DETECTION OF TOOTHBRUSHING ACTIVITY USING

FREE-LIVING ACCELERATION DATA

udiger Zillmer

Unilever R&D Port Sunlight, Quarry Road East, Bebington, U.K.

Keywords:

Activity monitoring, Free-living, Accelerometer, Classiﬁcation.

Abstract:

The present paper discusses the characterisation of toothbrushing activity, using acceleration data collected

for 50 subjects in free-living conditions. The data logging is triggered by super-threshold values of accelera-

tion, which can give rise to false activations by non-brushing activities. Due to large intra and inter individual

variations, it is not possible to obtain an exhaustive training-set of all activities that trigger the logging. Thus,

a structural analysis of appropriate data features is performed, which reveals a clustering of the data. The

comparison with brushing activity traces from laboratory experiments allows the identiﬁcation of toothbrush-

ing activity, while the remainder corresponds to various false activation events like electronic noise or brush

handling. The distribution of the resulting toothbrushing activity shows distinct peaks for morning and night

brushing activity.

1 INTRODUCTION

The measurement of human activity plays an im-

portant part in medical monitoring, where many ap-

plications consider the detection of certain dynami-

cal states which subsequently allows drawing conclu-

sions about the physical state of the subject. Usu-

ally, activity is measured in a laboratory environment,

where the experiment follows a ﬁxed protocol (e.g.,

5min walk, 5min treadmill etc.) (Preece et al., 2009),

or the user has to label manually the respective aciv-

ity (Bao and Intille, 2004). Due to obvious logistic

problems the sample size is usually restricted to 5 -

20 subjects. The labelling of the data allows the train-

ing and subsequent validation of different classiﬁca-

tion techniques. However, little is known about the

properties of data collected in free-living conditions

for large populations. Typical issues encountered in

ﬁeld experiments are:

• Unpredictable behaviour of the subjects.

• Unexpected noise sources.

• High interindividual variability of activity (see

e.g., (Welk et al., 2007)).

In order to obtain a labeling of the activity, experi-

ments can be combined with video observation (Vega-

Gonzalez et al., 2007). However, it is plausible that

knowledge of being observed changes the individual’s

behaviour. The last issue also effects the quality of

questionnaires, or self-reports, which are often biased

towards the ’desired’ behaviour. This paper consid-

ers an industrial approach to the activity classiﬁcation

based on accelerometer data. The data are taken from

a study conducted by Unilever Oral Care (Claessen

et al., 2008a), which measured the toothbrushing be-

haviour of a large population in Xian (China). The

subjects were given a Sensor brush, a novel device

developed by Unilever, which logged toothbrushing

events over a period of 3 weeks. The study was

originally designed to evaluate the effect of public

health communication, but in the present paper only

the technical aspect concerning activity classiﬁcation

shall be addressed.

The paper is organized as follows. First, the Sen-

sor brush technology is introduced. In Section 3 the

data format and analysis methods are discussed. This

includes results on the classiﬁcation of logged events

together with examples for the different classes.

2 THE SENSOR BRUSH DATA

In order to capture toothbrushing behaviour in free-

living conditions, a logging device, called Sensor

377

Zillmer R. (2010).

DETECTION OF TOOTHBRUSHING ACTIVITY USING FREE-LIVING ACCELERATION DATA.

In Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing, pages 377-380

DOI: 10.5220/0002592403770380

 SciTePress

brush (Figure 1), has been developed by Unilever Oral

Care (Claessen et al., 2008a). The Sensor brush has

the shape of a normal toothbrush with a compartment

in the handle that contains the data logger. The latter

includes a 3-axial accelerometer, memory, and bat-

tery. In order to reduce memory and power usage, the

data logging starts when the accelerometer signal ex-

ceeds a certain threshold, and then continues for 60

seconds. After 60 seconds the logging either contin-

ues if the signal is still above threshold, otherwise it

stops. The data thus consists of 60s traces of the x, y, z

acceleration, sampled at 10Hz, together with the date

and start time of the event.

Figure 1: Sensor brush 3-axial measurement.

The validity and reliability of this technology has

been established in a number of studies (Claessen

et al., 2008b), which conﬁrm that the Sensor brush

is able to detect time and date of brushing events, and

does not inﬂuence the brushing duration when com-

pared to a standard marketed toothbrush.

3 DATA ANALYSIS

As outlined in the previous section, the device starts

to log 60s of the acceleration trace after the signal

exceeds a threshold. The latter is chosen in a way

that no brushing events are lost. As a result, the data

is corrupted by false activations, due to handling of

the brush, opening and closing of cabinet doors, inci-

dental dropping of the brush, internal electronic noise

and many more. Hence, a post-processing of the data

is necessary, which should allow for unknown error

sources. Since there is no sufﬁcient training set avail-

able, the data features are searched for patterns, or

structures, which allow extracting the true brushing

events via an unsupervised clustering technique.

The data considered in the following consist of

logged events (60s of x, y, z acceleration) for 50 male

adults over a period of 3 weeks. Each event is labelled

by time and date, which allows the extraction of the

average intensity of toothbrushing for a given time of

the day. The respective analysis steps are described in

the following.

3.1 Feature Extraction

The signal of the accelerometers is corrupted by noisy

spikes that are removed in a pre-processing step. Each

data sample is then divided into adjacent windows of

3s that contain 3x30 data points for the x, y, z accelera-

tion. Because the sampling frequency is 10Hz, which

is quite close to typical brushing frequencies (between

3 and 5Hz), a principal component analysis is per-

formed to obtain the linear combination of the three

acceleration traces that contains the maximal vari-

ance. This is done for each window i, yielding a set of

(maximum variance) components a

(n), n = 1. . . 30.

The absolute values of ten Fourier coefﬁcients, cor-

responding to frequencies 2, 2.33, 2.67, . . . , 5Hz, are

obtained via fast Fourier transformation of the a

. We

denote these values as s

i,k

, k = 1. . . 10.

3.2 Data Analysis and Classiﬁcation

For the subsequent analysis, the logarithms of the co-

efﬁcients, logs

i,k

, for all subjects are merged to form a

large data matrix S

j,k

, where j labels the respective 3s

window and k = 1. . . 10 indicates the frequency com-

ponent. In order to reduce the dimensionality a princi-

pal component analysis (PCA) is performed on the ten

frequency components. The loadings of the ﬁrst three

principal components, which explain 93% of the total

variance, can be interpreted as follows: PC1 reﬂects

the total variation of all frequencies; PC2 reﬂects the

variation of the power in the 5Hz frequency compo-

nent; PC3 contrasts the power in 4, 4.33, 4.67Hz with

the other frequencies. Because 5Hz corresponds to

the Nyquist frequency, the second component PC2

is excluded from further analysis. More interesting

are the frequencies reﬂected by PC3, which corre-

spond to the typical range of toothbrushing frequen-

cies (Van Someren et al., 1996).

Figure 2: The distribution of the data in the PC1-PC3 plane,

indicating a partition into 3 clusters, C1, C2, C3.

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378

A scatter plot of the data in the PC1-PC3 plane

reveals a partition into three clusters. This is clearly

seen in the contour-plot of the data distribution shown

in Figure 2. The upper right cluster, C1, in Figure 2 is

characterized by high total power and relatively high

power in the frequency components 4, 4.33, 4.67Hz.

This is the expected property of toothbrushing events.

A visual check of the data and a comparison with test

toothbrushing traces (collected in the laboratory) con-

ﬁrms this expectation, such that it can be assumed

that the cluster C1 contains toothbrushing events. Ac-

cordingly, the false activations (due to non-brushing

activity) belong to the clusters C2 and C3. In order

to discriminate the data, a Gaussian mixture model is

used (Press et al., 2007). The result is presented in

Figure 3. The ﬁgure indicates a considerable over-

lap between the clusters, which is a typical feature of

real-life data.

Figure 3: The result of the clustering in the PC1-PC3 plane.

The toothbrushing events are contained in C1.

The majority of the data points belong to clusters

C2 and C3 with 41 and 46%, resp., while C1 con-

tains 13% of the data. Because the Gaussian mixture

model assigns a membership probability to each data

point, the probability of misclassiﬁcation can be esti-

mated. The results shown in the table 1 below sug-

gest that there is a 0.02 probability of misclassifying

a toothbrushing event as false activation (false nega-

tive) and a 0.13 probability of misclassifying a false

activation as toothbrushing (false positive). However,

these numbers are obtained under the assumption that

the Gaussian mixture model correctly describes the

data, while Figure 2 indicates that this is only approx-

imately valid.

In Figure 4 typical examples of logged events be-

longing to the three clusters are presented. The C1

sample shows strong oscillations combined with base-

line changes (due to brush rotations), which is char-

acteristic for toothbrushing activity. The C2 sample

Table 1: The average membership probabilities for the clus-

ters C1, C2, and C3.

classiﬁed as prob. C1 prob. C2 prob. C3

C1 0.87 0 0.13

C2 0 0.98 0.02

C3 0.02 0.01 97

is typical for a logger activation due to exceptional

electronic noise (spikes) that is followed by baseline

noise. An interesting case is the C3 sample: there are

distinct regular oscillations of about 2Hz that might

suggest a brushing activity. However, the small am-

plitude of the oscillations and the absence of baseline

shifts indicate a false activation, probably due to static

vibrations generated by a washing machine or similar.

Figure 4: Examples for accelerometer signals for (from top

to bottom) cluster C1 (toothbrushing, 1:00am), C2 (false

activation due to electronic noise), C3 (false activation due

to external perturbation).

An interesting property is the distribution of

logged events over the day. The result for the three

clusters is shown in Figure 5. The brushing activity

has distinct maxima between 7 and 8am and around

10pm, and minima between 3 and 4am and around

5pm. The prevalence of brushing in the morning

is typical for the group considered (see (Zhu et al.,

2005)). There is a small peak around 12 noon that

indicates toothbrushing activity after lunch. The non-

brushing events in C2 and C3 have a more ragged dis-

tribution, which is in general well correlated with the

brushing events. This is not surprising, since tooth-

DETECTION OF TOOTHBRUSHING ACTIVITY USING FREE-LIVING ACCELERATION DATA

379

brushing is usually accompanied by other activities in

the bathroom that are likely to trigger a false logging

event. There is a pronounced maximum between 7

and 8pm, which might be caused by a regular rou-

tine (e.g. washing after work), but this can only be

guessed due to the lack of further information.

Figure 5: The normalised distribution of logged events over

the day for the three groups C1, C2, and C3.

4 CONCLUSIONS

The present paper discusses the characterisation of

toothbrushing activity, where the data consists of ac-

celeration traces logged by a sensor that is integrated

in the brush. Because the data has been collected

under free-living conditions for a large population, it

was not possible to obtain an exhaustive training-set

of all possible super-threshold activities that trigger

the logging. Thus, a structural analysis of appropri-

ate data features is performed, which reveals a parti-

tion into three clusters. The comparison with brush-

ing activity traces from laboratory experiments allows

assigning one cluster to toothbrushing activity, while

the remainder corresponds to various false activation

events like electronic noise or handling of the brush

in the context of other activities.

An inherent property of real-life activity data is

the enormous variability, both within and across sub-

jects. Here, this is reﬂected by an overlap between the

clusters found in the data, that leads to a 13% classi-

ﬁcation uncertainty for toothbrushing events. A pos-

sible remedy for this problem is to collect more vari-

ables (e.g., rotation rate) at higher sampling frequen-

cies, which, however, is restricted by memory and en-

ergy consumption issues in the logger design. An im-

portant point is that the logger should not inﬂuence

the normal behaviour of the subjects while being in

the ﬁeld for several days.

It would be interesting to gather more information

about the possible sources of false activation and their

acceleration patterns. This will require elaborate lab-

oratory experiments that take into account learnings

from ﬁeld experiments.

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