Paradigm Shift in Continuous Signal Pattern Classification:Mobile Ride Assistance System for two-wheeled Mobility

Robots

Ali Boyali, Naohisa Hashimoto and Osamu MatsumotoNational Institute of Advanced Industrial Science and Technology

Tsukuba, Ibaraki, JapanE-mail: {ali-boyari}{naohisa-hashimoto}{matsumoto.o}@aist.go.jp

Abstract—In this study we describe the development ofa ride assistance application which can be implemented onthe widespread smart phones and tablet. The ride assistanceapplication has a signal processing and pattern classificationmodule which yield almost 100% recognition accuracy forreal-time signal pattern classification. We introduce a novelframework to build a training dictionary with an overwhelmingdiscriminating capacity which eliminates the need of humanintervention spotting the pattern on the training samples. Weverify the recognition accuracy of the proposed methodologiesby providing the results of another study in which the handposture and gestures are tracked and recognized for steeringa robotic wheelchair.

Keywords—Ride Assistance, Continuous Signal Pattern Clas-sification, Subspace Training, Mobility Robots

I. INTRODUCTION

Two-wheeled standing type Personal Transporters (PT),such as Segway (TM) and Toyota Winglet have been seen analternative transportation mode in the short range excursionsand gained acceptance from a broad range of users in manycountries. Driven by electric motors and having a goodtravelling range that varies between 10-38 km dependingon the vehicle type and brand, the PTs are preeminent forthe future of transportation.

Even though, the PTs have been developed for more thana decade, in many countries except USA, the use of thesevehicles on the public road related areas is prohibited bylegislation and traffic regulations [1]. The lingering contro-versy on the vehicle categorization limits the use of PTsonly within the designated areas or private properties. Theupper speed level of PTs and lack of braking and signalingequipment, insufficient number of reports and experimentalresults make the vehicle categorization impossible withinthe current regulations of the these countries confusing theregulation bodies and law makers.

On the other hand, these kind of mobility robot tech-nologies are necessary and desirable to reduce the trafficcongestions, the use of single occupancy vehicles for shortdistance trips and to increase the independent mobility ofthe people such as the elderly. The aging society havebeen the very fact that the many developed countries facetoday. The increasing population of the elderly who are65 years and older cohort in the developed countries havetremendous impact in nation’s economic vitality due to the

increasing health care and pension payment and the lackof care givers that can meet the emerging demand [2, 3].Aging processes do not only have impact on the economicvitality and social system, but also the elderly themselves.The decreasing motor and cognitive skills, the elderly areprone to social isolation, depression and poverty due withthe less transportation options to the nearest public hubs andcity amenities than the able-bodied individuals.

In this study we describe the development of an accurateand robust signal classification framework that suits for anytype of sensor domain to implement a ride assistance systemon the mobile smart-phones and tablets. The application ona smart phone or tablet which is attached to the handlebar of a PT continuously observe the sensor measurementsand classify the breaking maneuvers real-time and notifythe rider if the maneuver is dangerous while recording theride motion characteristics. The signal pattern classificationframework is not limited the breaking mode but it canbe implemented for various application domains such asclassification of lateral maneuvers or determining the roadsurface roughness as we elaborate in the proceeding sections.

The signal processing and classification module is thecore of the ride assistance system which also show thelocation and velocity information to the rider by producingwarnings and notifications on the screen. We designed acomplementary filter for sensor fusion for this purpose. Theacquired measurements from gyroscope and accelerometerare filtered out to obtain reliable orientation and motionparameters for the classification module.

We used the Block-Sparse [4, 5] and Collaborative Rep-resentation based Classification [6, 7] (B SRC and CRC)methods for the ride assistance system and compared the per-formance of classification accuracy and computation timesfor the mobile device. The success of these classificationmethods are highly dependent on the reliable training dic-tionaries with a powerful discriminating capability. Buildingsuch kind of dictionaries for the highly noisy signals requiredefining the start and end point of the training signal.However this task is highly difficult due to the fact thatin most cases the pattern boundaries are not distinct. Oneof the novelty we propose and demonstrate by this paperis building a dictionary with a high discriminating powerwithout spotting the signal pattern and using this dictionaryfor a continuous recognition of the performed maneuver.

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We organized the rest of the paper as follows. In Sec-tion II, we cogently review the mobile robot studies, thendescribe the robotic zone designated for investigation ofthe robot-society interaction. After briefly describing theconfiguration of the mobile tablet and mobility robot system,we detail on how we implement a complementary filter in aneasy way in Section III. The section IV is dedicated to theclassification systems and building training dictionary. Theexperimental results and discussion is given in the sectionV. The paper ends with the conclusion and future works inthe conclusion section.

II. PRIOR WORKS AND PROJECT DESCRIPTION

A scant number of studies in the literature investigate theinteraction of the mobility robots with the other road userson the public road related areas. The limited literature andexperimental data are not sufficient to guide the regulationauthorities and developers to make decisions and improve-ments collaboratively. Among these studies, the authors in[8] report the approaching distance the mobility robots tothe other road sharers at the various speed profiles with therecruited novice and expert riders. The stopping distance forthe different driving maneuvers, the braking and responsetime of the riders are assessed in a similar study [9]. Inthe study [10], the approaching distance of the other roadvehicles and pedestrians to the mobility robots includingSegway and robotic wheelchairs are presented. All of theexperimental studies are carried out under the controlled en-vironments with the limited resource that doesn’t contributeto sufficient statistics for the real world ride characteristics.In addition to the experimental studies, the reports [11, 12]release the opinion of the many stakeholders and institutionsas well as the subjective assessment of the recruited riders.

The crux of our motivation is to create a ride assistancesystem which analyzes the ride dynamics, lateral and longi-tudinal maneuvers as well as the rider’s behaviors and pro-duce immediate warnings when a dangerous situations arisewhile continuously gathering ride characteristics, interactionof the rider with other road sharers in the designated roboticzone (Fig. 1) to form a national database on the robot-societyinteraction.

The robotic zone in Tsukuba is the only designated zonein Japan in which the interaction of the robotic mobilitydevices with the other road sharers is investigated by variousresearch institutes. The robotic mobility device, Segway isused in the experiments, the results of which is given in thispaper. An Android tablet the screen facing up to the ridersis attached on the top of handle bar (Fig. 2) [13].

The tablet connected to Segway sharing system showsthe time to destination, speed and current location as well asthe sensor readings on the screen. The handle bar of Segwayis under continuous excitations induced by the steeringactions and road irregularities. The signal processing andclassification module is integrated to the mobile applicationto align the coordinate systems of the tablet and Segway toobtain the motion related parameters.

Fig. 1: Tsukuba Robotic Zone

Fig. 2: Segway and Android Tablet

III. SIGNAL PROCESSING AND FILTERS

The classification methods used in the study make useof the gyroscope output pitch rate and the pitch orientationof the handle bar as the most discriminating features for thebraking mode classification. The other sensor measurementssuch as that of the accelerometer are also used for other

classification and notification purposes. A rotation matrix isrequired in order to align the tablet coordinate system (Fig.3) with the motion coordinates of the robot to rotate theaccelerometer measurements subtracting the gravity vector.Since, the handle bar of Segway has only two Degrees ofFreedom (DOF), the pitch and roll rotations are the onlyinput parameters for this rotation matrix.

Fig. 3: Segway and Android Tablet

Although, Android Software Development Kit (SDK)provide access to the orientation, position and motion sensorinformation, the reported acceleration and orientation valuesare only reliable at low frequencies or when the device restsat stand still as stated in the Android SDK documents [14].In Fig. (4) the reported and filtered roll and pitch orientationare compared for an experiment in which the rider does sub-sequent sudden breaking maneuvers where high frequenciesprevail. As seen in the figure, the reported orientation anglesare very noisy with respect to the measurements obtained bya complementary filter.

Fig. 4: Reported and Filtered Orientation Angles

If the reported orientation angles are used for the rotationmatrix, the noise factor of the accelerometer measurements

to be rotated are amplified by this transformation. Thus, weapply a low pass, third order Butterworth filter to reducethe noise on the acceleration values which are then reservedfor other classification purposes such as road surface roadsurface detection.

The most discriminating features are the pitch rate andorientation angle which is obtained by a complementaryfilter for the breaking mode classification addressed in thispaper. A complementary filter sums the transfer functions ofmeasurement processes which are mathematically comple-ment to each other [15]. Micro Electro-Mechanical Sensors(MEMS) integrated on the mobile computing devices haveadvantages and disadvantages at certain frequency intervals.Gyroscope measurements have low noise level at the higherfrequencies while the accelerometer component give rela-tively reliable measurements at the lower frequencies. As avariant of the steady state Kalman filter, a complementaryfilter can be implemented by a Proportional and Integral (PI)controller [16].

In this application we design a complementary filterto obtain reliable roll and pitch orientation angles. Thesevariables can be computed from either of the sensor’smeasurements; by integrating the gyroscope readings orusing the trigonometric functions on the accelerometer asthe component of the gravity vector appears on each axisof the acceleration data. These trigonometric functions toderive the pitch (θ) and roll (φ) angles from the measuredacceleration on the each axis (ax, ay, az) are given Eq.(1).

φ = atan2(ay, az), θ = atan2(−ax,√a2y + a2z) (1)

The derived pitch angles from gyroscope and acceler-ations in this way and the output of the complementaryfilter are compared in Fig 5. As seen in the figure the com-puted orientation angles from the gyroscope measurementsexhibits drift due the integrated noise, whereas those fromthe accelerometer is jittery.

Fig. 5: Pitch Angle Derived from the Sensors and Filtered

Fig. 6 shows the block diagram of the complementaryfilter Matlab Simulink model. The error between the com-puted orientation angles by the gyroscope and accelerometersensor measurements are used as the input for the PI

controller in the block diagram. The output is the controlinput for the process.

Fig. 6: Complementary Filter Simulink Block Diagram

The transfer function for the estimated pitch and rollangles becomes;

φf =1

sφ̇g +

Kp

s(Φa − Φf ) +

Ki

s2(Φa − Φf ) (2)

After a series of algebraic manipulations, the transferfunction for the roll and pitch angles is obtained as infollows.

φf =s2

s2 +Kps+Ki

(1

sφ̇g)

+Kps+Ki

s2 +Kps+Kiφa (3)

In the equation φf , φg and φa represent the filteredroll and pitch rotations obtained from the gyroscope andaccelerometer measurements. The same difference equa-tion is used to compute both orientation angles. The filtercoefficients Kp and Ki must be defined. We used thePID tuning toolbox of Matlab to find the optimum filtercoefficients. Once the transfer function and it’s coefficientsare obtained, the difference equation for the digital filteringcan be computed using the bilinear transformation. TheMatlab’s PID tuning toolbox gives the optimum coefficientsas Kp = 7.5924 and Ki = 20.7015. Upon applying thebilinear transformation, the difference equation for the rollrotation is obtained as:

φf =(0.907z−1 − 1.814z−2 + 0.819z−3)φg

1− 1.8091z−1 + 0.8188z−2+

(0.093z−1 + 0.0048z−2 − 0.0882z−3)φa1− 1.8091z−1 + 0.8188z−2

The rotation matrix is obtained using these filteredpitch and roll rotations. The accelerometer measurementsare multiplied with the rotation matrix to align the sensorreadings to the Seqway’s motion coordinate system. Therotated accelerometer measurements are given in Fig. (7).

The dotted curves represent the aligned measurements inthe body coordinate system whereas the continuous curvesrepresent the unaligned measurements. The accelerationvalues were recorded while the Segway was in a stand stillposition. As seen in Fig. (7), the gravity value measuredalong the z-axis of the Android device fluctuates aroundEarth’s gravity value after rotation. As there is no lateraland longitudinal acceleration, the acceleration values on thex and y axes fluctuate around the zero line as expected.

Fig. 7: Aligned and Unaligned Accelerometer Measurements when theSegway at stand still

IV. CLASSIFICATION METHODS AND TRAINING

A. Classification Methods

The Sparse Representation based Classification (SRC)[17] and CRC assume that any observed signal y canbe approximated as a linear combination of the samplesignals from the same classes. The resulting system oflinear equation systems y = Ax is then solved using acombination of vector norms. The general objective functionof the optimization problem is given in Eq. (4) whereA = [A1, A2, . . . , An] ∈ Rmxn represents the trainingdictionary in which the training samples from every classesare put as a column vector and λ is the Lagrange penaltycoefficient.

minx

x̂ = ‖y −Ax‖q + λ‖x‖p (4)

In the SRC method, the norm values q = 1 or 2,and p = 1 are used depending on the noise and outlierconditions, whereas in the Least Square Regularization ver-sion of the CRC method (CRC RLS) [7], the norm valuesq = p = 2 are used. In this case the objective functionbecomes:

minx

x̂ = ‖y −Ax‖22 + λ‖x‖22 (5)

The label of the observed signal is obtained using theobjective function given in Eq. ( 6) where δi : Rn → Rnis the selection operator that selects the coefficients of ithclass while keeping other coefficients zero in the solutionvector x̂.

mini

ri(y) = ‖y −Aδi(x̂)‖2 (6)

The SRC relies on an overcomplete dictionary whereasfor the CRC RLS, the dictionary might be undercomplete,as the observed signal is approximated by using the trainingsamples from other classes collaboratively. The solution forthe objective function given in Eq. (6) is a Ridge regressiongiven in Eq. (7).

x̂ = (ATA+ λI)−1AT y (7)

Both of the methods yield high classification accuracyas well as a training dictionary with a high discriminatingcapacity. The iterative nature of the `1 solvers make the SRCbased methods slower then the CRC method in which thepre-computed regression operator Pλ = (ATA + λI)−1AT

is only computed once because it is independent of the ob-served signal y and stored for further computations. Due tothese reasons, the computation times are beyond comparisonthat nominates the use of CRC method as a good alternativeon smart phones and tablets. We compared the computationtimes in our simulations and found out that the B SRC takes112 seconds for complete the computations for 1000 timesamples with the minimum number of the iterations whilethe CRC takes 0.39 seconds.

B. Training - Finding Best Representative Patterns

In real time signal pattern classification applications, twoimportant issues arise. First, the signal that is observedand sampled are acquired by a sliding window with afixed width. This acquisition procedure stipulates puttingrepresentative signal samples for every observed pattern inthe training dictionary for sake of classification accuracy.Ensued by the second, picking these representative sampleson the training signal on which the boundaries of the classsamples are vague. Alluded issues deserve some elaboration.Let’s assume that we have a multi-dimensional signal asgiven in Fig. (8).

The signals are sampled at a frequency of 20 Hz andthe observed portion of a signal is acquired by a fixedlength sliding window. Fig. (8) shows three measurements;acceleration along the x-axis, pitch rate and angle collectedfor a sudden breaking experiment. In the experiments, therider accelerates, reaches a constant velocity then try to stopthe Segway immediately by harsh breaking maneuver seventimes. When we analyze the signal we ostensibly expect andobserve two breaking states which are cruising (no braking)and sudden breaking while there are three breaking patterns.The normal breaking is hidden in the sudden breakingregions on the signal and the mobility robot passes fromnormal breaking state for a short time before and after thesudden stop maneuver. As seen in the figure, in addition toindistinct noisy signal boundaries between the cruising andsudden breaking regions, the signal patterns belonging toeither of these classes are not alike.

In order to build a dictionary on the given training signal,the start and end points of the patterns must be distinguishedexactly that requires a diligent and tedious effort. Albeit one

Fig. 8: Aligned and Unaligned Accelerometer Measurements during aSudden Stop Experiment

built does not necessarily guarantee a high discriminationpower. On the other hand, the sliding window captures someportions from any of two states with the varying lengthduring transition that deteriorates the classification accuracy.

The remedy to these problems encountered in building atraining dictionary for the real-time applications is to createa matrix, the columns of which is captured by a slidingwindow, then clustering the training matrix into the unionof subspaces that generates the signal patterns in the classes.The resulting training matrix takes the form of a blockHankel matrix in which the neighboring columns are verysimilar to each other. In this regard, the clustered matrixcolumns according to their generating union of subspacescan be further clustered applying the same procedure, ifany training class is a combination of any two classes suchas in the case that sudden breaking patterns. When thistraining framework is implemented in a nested manner, allthe representatives from each class are distilled into theirpure class range successfully.

The subspace clustering algorithms have gained impetusduring the era of Compressed Sensing (CS) research fieldwhich seeks the sparsest solutions to the linear representationproblems. Similar to the CS problem setup, the form ofwhich is given for SRC, the subspace clustering methodsaim to represent the signal patterns stacked into a dictionaryby the linear combination of similar signals generated by thesame subspace by seeking the sparsest solution in differentsparsity promoting norms. These methods conglomeratearound two pivotal approaches; Low Rank Representation(LRR) [18] and Sparse Subspace Clustering (SSC) [19]methods benefiting from various imposed penalty conditionswith respect to problem setup. The subspace clustering

algorithm we utilized for the training dictionary matrix witha form of block Hankel structure is the Ordered SubspaceClustering (OSC) method 20 which is derived from the SSCapproach.

In SSC method, it is assumed that for a given observationmatrix X , any column of which can be represented bythe linear combination of the other similar columns. Theobjective function of this self similarity premise is given inthe following equation where Z is the coefficient matrix andE is the error.

minZ

‖Z‖1 s.t. X = XZ + E, diag(Z) = 0 (8)

The sparsity promoting norm `1 is used in the objectivefunction. After obtaining the coefficient matrix Z, an affinitymatrix is composed and the clusters are labeled usingspectral methods such as the Normalized Cuts [21]. TheOSC incurs an additional penalty function in Eq. (8) thatenforces the similarity of the neighboring columns. In ourproblem setup, as the training matrix consists of neighboringcolumns with a block Hankel structure, we employ OSC inthe training phase. The objective function of the OSC isgiven in Eq. (9).

minZ,E

1

2‖E‖2F + λ1‖Z‖1 + λ2‖ZR‖1,2

s.t. X = XZ + E (9)

where R in the equation is a lower bidiagonal matrix, allthe elements in the main and lower diagonal of which arerespectively -1 and 1 as given below.

R =

−11 −1

1 −1. . . . . .

1 −1

(10)

The overall training scheme is given in Fig. (9). Aftercollecting experimental data for each breaking states seventimes, we build the training matrix using the data of only oneexperiment, the others are separated for test and simulations.It is not possible to conduct experiments for only one state,such as sudden stop, thus every experiment contains at leasttwo breaking states such cruising and normal breaking.

As shown in Fig. (9) in which the label number of theclusters are shown, we take an experiment in which cruisingand normal breaking maneuver are subsequently repeatedand cluster the obtained data only once assuming that there isno sudden breaking intention of the driver. However, we runthe clustering algorithm twice for the experiment in whichsudden stops are performed repeatedly to further eliminatethe normal stops from the sudden stop cluster and obtainpure representatives for this state.

Fig. 9: Training Procedure

V. IMPLEMENTATION AND RESULTS

The procedures are summarized in Fig. (10) prepared bySimulink toolbox in Matlab. We implemented all the proce-dures in Matlab for simulations and used Java programmingenvironment for the mobile application. As the CRC RLSmethod has only a few lines of codes, the implementationdoes not take long for the mobile application once allthe parameters and coefficients are readily available to thesystem.

Fig. 10: Block Diagram of the Ride Assistance System - Signal Processingand Classification

We give the results for remaining six experiments foreach maneuver in the figure below. The normal and suddenbreaking figures are put in the first and second columnrespectively. One of the dictionary features, the pitch angleof the handle bar is also seen in the figure to provideadditional information. As seen in the first column, normalbreaking results, the class of the maneuver is labeled at afrequency of 20 Hz and each experiments is a mean lengthof 1300 samples. In total the application labels the observedsignal pattern 7800 times. The only misclassification regionis seen in the first column on the fifth experiment which isenclosed by a circle. The breaking pattern of the rider inthe enclosed region is close to sudden breaking where thepitch angle pattern is steeper then the others. The numberof sudden breaking label on the figure is only six out of7800 computations. On the sudden breaking column as westated in the preceding sections, the results contain normal

breaking labels.

Fig. 11: Normal and Sudden Breaking Experiments Results

We also collected data for only cruising mode, the resultsof which is given in Fig. (12). In the 40 seconds experiment,the rider accelerates and reaches a constant velocity andstops at the end with the normal breaking pattern as seen inthe figure.

Fig. 12: Cruising Experiment Results

We verified the accuracy and versatility of the proposedframework by following the same procedures for a differentapplication. The authors in the study [5] compare B SRCand SRC methods and demonstrate steering a power wheel-chair by posture recognition [22]. A hand continuouslytracked by a Leap Motion sensor which accurately trackthe position and orientation of the hand at sub-millimeterlevels and the recognized hand postures are mapped to thesteering commands for a robotic wheelchair. We extendedthese studies by replacing the B SRC method for hand

posture recognition with the CRC, employed the subspaceclustering method in the training phase and introduce handgesture recognition as the hand makes a gesture while inthe transition state; changing position from one posture toanother. We followed the same procedures given in thispaper for the power wheelchair steering by hand postureand gesture recognition project in which we have beendeveloping smart human machine interfaces for severelydisabled people.

Fig. 13: Continous Hand Posture an Gesture Recognition for Steering aPower Wheelchair

The results of the experiments are given in Fig. (13)which shows the recognition results of six different testsimulations. In the simulations, a hand continuously changepostures. There are five deictic hand posture states whichare turn left, right, go straight, stop and go backward. Thetrajectory of the hand while it is changing the posture oneto another is a gesture, the number of which is eight in theapplication. The application first classify whether the handis in a gesture and posture state then label the hand stateout of five postures and eight gestures. The number of totalclassification is 5850 and the recognition accuracy is 100%as shown in the figure. Unlike the braking state classification,the states of hand are well-defined and less noisy in the handposture and gesture study in which the classification moreaccurate.

VI. CONCLUSION AND FUTURE WORK

In this study, we explained a framework to develop aride assistance system which include a signal processingan classification module capable of classifying any type ofsignal patterns and in particular, we detailed the breakingmode classification and training procedures. The subspacemethod we employed in the training phases lead obtaininga clean dictionary in which the pure representative samplesfrom each of the class are stacked a column vector. Thereforethe recognition accuracy is almost 100% for the breakingmodes and 100% for the hand gesture and posture studyin the real-time applications. In the near future, we plan toinvestigate the differences of the ride characteristics in termsof age, gender and skills with the robotic mobility and othermobility devices.

ACKNOWLEDGMENT

This study is supported by Japan Society for the Promo-tion of Science fellowship program.

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