harle, “Pedestrian localisation for indoor environ-ments,” in Proc. 10th Int. Conf. Ubiquitous Comput., 2008, pp. 114–123.

[43] A. Rai, K. K. Chintalapudi, V. N. Padmanabhan, and R. Sen, “Zee: Zero-effort crowdsourcing for indoor localization,” in Proc. 18th Annu. Int.Conf. Mobile Comput. Netw., 2012, pp. 293–304.

[44] A. Brajdic and R. Harle, “Walk detection and step counting on uncon-strained smartphones,” in Proc. ACM Int. Joint Conf. Pervasive UbiquitousComput., 2013, pp. 225–234.

[45] M. Alzantot and M. Youssef, “Crowdinside: Automatic construction ofindoor floorplans,” in Proc. 20th Int. Conf. Adva. Geographic Inform.Syst., 2012, pp. 99–108.

[46] M. Mladenov and M. Mock, “A step counter service for java-enableddevices using a built-in accelerometer,” in Proc. 1st Int. Workshop Context-Aware Middleware Services, Affiliated 4th Int. Conf. Commun. Syst. Softw.Middleware, 2009, pp. 1–5.

[47] L. R. Rabiner, “A tutorial on hidden Markov models and selected applica-tions in speech recognition,” in Proc. IEEE, vol. 77, no. 2, pp. 257–286,Feb. 1989.

[48] Y. Kim, Y. Chon, and H. Cha, “Smartphone-based collaborative and au-tonomous radio fingerprinting,” IEEE Trans. Syst., Man, Cybern., C, Appl.Rev., vol. 42, no. 1, pp. 112–122, Jan. 2012.

Baoding Zhou (S’14) received the B.E. degree fromSchool of Information Science and Engineering fromShandong University, Jinan, China, in 2009, and iscurrently working toward the Ph.D. degree with theState Key Laboratory of Information Engineering inSurveying, Mapping, and Remote Sensing, WuhanUniversity, Wuhan, China.

His research interests include indoor localizationand navigation, pervasive computing, and intelligenttransportation.



Qingquan Li received the Ph.D. degree in geographicinformation system and photogrammetry from theWuhan Technical University of Surveying and Map-ping, Wuhan, China, in 1998.

He is currently a Professor with Shenzhen Uni-versity, Guangdong, China and Wuhan University,Wuhan. His research areas include 3-D and dynamicdata modeling in GIS, location-based service, sur-veying engineering, integration of GIS, global posi-tioning system and remote sensing, intelligent trans-portation system, and road surface checking.

Qingzhou Mao (M’14) received the Ph.D. degreein photogrammetry and remote sensing from WuhanUniversity, Wuhan, China, in 2008.

He is an Associate Professor of Wuhan University,Wuhan, China. His main research interests includesatellite navigation system, remote sensing and geo-graphic information system (3S) integrates theory andmethod, GNSS/IMU navigation and position tech-nology, high-precision laser measurement and pointcloud data intelligent processing algorithm, patternrecognition, and vision measurement technology and

its application in mapping, road, railways and tunnels and other major projectstesting and measurement field.

Wei Tu (M’14) received the B.E. and Ph.D. degreesin computer science from Wuhan University, Wuhan,China, in 2007 and 2013, respectively.

He is currently a Postdoctoral Fellow with theShenzhen Key Laboratory of Spatial Smart Sens-ing and Service, Shenzhen University, Shenzhen,China. His research interests include spatiotempo-ral data modeling, spatiotemporal data analysis, andspatiotemporal data mining.

Xing Zhang received the B.E. and Ph.D. degrees ingeographic information science from Wuhan Univer-sity, Wuhan, China.

He is currently with the Shenzhen Key Laboratoryof Spatial Information Smart Sensing and Services,Shenzhen University, Shenzhen, China. His researchinterests include mobile navigation, visual cognition,ubiquitous computing, and intelligent transportation.

activity sequence-based indoor pedestrian localization using smartphones

Documents