UNDERGRADUATE RESEARCH PROJECT, EE497, 2015 1

Detection and Classification for Freezing of Gaits inParkinson’s Disease Using Inertial Measurement

UnitsWei Miao, Collaborating with G.V. Prateek, Supervised by Ed Richter and Arye Nehorai

Abstract—Freezing of Gaits(FOG) is a phenomenon in Parkin-son’s Disease characterised by a short period of inability toinitiate a gait or by a series of short steps with tremors on lowerlimbs, which mostly occurs when initiating gaits and turning.For researchers in health therapy, it is important to identify theinstances of freezing of gait (FoG), in order to study the gait ofa person suffering with Parkinson’s disease. A video for walkingof Parkinson’s patient must be recorded so that researchers canlabel the time and type of FOG by watching the video again.

To reduce the manual work and the cost of equipments, suchas a video feed data, we use low cost Inertial MeasurementUnit(IMU) to automatically detect and classify these occurrencesof FoG.We use the instances of zero-velocity updates to detect FoGinstances and classify them accordingly. With a decision machinecombined with support vector machine analyzing ZUPTs andyaw angle from the Kalman Filter, we successfully detected andclassified the FOG in our experiments.

Keywords—Freezing of Gaits, Zero-Velocity Updates, DecisionMachine, Support Vector Machine

I. INTRODUCTION

A. Freezing of GaitsFreezing of Gait(FOG), with the inability of initiating gaits

or arrhythmic stepping of high frequency (4-5Hz), is one of theleast understood and most disturbing symptoms in Parkinson’sDisease(PD) that attracts numerous researchers working on[1]. FOG is a PD symptom of high stage that can occurunder different circumstances. Based on the research fromNeurology [2], FOG can be elicited by turning(63%), initiatinggaits(23%), walking through narrow spaces(12%) and reachingdestination(9%). Different ways of mitigating symptoms canbe proposed for different types of FOG, so that researchersneed the FOG type and its severity. In experiments of re-searching FOG, a video is always necessary for researchersto manually label the period and type of FOG. In order toreduce the laborious work dealing with videoes, an automaticway of detecting and classifying FOG is necessary. Since FOGis obviously different from normal walking in kinematic way,

A. Nehorai is with the Department of Electrical and Systems Engineering,Washington University in St. Louis, St. Louis, Missouri, 63130 USA e-mail:(see http://www.ese.wustl.edu/ nehorai/index.html).

W. Miao is with the Department of Automation, Tsinghua University,Beijing, China, 100084 e-mail: (mw12@mails.tsinghua.edu.cn).

P. G. Vijay and E. Richter are with the Department of Electrical and SystemsEngineering, Washington University in St. Louis, St. Louis, Missouri, 63130USA.

Manuscript written August 24, 2015.

algorithm using kinematic data of patient to detect FOG ispossible to develop.

B. Inertial Measurement Unit

Inertial Measurement Unit(IMU), consisting of three-axisaccelerometers and three-axis gyroscopes, provides three axisand angular turning rate detection [3]. IMU is right now widelyused in indoor navigation [4] [5] [6]. With the informationof accelerations and angular velocities provided, we can alsopredict the position and gesture through gait analysis usingKalman filter [7] [8].

The remainder of this project is organized as follows.Section II introduces the all the methods we used in ourproject, including the setup for device and experiment, thealgorithm of detecting and classifying FOG. Section III showsthe results of experiments through our algorithm. Section IVillustrates the conclusion and discussion about our project.

II. METHODOLOGIES

A. Device Setup

1) Hardware Setup: The device we used in this project is amodule of foot-mounted indoor navigation system implemen-tation described in reference [9]. In this implementation, thereare four invensense MPU9150 IMUs, Atmel ACR32UC3Cmicrocontroller, Bluetooth and USB interfaces. In each IMU,there are one three-axis accelerometer and one three-axisgyroscope. Each IMU can collect three-axis accelerations andthree axis angular velocities with a sample rate of 1000Hz atfull speed. In our project, we need two modules in total fortwo feet.

In order to collect data with wired cable connected to theIMU module, two USB cables (USB A male to Micro USBMale) longer than 10 feet and one laptop with more than twoUSB interface available are also required.

More documents for IMU module can be found on thewebsite in reference [10].

2) Software Setup: To correctly configure the device andcollect data, environment of software must be setup in thelaptop used. The instruction of installing software can befound in reference [11]. (Note: If AVR Studio 5 doesn’t work,download the latest verison of SVR Studio)

UNDERGRADUATE RESEARCH PROJECT, EE497, 2015 2

(a) side view (b) front view

Fig. 1. Fix the IMU devices onto shoes

B. Experiment SetupWe applied to collect data from patients with Parkinson’s

Disease. But unfortunately the approval was beyond the dateof this project. In order to collect data, we try to mimicthe gait of a person suffering with Parkinson’s disease. Welabel the obtained data into different categories of FOG. Weuse data from several experiments to train a Support VectorMachine(SVM). Using data from other experiments, we testthe validation of our algorithm by comparing the labeledcategories with categories given by SVM. The experiment wedid in this project is setup by the following step:

1) Fix the IMU devices on top of shoes and connect themto the laptop with USB cables as shown in figure 1.

2) Turn on the IMU devices and check the Port Numberby opening the Device Manager of the Laptop. Then modifythe Matlab code with the correct Port Number.

3) Configure the IMU device and set the sample rate as1000Hz. The communication protocol of IMU device canbe found in the reference [12]

4) Walk step by step straight forwardly for about 10meters. Then turn around and walk back. The walkingspeed is about 1 m/s.

C. Zero-Velocity UpdatesZero-Velocity Updates(ZUPTs) is a method initially used to

bound error growth of Kalman filter in pedestrian navigationsystems [13]. The detection of ZUPTs is to make a decisionwhether the IMU is stationary or not. Mathmetically, thedetection problem can be translated into a binary hypothesistesting problem, where two hypothesis H0 and H1 defined asthe following are chosen by a detector [13]:

H0: IMU is movingH1: IMU is stationary

For the IMU we used, we choose GLRT as the zero-velocitydetector [13]. This detector can be represented as choosing H1

if: (notations declared in [13])

T (zn) =1

N

∑k∈Ωn

(1

σ2a

‖yak−g

yan

‖yan‖

2

‖+ 1

σ2ω

||yωk ||2) < γ′ (1)

In the project, we define the output of detector for znby equation 2. The output represents whether the IMU isstationary or not at sample time n.

zn =

1 if H1 is chosen0 if H0 is chosen

(2)

A typical sequence of ZUPTs in our experiment can beshown as figure 2(a). As figure 2(a) shows, ZUPT=0 whenvelocity is obviously above zero. While ZUPT possibly equalsto one when velocity is near zero. (Estimation of velocity iscalculated by Kalman Filter).

D. Decision MachineThe gait of a patient with Parkinson’s disease can be

considered as a state machine as shown in figure 2(b).As figure 2(b) shows, a stride for normal walking can be

divded into two period, stationary (ZUPT = 1) and moving(ZUPT = 0). If the patient starts freezing, the FOG occurs aftertoes down, before standing stationary. So that we can developa decision machine to judge the state of walking using theZUPTs. The state diagram is shown as figure 3.

The function of this decision machine is to divide thewhole sequence of ZUPTs Z0 into sequences of ZUPTsfor separate strides Z1,Z2, · · · ,Zn by detecting a period ofmoving longer than an adaptive threshold. After setting aninitial value as a baseline according to the rules of velocityin our experiment, the threshold is calibrated by the normalstrides in the experiment. In the remainder of this project, wedefine a stride Freezing Stride if a stride contains freezing.If not, we define it as Normal Stride. The typical stem plotsfor a Normal Stride and Freezing Stride are shown as figure4(a) and figure 4(b).

E. Support Vector MachineThe decision machine is used to divide Z0 into separate

strides. After that, we need to classify these strides intodifferent types. We used two support vector machines, onefor identifying freezing stride from normal stride, one foridentifying turning freezing from non-turning freezing. In thissection, we trained two SVM models using six experiments.The experiments were done by three people and each of themdid two experiments.

UNDERGRADUATE RESEARCH PROJECT, EE497, 2015 3

(a) Sequence of ZUPTs in experiment (b) State Machine for walking

Fig. 2. Analysis of walking in experiment

Fig. 3. State Diagram

(a) Stem plot for typical normal stride (b) Stem plot for typical freezing stride

Fig. 4. ZUPT sequence in one stride

UNDERGRADUATE RESEARCH PROJECT, EE497, 2015 4

Training(nm vs fr).jpg

Fig. 5. Seperating Hyper-plane for Normal Stride vsFreezing Stride

1) Normal Stride vs Freezing Stride: We extracted twofeatures to identify freezing stride from normal stride.

First, a characteristic symptom of FOG is the inability ofinitialing gaits or irregular tremors of high frequency. Thedecision machine we used clarify a stride by detecting a longperiod of moving, while the periods of moving during FOGare always drastically shorter than the one in a normal gait.We may consider a freezing stride consists of freezing andone normal stride, so that the length of freezing stride is oftenlonger than the one of normal stride as shown in figure 4(a)and figure 4(b). For the sequence of ZUPTs Zi of a stride, wedenote the length of Zi as Li and the length of last movingperiod as li. We define Duty Cycle for Zi as 3. Normalstrides usually have smaller Di than freezing stride.

Di =Li − liLi

(3)

Second, since FOG usually represents irregular tremors,there are several rising edges and trailing edges in ZUPTs ofa freezing stride. While in ZUPTs of a normal stride, ideallythere is only one trailing edge as shown in figure 4(b). Wedenote the sum of rising edges and trailing edges in Zi as ciand define F lip ratio by equation 4. Normal strides usuallyhave smaller fi than freezing stride.

fi =ciLi

(4)

We define state variable xi for Zi as xi = [Di, 100 ∗ fi].Here we set a factor 100 because if the first element of xi ismuch larger than the second, the cut-off error in calculatingfactors for SVM will be magnified. By setting elements in xicomparable to each other, we can reduce the cut-off error.

By plotting all the xi for strides detected by decisionmachine on a two-dimensional plane in figure 5, we can seexi is linear separable. So we can use a Support Vector Ma-chine(SVM) with linear kernel to classify strides into normalones and freezing ones. We denote this SVM as SVM1.

In our training of SVM1, our goal is to find a functionf(x) = a′x − b that classifies the non-separable pointsx1, · · · ,xN and y1, · · · ,yM into two sets by making

Training(turning vs nonturning).jpg

Fig. 6. Seperating Hyper-plane for Turning Freezing vs Non-TurningFreezing

a trade-off between misclassfications and width of separatingslab. Mathmatically, a and b can be obtained by solving theoptimization problem below.(‖ · ‖p represents the p-norm. <represents element-wisely larger than)

minimize ‖a‖2 + γ1 ∗ ‖u‖1 + γ2 ∗ ‖v‖1s.t. a′xi − b > 1− ui for i = 1,· · · ,N

a′xi − b 6 −(1− vi) for i = 1,· · · ,Mu < 0 and v < 0

In our project, we set γ1 = γ2 = 0.5. The separating hyper-plane is shown in figure 5. By manually label points for normalstrides and freezing strides, we can calculate the training errorinside the SVM as shown in table I.

TABLE I. TRAINING ERROR INSIDE SVM1

Classified MisclassifiedNormal Stride 391 4Freezing Stride 68 4

2) Turning Freezing vs Non-Turning Freezing: Accordingto reference [2], there are several types of triggers for FOG,among which turning is the most important. The other threecommon triggers, walking through narrow space, initiatinggaits and reaching destination, are all FOG that occurs whenwalking straight forwardly. Only according to the specific ordergiven in the experiment can the types of FOG triggered bythose three be defined, or they will be just treated as non-turning freezing.So that after detecting strides and classifyingthem into normal and freezing strides, we can then classify thefreezing strides into turning freezing and non-turning freezing.

The only difference between FOG elicited by turning andother triggers is the yaw angle in the horizontal plane foreach foot changes when the freezing occurs. Given three-axisaccelerations and three-axis angular velocity, we can estimatethe yaw angle for each foot by Kalman Filter.

UNDERGRADUATE RESEARCH PROJECT, EE497, 2015 5

For the ith freezing stride, we denote the start time as tsand the end time as te. We also denote the yaw angle at timet for left foot as Lyaw(t), the one of right foot as Ryaw(t).

We then define state parameter yi = [Li, Ri], in which Li =Lyaw(te)− Lyaw(ts),Ri = Ryaw(te)−Ryaw(ts).

As shown in figure 6, we plot yi for the freezing strides weclassified from the SVM1. We denoted the SVM that classifiesturning and non-turning freezing as SVM2. Figure 6 showsthat yi for turning freezing and non-turning freezing is linearseparable so that we can use another SVM to classify them.The separating hyper-plane is shown in figure 6.

By manually label points for turning freezing and non-turning freezing, we can calculate the training error inside theSVM2 as shown in table II.

TABLE II. TRAINING ERROR INSIDE SVM2

Classified MisclassifiedTurning Freezing 12 1

Non-turning Freezing 59 0

III. RESULTS

We did six experiments to valid our algorithm of detectingand classifying FOG. The experiments were given by twopeople. One gave out four experiments and the other gave outtwo experiments.

Result of one experiment is shown in figure 7 and tableIII and tableIV. The estimation of Kinetic information isgiven by Kalman Filter. We can see the time of FOG hasbeen approximately detected and the type of FOG has beensuccessfully classified.

Fig. 7. Kinetic information for Experiment 1

TABLE III. FOG ON LEFT FOOT IN EXPERIMENT 1

Start End Type1 9.624s 15.419s Non-Turning2 23.131s 28.176s Turning3 28.177s 33.248s Turning4 39.504s 45.085s Non-Turning

After labeling type of FOG manually, we can calculate thetesting error by counting misclassifications in two SVMs. Theoverall testing error for SVM1 and SVM2 among the sixexperiments are shown in table V and table VI.

TABLE IV. FOG ON RIGHT FOOT IN EXPERIMENT 1

Start End Type1 10.506s 16.142s Non-Turning2 22.365s 32.323s Turning3 40.087s 46.028s Non-turning

TABLE V. TESTING ERROR INSIDE SVM1

Classified MisclassifiedNormal Stride 170 4Freezing Stride 39 2

TABLE VI. TESTING ERROR INSIDE SVM2

Classified MisclassifiedTurning Freezing 13 1

Non-Turning Freezing 23 3

IV. CONCLUSION AND DISCUSSION

As we can see, our algorithm works well for the experimentswe used. There are still several discussions and prospectivework for this project.

When traing the SVM model, ui and vi represent themisclassification of normal stride and freezing stride in train-ing. So that γ1 and γ2 can be considered as punishmentparameter in target function. We can use different γ1 andγ2 for different purpose. For example, if we are reluctant inmissing FOG, γ2 can be set bigger to enlarge the punishmentfor misclassification for FOG.

The sample size for training and testing SVM model maybe enlarged. More patients with Parkinson’s Disease should beinvolved in the experiments to train more robust SVM model.With sample of great diversity, our model can be validatedbetter.

Numbers of IMU modules and positions they are placed alsoneed further discussion. Work of discussing the best way ofplacing IMU modules can be done in the future.

Since the methods we used in our project, including KalmanFilter, Zero-Velocity Updates, Classification using SVM, alluse instant data or a moving window of data, online detectionand classification can be developed. Once online detection isachieved, motors that assist patients or other facilities can beadded to help patients walking.

ACKNOWLEDGMENT

I would like to thank Prateek Gundannavar Vijay for thediscussion and help in this project. I also would like to thankProf. Edward Richter for the help on setting up hardware anddaily supervision. More importantly, I would like to thank Prof.Arye Nehorai for the generous invitation and support for thisproject!

REFERENCES

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[2] J. D. Schaafsma, Y. Balash, T. Gurevich, A. L. Bartels, J. M. Hausdorff,and N. Giladi, “Characterization of freezing of gait subtypes andthe response of each to levodopa in parkinson’s disease,” EuropeanJournal of Neurology, vol. 10, no. 4, pp. 391–398, 2003. [Online].Available: http://dx.doi.org/10.1046/j.1468-1331.2003.00611.x

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[3] M. M. Morrison, “Inertial measurement unit,” Dec. 8 1987, uS Patent4,711,125.

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[12] J.-O. Nilsson, “Communication protocol for the OpenShoe modules,”http://www.openshoe.org/wp-content/uploads/2015/01/OpenShoecommunication protocol.pdf/, 2015, [Online; accessed 24-August-2015].

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Wei Miao will receive the B.Sc. degree in automa-tion in 2016 from Tsinghua University, China. Inthe summer of 2015, as an arsing senior, he came toDepartment of Electrical and Systems Engineeringin Washington University in St. Louis to attendUndergraduate Research Project, supervised by Prfo.Arye Nehorai and Edward Richter.