Data augmentation of wearable sensor data for parkinson’s disease monitoring using convolutional neural networks

doi:10.1145/3136755.3136817

Data Augmentation of Wearable Sensor Data for Parkinson’s

Disease Monitoring using Convolutional Neural Networks

∗

Terry T. Um

University of Waterloo

Canada

terry.t.um@gmail.com

Franz M. J. Pster

Ludwig-Maximilians-Univ. München

Germany

fmj.pster@me.com

Daniel Pichler

Technical University of Munich

Germany

daniel.pichler@tum.de

Satoshi Endo, Muriel Lang,

Sandra Hirche

Technical University of Munich

Germany

{s.endo,muriel.lang,hirche}@tum.de

Urban Fietzek

Schön Klinik München Schwabing

Germany

urban.etzek@schoen-kliniken.de

Dana Kulić

University of Waterloo

Canada

dana.kulic@uwaterloo.ca

ABSTRACT

While convolutional neural networks (CNNs) have been success-

fully applied to many challenging classication applications, they

typically require large datasets for training. When the availability

of labeled data is limited, data augmentation is a critical prepro-

cessing step for CNNs. However, data augmentation for wearable

sensor data has not b een deeply investigated yet.

In this paper, various data augmentation methods for wearable

sensor data are proposed. The proposed methods and CNNs are

applied to the classication of the motor state of Parkinson’s Dis-

ease patients, which is challenging due to small dataset size, noisy

labels, and large intra-class variability. Appropriate augmentation

improves the classication performance from 77.54% to 86.88%.

CCS CONCEPTS

· Computing methodologies → Supervised learning by clas-

sication; · Applied computing → Consumer health;

KEYWORDS

Data augmentation; wearable sensor; convolutional neural net-

works; Parkinson’s disease; health monitoring

ACM Reference Format:

Terry T. Um, Franz M. J. Pster, Daniel Pichler, Satoshi Endo, Muriel Lang,

Sandra Hirche, Urban Fietzek, and Dana Kulić. 2017. Data Augmentation of

Wearable Sensor Data for Parkinson’s Disease Monitoring using Convolu-

tional Neural Networks. In Proceedings of 19th ACM International Conference

on Multimodal Interaction (ICMI’17). ACM, New York, NY, USA, 5 pages.

https://doi.org/10.1145/3136755.3136817

∗

This work was partly supported by the EU Seventh Framework Programme FP7/2007-

2013ithin the ERC Starting Grant Control based on Human Models (con-humo), grant

agreement No. 337654.

Permission to make digital or hard copies of all or part of this work for p ersonal or

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ICMI’17, November 13ś17, 2017, Glasgow, UK

ACM ISBN 978-1-4503-5543-8/17/11. . . $15.00

https://doi.org/10.1145/3136755.3136817

1 INTRODUCTION

In recent years, convolutional neural networks (CNNs) have shown

excellent performance on classication problems when large-scale

labeled datasets are available (e.g. [

9

,

20

]). However, it is challenging

to apply CNNs to problems where only small labelled datasets are

available. For example, collecting and labeling a large amount of

medical data is often dicult. As a result, it is challenging to apply

CNNs to small-scale medical data.

Data augmentation leverages limited data by transforming the

existing samples to create new ones. A key challenge for data aug-

mentation is to generate new data that maintains the correct label,

which typically requires domain knowledge. However, it is not

obvious how to carry out label-preserving augmentation in some

domains, e.g., wearable sensor data. For example, scaling of the

acceleration data may change their labels because some labels are

dierentiated by the intensity of motion.

In this paper, the problem of classifying the motor state of Parkin-

son’s disease (PD) patients is tackled using CNNs. PD motor state

classication is a challenging task due to noisy labels, irrelevant

motion interference, large variability over patients, and limited

availability of the labelled data. In this paper, we propose data aug-

mentation methods for wearable sensor data and successfully tackle

the challenging PD classication task using CNNs.

The contributions of the paper can be summarized as follows:

•

Application of CNNs to the task of PD motor state classica-

tion, using a clinician-labeled dataset of 30 PD patients (25

patient’s data are exploited) in daily-living conditions.

•

A set of approaches for data augmentation of wearable sensor

datasets for CNN-based classication.

•

Experimental comparison of proposed data augmentation

methods.

2 RELATED WORK

Most PD patients experience motor uctuations, which are charac-

terized by phases of bradykinesia, i.e. underscaled and slow move-

ment, and dyskinesia, i.e. overowing spontaneous movement [

17

].

Dopaminergic treatment can alleviate symptoms of bradykinesia

while its over-treatment can cause dyskinesia. Thus, an accurate

evaluation of a patient’s phenomenology is needed for determining

the right dose of medication. Current PD motor state evaluation

216

ICMI’17, November 13ś17, 2017, Glasgow, UK T. Um, F. Pfister, D. Pichler, S. Endo, M. Lang, S. Hirche, U. Fietzek, and D. Kulić

(a)Bardykinesia

(typical)

(b)Dyskinesia

(typical)

(c)Bardykinesia

(atypical)

(d)Dyskinesia

(atypical)

Figure 1: (a) and (b) show typical examples of bradykinesia

and dyskinesia in a 1 min window while (c) and (d) show

atypical patterns. The blue, red, green represent X,Y,Z sig-

nals from the accelerometer, respectively.

methods rely on patient self-reports and visual observation by the

clinician [17].

Researchers have proposed automating the evaluation with wear-

able sensors (e.g. [

5

,

19

]). However, most approaches to date have

been limited to standardized motor tasks in clinical settings [

18

].

To enable automated evaluation of PD motor states which covers

a wide range of PD symptoms across patients, a large amount of

wearable sensor data in daily-living conditions is needed [

4

]. Deep

learning (DL) approaches [

15

] provide a promising methodology

to deal with the large variability of PD data [

5

,

8

,

13

]. Given the

diculty in collecting such large datasets, data augmentation is

needed [6].

Data augmentation is an indisp ensable preprocessing step for

achieving peak performance in DL approaches (e.g. [

9

,

12

]). For

augmenting time-series data, Le Guennec et al. [

14

] used window

slicing and window warping methods, which extracts multiple

small-size windows from a single window and lengthens/shortens a

part of the window data, respectively. Unlike data augmentations for

image [

2

] and speech recognition [

3

], however, data augmentation

for wearable sensor data has not b een systematically investigated

yet to the best of our knowledge. In this paper, we propose various

data augmentation methods that enable the classication of PD

motor states from wearable data and evaluate them using CNN.

3 PD MOTOR STATE CLASSIFICATION

3.1 Challenges in PD Data

We consider two frequent PD motor states: bradykinesia, which

is characterize d by decreased movement speed and may be ac-

companied by tremor, and dyskinesia, which is characterized by

involuntary extremity movements. Figure

1 illustrates exemplar

one minute data windows of both motor states, from a single ac-

celerometer worn on the wrist of PD patients. Bradykinesia data

typically appear as constant signals indicating less movement (Fig

1(a)) while dyskinesia data consist of uctuating movements (Fig

1(b)).

However, there are a signicant number of examples that devi-

ate from the stereotypical expressions. For example, bradykinesia

accompanied by tremor can show uctuating signals which look

like a dyskinesia state (Fig 1(c)). On the other hand, dyskinesia with

voluntary suppression can show constant signals which look like a

bradykinesia state (Fig 1(d)).

There are several factors that can cause an apparent disagree-

ment between the obser ved data pattern and the expert label. First,

if the body of the patient indicates, e.g., a dyskinesia state, but the

hand which wears the wearable sensor does not move because the

patient is, e.g., holding a chair for suppressing the symptom, the

assigned label based on the overall body expression will be mis-

matched with the recorded data from the wearable device. Also,

the expert rater typically rates the symptoms for a xe d length

window, but arbitrary segmentation into xed length windows

may not result in single motor state windows. Furthermore, the

interference of voluntary movements, e.g., waving the hand, can

make bradykinesia states look like dyskinesia, and, e.g, voluntary

rest, appear like bradykinesia. Finally, bradykinesia accompanied

by tremor can also can make it dicult to distinguish between

bradykinesia and dyskinesia.

The factors described above introduce noisy labels, and lead to

large intra-class variability and signicant overlap between two

classes. As a result, it makes the PD motor state classication more

challenging, particularly given a small amount of data.

3.2 Data Augmentation Methods for Wearable

Sensor Data

Data augmentation can be viewed as an injection of prior knowl-

edge about the invariant properties of the data against certain trans-

formations. Augmented data can cover unexplored input space,

prevent overtting, and improve the generalization ability of a

DL model [

6

]. In image recognition, it is well-known that minor

changes due to jittering, scaling, cropping, warping and rotating do

not alter the data labels because they are likely to happen in real

world observations. However, label-preserving transformations for

wearable sensor data are not obvious and intuitively recognizable

(Fig 2).

One factor that can introduce label-invariant variability of wear-

able sensor data are dierences in sensor placement between partic-

ipants. For example, an upside-down placement of the sensor can

invert the sign of the sensor readings without changing the labels.

Therefore, augmentation by applying arbitrary

rotations (Rot)

to

the existing data can be used as a way of simulating dierent sensor

placements.

Another factor that can introduce variability is the temporal

location of activity events, e.g., tremor, in the window. Since the

xed size window segmentation is arbitrary, the location of the

observed symptom in the window does not have any meaning. Thus,

we may augment data by perturbing the location of the windows

or events.

Permutation (Perm)

is a simple way to randomly perturb the

temporal location of within-window events. To perturb the location

of the data in a single window, we rst slice the data into

N

same-

length segments, with N ranging from 1 to 5, and randomly permute

the segments to create a new window.

Time-warping (TimeW)

is another way to perturb the temporal location. By smoothly dis-

torting the time intervals between samples, the temporal locations

of the samples can b e changed using time-warping.

Small changes in magnitude may preserve the labels, dep end-

ing on the target task.

Scaling (Scale)

changes the magnitude of

the data in a window by multiplying by a random scalar, while

217

Data Augmentation of Wearable Sensor Data for Parkinson’s Disease Monitoring ... ICMI’17, November 13ś17, 2017, Glasgow, UK

No Aug Jittering Scaling Rotation Permutation MagWarp TimeWarp Cropping

Rot+Perm1 Rot+Perm2 Rot+Perm3 Rot+Perm4 MagW+TimeW1 MagW+TimeW2 MagW+TimeW3 MagW+TimeW4

Figure 2: Various data augmentations that are used in the experiments: jittering, scaling, rotating, permutating, magnitude-

warping, time-warping methods. Combinations of various data augmentations can also be applied.

magnitude-warping (MagW)

changes the magnitude of each

sample by convolving the data window with a smooth curve vary-

ing around one. In addition,

jittering (Jitter)

is also considered as

a way of simulating additive sensor noise. These data augmenta-

tion methods may increase robustness against multiplicative and

additive noise and improve performance.

Lastly,

cropping (Crop)

, which is similar to image cropping or

window slicing in [

14

], is applied for diminishing the dependency

on event locations. Note that cropping can capture an event-free

region, which might change the label. Also, note that cropping with

random locations over epochs will eventually converge to a sliding

window metho d with arbitrary stride sizes.

In a nutshell, jittering, scaling, cropping, rotating, permutating,

magnitude-warping and time-warping methods are applied for aug-

menting wearable sensor data. In the next section, the performance

of PD motor state classication with the proposed data augmenta-

tion methods is evaluated using CNNs.

4 EXPERIMENTS

4.1 Data Preparation

A dataset of 30 patients’ motor states was collected using Microsoft

Band 2 [

1

] in daily-living conditions without requesting specic

motor tasks

1

. The 30 PD patients are 67

±

10 years old, median

Hoehn & Yahr stage 2, average disease duration 11

±

5 years, and

MoCA points 26

±

3. Among them, 25 patient’s data are used for this

research and each one minute interval is labeled by a clinical expert.

The data are collecte d at a frequency of 62.5Hz and resampled to

120Hz to deal with sampling irregularities. The rst 58-seconds of

data (6960 samples) from each one minute window is used to make

same-length instances.

Similar to previous works (e.g. [

19

], [

7

], [

8

], [

5

]) acceleration

data only are used for the PD motor state classication. Also, no-

symptom data are removed to simplify the problem and focus on

characterizing data augmentation methods. Data collected during

walking, laying and eating activities are also removed due to limited

observation of movement during these activities. Note that no other

preprocessing, e.g., data normalization or smoothing, is applied

because they may confound the data label and subsequent results.

The resulting dataset consists of 3530 min (58.8 hours) of bradyki-

nesia and dyskinesia data. For cross-validation, the 25 PD patients

1

The study was approved by the ethics committee of Technical University of Munich

(Az. 234/16 S).

⋮

6960*3 2319*3 772*3 385*3 193*3 97*3 49*3 48*1 48 2

⋮

input conv1(16) conv2(32) conv3(64) conv4(64) conv5(64) conv6(64) conv7(64) GAP(1) output

input size

Figure 3: 7-layer CNN with a global average pooling (GAP).

The 7-layer CNN consists of 16-32-64-64-64-64-64 feature

maps which reduce the size of the inputs to 2319*3, 772*3,

385*3, 193*3, 97*3, 49*3, 48*1, respectively.

are divided into ve subject groups. The performance of PD mo-

tor state classication is reported in Section 4.3 using the average

values of 5-fold cross-validation results.

4.2 The CNN Architecture

In this research, CNNs are used for PD motor state classication.

CNNs are more suitable for small-scale datasets than long short-

term memories (LSTMs) [

10

] because CNNs generally use a smaller

number of parameters compared to fully-connected LSTMs. Deep

and sparse 7-layer CNNs (Figure 3) are employed to capture the

large variability of the small-scale PD data.

A convolutional layer, a batch normalization layer [

11

], and an

activation layer using rectied units (ReLUs) form a single con-

volutional layer of the 7-layer CNN. With strided convolutions

using 4*1, 4*1, 3*1, 3*3, 2*3, 2*3, 2*3 convolution lters, the sizes

of the inputs are reduced from 6960*3 to 48*1 over layers (Figure

3). Note that XYZ signals of the accelerometer are convolved in

layers 4,5,6 and 7 to capture inter-vector-component features. For

reducing the number of parameters for small-scale datasets, a global

averaging pooling (GAP) layer [

16

] is applied at the end instead of

fully-connected layers.

4.3 Results

Classication of PD motor states is performed using the CNN with

the various data augmentation methods. For baseline results, a sup-

port vector machine (SVM) with an RBF kernel is applied to 540

dimensional statistical features: mean, variance, skewness, kurtosis,

218

ICMI’17, November 13ś17, 2017, Glasgow, UK T. Um, F. Pfister, D. Pichler, S. Endo, M. Lang, S. Hirche, U. Fietzek, and D. Kulić

Table 1: The results of PD motor state classication with

various data augmentation methods. R,P,T,M represent Rot,

Perm, TimeW, MagW, respectively.

SVM CNN Jitter Scale Crop Rot Perm

Train 98.82 99.92 99.78 99.84 65.77 100.0 99.33

Test 70.72 77.54 77.52 79.46 73.58 82.62 81.16

MagW TimeW P,T R,P R,T R,P,T R,P,T,M

Train 100.0 94.67 96.63 99.08 94.70 94.43 94.20

Test 79.33 82.00 81.75 86.76 85.01 86.88 85.60

0 100 200 300 400

Epoch

30

40

50

60

70

80

90

100

Accuracy (%)

(a) Training Accuracy

CNN

Rot

Perm

TimeW

R,P

R,P,T

0 100 200 300 400

Epoch

30

40

50

60

70

80

90

100

Accuracy (%)

(b) Test Accuracy

CNN

Rot

Perm

TimeW

R,P

R,P,T

Figure 4: Training curves for CNN, Rot, Perm, TimeW,

Rot+Perm and Rot+Perm+TimeW methods. The curves of

Rot+Perm+TimeW shows slow training improvement and a

better generalization performance.

and maximum values are extracted from 1 min data using 5 and

10-sec sliding windows. Also, a CNN is applied to raw 1 min data

without data augmentation for baseline comparison. All experi-

ments except for the SVM are performed for 400 epochs and the

median values from the last 10 epoch results are used for averaging

the 5-fold cross-validation results.

Dierent random parameter values are applied for data augmen-

tation. For jittering, a standard deviation (STD) value is sampled

from a Gaussian distribution with 0.03 STD, and 1 min of Gaussian

noise is generated using the sampled STD value. For scaling, a ran-

dom scalar is sampled from a Gaussian distribution with a mean of 1

and 0.1 STD. For rotation, an arbitrary rotation matrix is generated

for each instance. For permutation, a random integer

N

is deter-

mined by rounding a positive value sampled from a Gaussian dis-

tribution with 5.0 STD. For magnitude-warping and time-warping,

random sinusoidal curves are generated using arbitrary amplitude,

frequency, and phase values. The implemented code for the pro-

posed data augmentation methods is available online: https://github.

com/terryum/Data-Augmentation-For-Wearable-Sensor-Data

The main results are presented in Table 1. Jittering fails to im-

prove the performance of PD motor state classication because

it introduces rapid uctuations which look similar to dyskinesia.

Cropping also fails because it drops the information of 2

/

3 win-

dow samples, which could be a critical loss given the small dataset.

Cropping of an event-free region also hinders the learning process

and can be a cause of the poor performance. Scaling and magnitude-

warping also fail be cause changing of the intensity of the signal

may alter the labels.

Figure 5: Randomly selected 40 incorrect mispredictions

from the Fold-1 results of Rot+Perm+TimeW experiment.

Fluctuating signals from bradykinesia (white) and constant

signals from dyskinesia (yellow) are often misclassied.

On the other hand, rotation, permutation, and time-warping

methods improve the performance of PD motor state classication.

The best performance among the single data augmentation methods

is achieved by rotation. Permutation and time-warping also provide

performance improvements by perturbing the temporal locations

of samples. These results indicate that the major sources of vari-

ability are dierent sensor placements between participants and

event locations in an arbitrarily segmented window. The proposed

rotation, permutation, and time-warping methods eectively com-

pensate the unnecessary variations and improve the performance

by 3.6-5.1% accuracy.

Combinations of various data augmentation methods show bet-

ter performance than that of a single data augmentation method.

The combinations of Rot+Perm and Rot+TimeW show better perfor-

mance than the baseline of CNN by 7.5-9.2%. The best performance

is achieved by Rot+Perm+TimeW with 86.88% accuracy. These re-

sults indicate that rotation can be used to alleviate sensor pose

variability while either permutation or time-warping can be em-

ployed for addressing the variability of temporal locations of events

in a window.

Training curves of the experiments are depicted in Fig 4. The

Rot+Perm+TimeW curve shows slow training improvement and a

better generalization performance than others thanks to the regu-

larization eect provided by the data augmentation. Some of the

failed predictions are presented in Fig 5. From the gure, it can be

observed that CNNs often misclassify uctuating bradykinesia and

constant dyskinesia data, which can be considered as seemingly-

noisy labels as described in Section 3.1.

5 CONCLUSION

In this paper, an automatic classication algorithm for PD motor

state monitoring is developed based on wearable sensor data. PD

motor state classication is a challenging task because of large

inter-class variability, noisy labels, interference by irrelevant mo-

tion signals and limited data availability. The challenging PD task

is successfully tackled using a 7-layer CNN and the proposed data

augmentation methods. The combination of rotational and permu-

tational data augmentation methods improves the baseline perfor-

mance of 77.52% accuracy to 86.88%. Systematic experiments with

various data augmentation methods provide a direction towards a

general approach for augmentation for wearable sensor data.

219

Data Augmentation of Wearable Sensor Data for Parkinson’s Disease Monitoring ... ICMI’17, November 13ś17, 2017, Glasgow, UK

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Figures

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