W e arab l e C o m pu t i n g

Sensor Placement
Variations in Wearable
Activity Recognition
How do placement variations in user-carried electronic appliances
influence human action recognition? The authors categorize possible
variations, present a systematic evaluation about their impact on humanaction recognition, and discuss ways to compensate for such variations.

We’re currently witnessing
an explosion in sensorequipped, on-body consumer
devices, from smartphones toGoogle Glass to fitness trackers. In general, the quality of the sensors embedded in these devices is similar to the ones typicallyfound in the dedicated wearable sensing systems
used in activity recognition
research, but there’s one majordifference. With a few exceptions,1,2 the bulk of research in
Kai Kunzehuman activity recognition to
Osaka Prefecture University
date assumes known fixed sensor locations, which are often
Paul Lukowicz
carefully chosen to suit a parTechnical University of
ticular application. In contrast,
Kaiserslautern Germany
mobile consumer devices typically aren’t firmly fixed to thebody—they’re placed in a pocket or bag, where
they can shift around and rotate into different orientations. Even devices specifically designed for
concrete placement such as glasses or bracelets
might at times be carried in a pocket or bag.
Motivated by these considerations, recent research interest has focused on understanding
the influence of device placement variations on
activity recognition systems. However, so far,that research is fragmented, with most work
focusing on individual, narrow problems andspecialized methods. An activity recognition

2

PER VA SI V E computing r­ esearcher trying to understand how to impro­ve
his or her system with respect to placement
variations would need to work through a lot of
papers with no overall conceptual framework.
This article aims to change that by providing asystematic understanding of the type and impact of variations, describing a comprehensive
set of methods to deal with the different variation types, and illustrating how such methods
work and are designed and evaluated.

Understanding Placement Variations
Generally, we can distinguish between three
types of device placement variations: on-bodyplacement, within-body displacement, andorientation.
On-Body Placement
Depending on preference, people carry devices
on different parts of their body. Variations
based on these differences are usually coarse—
for example, the device might reside in a front
or back trouser pocket, a jacket pocket, an arm
or a hip holster, or a bag.3 Changes in on-bodyplacement aren’t as frequent as within-body displacements or changes in orientation—but eventhough several devices are associated with aparticular body placement (for example, glasses
are associated with the eyes), changes can still
occur (the user might put her glasses in a shirt
pocket or carry them in her bag).

Published by the IEEE CS n 1536-1268/14/$31.00 © 2014 IEEE

Displacement within a Body Part
Depending on the specific piece ofclothing, a device can move within apocket or holster and thus might be at aslightly different location. In addition,a device can shift around if it is significantly smaller than the pocket or if theholster itself can shift (see ­Figure 2).
The problem of “within body part”
variations differs in three ways fromthe problem of inferring on which bodypart a device is placed:• The placement variations are continuous rather than discrete.
• The variations are mostly small, usually not more than a few centimeters
and at most 10 to 20 cm.
• The placement is likely to change
more frequently as the device shifts
around. Therefore, recognizing thewithin-body location is a difficult
problem, making understanding how
placement affects the sensor signal
and defining features that are tolerant to it a better approach.
To investigate the impact of withinbody displacement, we conducted experiments in two gym scenarios: one studying
locomotion and the other arm exercises.4
For each scenario, we used four Xsense
inertial measurement unit (IMU) sensors
uniformly and randomly distributed overthe upper leg for the locomotion study
and the upper arm for the arm exercises

october–december 2014

10

Torso
PocketAcceleration

5

0

–5

–10
Time

(a)
70
60
Percentage

The influence that the body part onwhich the device is placed has on signals from motion sensors can be twofold: one, some activities are associatedwith specific body parts and sensors,
such as activities related to subtle arm
motions (washing hands), and two,even for activities that aren’t strictlybody-part specific, the motion sensor
signals vary significantly from body location to body location (see Figure 1a).
A device’s on-body location also
­impacts sound. As Figure 1b indicates,
differences arise less from body damping
and more from the absorption spectra of
the clothing on which a device is placed.

50
VelvetJeans
Backpack
Leather
Woll

40
30
20
500

1,000

1,500

2,000

2,500

Frequency/Hz(b)
Figure 1. Impact of device placement on signals from different types of sensors.
(a) Acceleration signals recorded from two different body locations while walking.
(b) The sound absorption spectra of different types of clothing recorded withan iPhone microphone. The graph shows signal intensity as a percentage of theintensity recorded with a device placed on a table and not obstructed by clothing.

study. The locomotion exercises included
eight classes: walking, running, running
uphill, biking, rowing, walking up stairs,skiing, and cross-training. The arm exercises included using the lat/pectoral
machine to do shoulder presses, upperback stretches, arm extensions/curls, pull
downs, and chest presses. Two test subjects performed two circuits, with 20 repetitions of each exercise.
Using just accelerometer data fromthe same location for training (34percent) and testing (66 percent), we
achieved 100 percent recognition for the
locomotion scenario and 96 percent forarm exercises. However, when testing
and training on a different location, theperformance dropped to 63 and 24 percent, respectively (see Table 1). Training
the system on two different locations
improved the recognition rate, but only
slightly, to 61 and 31 percent.
Orientation
The final displacement issue is the device orientation with respect to theuser’s body. As an example, consider amobile phone in a hip holster. In mostcases, you can put the device into theholster in at least two ways: display

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Table 1
Classification results for displaced sensors in a simple gym experiment.
Modality

Trained andtested on same
location

Trained on one
location and­tested on anotherTrained on two
locations prior to
testing

Acceleration

100%

33%

35%

Gyroscope

65%

43%

44%

Cut off*

—

42%

47%

Combined†

—

78%

85%

*Just the acceleration features, but ignoring parts where displacement was detected†Mixes acceleration features for translation and gyroscope from rotation-dominated body parts“What influence does rotation have on
motion signals?” but rather “How muchinformation is lost when discarding orientation-sensitive features?” Dependingon the antenna/microphone, the device’sorientation on the body can dampen thesignals for GPS, Wi-Fi, and sound.

body means that the sensor can be used
to determine the direction in which theuser is facing. In turn, this can be used toinfer the user’s focus of attention (such as
facing a specific shelf in a store, thereby
indicating interest in a certain product).
More details appear elsewhere.5

Signal damping. Fixed rules for the influence of orientation on signal damping
are difficult to formulate because theeffect strongly depends on device andclothing configuration. Consideringsound, signal dampening can increase
if the microphone is oriented toward
the user rather than the environment(see Figure 3).

Mitigating Placement
Variations

Loss of orientation information. To show

Figure 2. An example of “within bodypart” displacement. The placement of
the device, although located on the arm,
can shift up or down.

facing inward or display facing outward; you might also have the optionof ­attaching the holster either verticallyor horizontally, or be able to place theholster at various locations on the hip,which would determine its exact horizontal orientation.
In most cases, motion sensor-enabled
devices are equipped with three-axis sensors, letting us use the signal’s norm asa simple orientation-invariant signal. In
this case, the interesting question is not

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PER VA SI V E computing 

what kind of information is lost when
discarding orientation, we used the standard locomotion recognition problem:
distinguishing between walking upstairs,
walking downstairs, running, or walking on a level surface. In Figure 4, we
use Euclidean distance as a measure oftime-series similarity to compare how
well those classes are separated by thenorm and any orientation-sensitive signals from an accelerometer signal’s three
individual axes. Even for simple classes(which can actually be recognized reasonably well using the norm), the orientation-sensitive features contain a lot
more information.
On its own, device orientation is particularly relevant for systems containing
a magnetic field sensor, as is i­ ncreasingly
the case with modern smartphones.
Knowing the orientation of the magnetic
sensor axis with respect to the user’s

We’ve discussed the types of sensor displacement and their effect on varying recognition modalities, so now we build on
this conceptual framework to present acomprehensive set of methods for dealing with such effects. We don’t provide aclassical, exhaustive survey: there’s a lotof current interest in this topic but notmuch work in the field yet. Our goal isto help activity recognition researchers
make their systems more robust with respect to sensor displacement. For practical reasons, we provide examples fromour own work to illustrate key concepts,
but we take care to include relevant related ­approaches by others as well.
Addressing On-Body Variations
To mitigate the influences of on-bodyplacement, we can apply either location-independent features or body partplacement recognition.
Location-independent features and classifiers. For activity recognition applica-

tions, two strategies are commonly used
to achieve location independence.
First, for some recognition tasks, we
can compute features that are independent of the body part on which the­device is placed. For detecting walking,www.computer.org/pervasive

45

55

Front up
Front down
Back

40

45

35

40

30

35
Percent

Percent

Front up
Front down
Back

50

25
20

30
25
20

15

15

10

10
5
500

1,000

1,500

2,000

500

1,000

Frequency

1,500

2,000

Frequency

(a)

(b)

Figure 3. The influence of orientation on the audio signal absorption spectrum for the (a) Nokia 81 and (b) iPhone 3gs.
Depending on where the microphone is located on the device, a different orientation will cause it to be more or less obstructedby the body or clothing.

october–december 2014

3.5
Norm
All axis

3.0
Relative euclidian distancefor example, strong peaks between 1
and 4 Hz in the signal’s frequency spectrum are present in all body locations.
Our previous work shows how to detect
walking independent of body location
with up to 95 percent accuracy using
the acceleration signal norm.6 Other
work focuses on how to use sensor fusion methods to achieve robust recognition.7,8 As expected, these methods
require significantly larger, more representative datasets for training compared to the non-robust alternative.
Second, classifiers can be trained on
a combination of different locations.
Thus, the classifiers automatically select a feature set that’s as independent
as possible of the sensor location.9 One
study7 examined such an approach for a
multimodal sensor system that included
different sensors (motion, sound, light),
three different body locations (shoulder,wrist, hip), and eight everyday activities(different modes of locomotion). The
classifiers trained and tested on different locations had up to 20 percent
worse recall and precision than the ones

2.5
2.0
1.5
1.0
0.5
0.0

Stairsdown - run

Stairsup - down

Stairsup - walk

Stairsup - run

StairsWalking - run
down - walk

Figure 4. The value of the orientation information for motion sensor-based activity
recognition, depicting the difference between recordings for different pairs of modesof locomotion as expressed by the vector norm (an orientation-invariant feature) of
the acceleration on the upper leg and the corresponding three vector components.
Note that the vector components can only be used if the device orientation is known.

trained and tested on the same location.
A classifier trained on all three locations
was only a few percent worse than thelocation-specific classifiers.

We investigated a similar approachwith respect to sound classification indifferent pockets.10 In the experimental setup, we considered sound fromPER VA SI V E computing5

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phone is in a pocket by utilizing audiowith a multiround classifier method.12

100
75

50
25
0

Jeans

Leather jacket Golfing trousers

Trained clean/majority
Trained clean/frame by frame

Sports cost

Belt holster

Trained mixed/majority
Trained mixed/frame by frame

Figure 5. Classification results for audio from nine household and office appliances,
showing the effect of sound dampening on accuracy. “Trained clean” refers to training
with the phone being held in one’s hand without absorption through clothing.
“Trained mixed” refers to training on data from different pockets. “Majority” implies amajority decision over all frames in a recording instance.

nine household and office appliances
recorded with an iPhone placed in two
different trouser pockets, two different jacket pockets, a belt holster, andthe user’s hand. If we trained and tested
the device at the same location, the recognition results were 99 percent (seeFigure 5). If we trained the system withthe device in hand—that is, without absorption through clothing (designated“clean” in Figure 5)—and tested it ondifferent pockets, then the performance
went down to around 60 percent on aframe-by-frame basis, and around 70
percent for the majority decision over all
frames in a recording instance. By training on data recorded from different typesof pockets, the system learns to deal withabsorption through clothing, and performance can be improved to between 80
and 90 percent on a frame-by-frame basis
and 100 percent for the majority decision.
Body placement recognition. Clearly,
placement-invariant features and mixed
training works only if there is sufficientsimilarity between signals from different
body locations. However, if the signals
differ sufficiently, we can automaticallydetect device placement. In previous

6

PER VA SI V E computing work, we showed that such recognitionis possible using the acceleration signal
alone.6 We were able to detect different
on-body placement for torso, hand, leg,and head by relying on the fact that theseparts all move with different frequency
and along different distinct axes.
Leonard Grokop and his colleagues
pursued a different approach to location recognition by using a fusion of­acceleration, proximity, and light sensors to jointly recognize simple activities
and on-body locations.11 Thus, walking
with the sensor in a pocket is a separate
class from walking and having the sensor in the hand, reflecting the fact thatboth are separate events in signal space.
A disadvantage of this approach is the
explosion of the state space.
Related research focuses on phone
placement sensing. Jason Wiese and hiscolleagues extended the work to detecting a phone’s on-body location underreal-life scenarios by utilizing accelerometer data and a capacitive sensor prototype. They also explored where people
usually wear phones to adjust priors on
different locations.3 Emiliano Miluzzo
and his colleagues presented the Discovery system, which can detect whether a

Addressing Within-Body
Displacements
Features tolerant to within-body displacement of gyroscope and acceleration sensors 4 can be derived byassuming a rigid body idealization.
Neglecting deformation assumes the
distance between any two given points
to be constant regardless of the external
forces exerted on the object in question.
As Figure 6 shows, a rigid body canmove in two ways: through translationand through rotation:• During translation, all points in arigid body move at exactly the same
speed and acceleration. Thus, signals
from accelerometers are location invariant. Because a translation per definition contains no rotational component, gyroscopes produce no signal.
• During rotation, each point in a rigid
body moves with the same angularvelocity and acceleration. The gyroscope signal is the same for all points
within the body, but the acceleration
signal is placement dependent.
• In addition to the dynamic components resulting from motion, accelerometer signals contain a component
related to gravity that’s effectively
identical at all places on an object
and is thus invariant to displacement
within body parts. It does, however,depend on sensor orientation.
From these facts, we can draw threeconclusions:• When motions are dominated either
by translations or changes in orientation with respect to gravity, we canuse acceleration features.
• When motions are dominated by­rotations, we should avoid acceleration features; gyroscopes provide­information that’s invariant to bodypart displacement.
• When motions contain an equalamount of rotation and translation,www.computer.org/pervasive

θ1
p1

θ2
θ3
p1

p2
d 1,2

d 1,3

p3

d2,3
α 1,3
α 1,2 α 2,3
r2 r3
r1

p1

d 1,2 d 1,3p2 d2,3 p3
α 1,3

d 1,3

p3

d 2,3

x1

α1,2
α 2,3
r1 r2
r3

(a)

d 1,3
p2
x2

p3

d1,3

p1
d 1,2

(b)

x3

p2

d 2,3

(c)

Figure 6. Rigid body approximation: (a) rotational and (b) translational motion of different points within a rigid body. It can beseen that a translational motion moves all points within a rigid body by the same distance, which means that they all have thesame speed and acceleration at all times. By contrast, during rotational motion, (a) different points have different degree ofdisplacement, which leads to different acceleration values (acceleration signal is location dependent).

we should still pick the gyro f­ eatures—
although we might lose some information, we’ll retain the shift invariance.
Unfortunately, arbitrary combinationsof rotations and translations with avarying vertical component are still
likely to occur. Consequently, static
optimization will have only a limited
effect on a typical recognition application’s displacement invariance. To handle this, we need to choose the correctfeatures dynamically, depending on thekind of motion being performed.
In previous work, we showed how toaccomplish this kind of dynamic selection,4 using the ratio of the acceleration
vector norm to the angular velocity vector norm to determine if a signal frame
is dominated by translation or rotation.
As described earlier, translations generate no gyro signal, so the more the ratio
is tilted toward the acceleration norm,
the more translation-dominated the signal frame.
When applying this method to thegym experiment, we picked the decision boundary to be 150 degrees per
second. Above this boundary, we usedgyro features and set the accelerometer features to zero; below it, we discarded the gyro features and used theaccelerometer features only. We madethis decision for every data point at
a 100-Hz sampling rate. As Table 1

october–december 2014shows, significant improvement canbe achieved.
The standard practice for dealing withdisplacement is to take robust ­aggregatefeatures. However, in general, such features only work for very simple recognition tasks. For more complex tasks,
they lead to a significant degradationin recognition performance and requirelarger training ­datasets.7,8 Navid Aminiand his colleagues used robust rotation
features to compensate for displacement while detecting a specific medical device’s on-body location.13 Kristof
Van Laerhoven and colleagues trained
recognition models to be adaptive tosmall placement issues. The classes weretrained as user-dependent, under the assumption that a particular displacement­happens more often with a specific user,
one drawback being that this training­required direct user feedback.9
K. Forster and his colleagues and
H. Sagha and his colleagues regarded
displacement as a continuous tracking problem.14,15 The Sagha study also­detected anomalies in the recorded datathat the inference algorithm should disregard, but for their algorithm to work,
it had to know the device’s initial exact
position on the body.
Addressing Orientation Variations
Estimating a device’s orientation with respect to a user’s body involves two distinct

subproblems: vertical orientation (the angle with respect to the gravity vector) andorientation in the horizontal plane.
As first proposed by David Mizell andhis colleagues, vertical orientation canbe estimated when the object experiences no change in motion speed.16 In
this case, the only acceleration registeredby the sensor is the Earth’s gravity (9.81m/s2), and the direction of the measured
acceleration vector defines the verticalplane. To identify signal segments withthe above characteristics, the norm of
the measured acceleration vector is used
together with its variance. When variance of all axes tends toward 0 and thenorm vector approaches 9.81 m/s2, thesignal is very likely to be dominated by
the vertical orientation component. In
theory, this might not necessarily betrue: the object could be freely falling(and therefore, lack a gravity component) while experiencing a constant
9.81 m/s2 along an arbitrary direction.
To derive the horizontal orientationof a body-worn accelerometer from asignal segment recorded while a user iswalking, we have presented a method25
based on the fact that, while walking,most variations in the acceleration signal’s horizontal component are likely to
be parallel to the direction of motion.
Thus, we project the signal into theplane perpendicular to the vertical gravity vector (that is, the horizontal plane)

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Angular velocity

Acceleration

Frame-by-frame
walkingrecognition

fusion-based approach,19 while Ming Jiang and colleagues calculated a transformation matrix by using G­ ram-Schmidt
orthonormalization to eliminate thesensor’s orientation ­­error and then­employing a low-pass filter with a cut-off
frequency of 10 Hz to eliminate the main
effect of the sensor’s misplacement.20
Additional dedicated work is in thearea of modeling orientation variations
in garment-integrated sensors.21,22 The
research here focuses on exploiting garment properties to estimate orientation
changes and displacement.

Rest period?

Yes
SmoothingNo

Detecting On-Body Placement
Calibration

No

Walking?
Feature extraction
Yes

Frame-by-frame
placement

Majority decision

HHM
classification

Particle filtersmoothing

Figure 7. Performance of particle filtering for the respective datasets over time. It canbe seen that the system needs approximately two minutes to reach its peak accuracy.

and apply principal ­component analysis to determine the direction in which
the variation is greatest. To distinguish­between front and back, we consider the
integral of the signal over time.
Other Approaches
Most other methods for dealing withorientation changes focus on using ­orientation-robust features. Forster and colleagues describe how to
track orientation and displacementchanges over time, but they assume
known ­sensor positions at the beginning.14 ­Ricardo Chavarriaga and his

8

PER VA SI V E computing ­c olleagues 17 propose a similar yet
more elaborate ­approach, an unsupervised adaption method that can compensate for small sensor displacements
and orientation changes over time (thisapproach can’t handle large changes,
such as a user taking out his mobile
phone and putting it back in a different orientation).
Surapa Thiemjarus and his colleagues
defined device orientation as a classification problem,18 implying that eachdifferent device orientation required its
own training data. Oresti Banos and hiscolleagues presented a weighted sensor

Up to now, we’ve merely sketched possible solutions and methods (spacedoesn’t permit a more detailed discussion). To provide some deeper insight,
we pick a specific example and detail it
here—specifically, recognition of bodypart location—and by extending ourown prior work,6 we describe how arecognition system looks in detail.
Different body parts vary in degrees of
freedom and move in different patterns.
We evaluated more than 35 features andfound that six best capture those differences. The first three are standard deviation, zero crossings, and mean of thenorm of the acceleration vector minus
the gravitational pull g0:
x2 + y2 + z 2 − g0 .
The fourth is the sum of the norm of thedifferences in variance for the normalized axes a1, a2 , a3 divided by the variance of the vector norm
n

1 2∑

n

∑

i = 1 j = 1, j < ivar(ai ) − var(a j )var(norm).

The fifth is the number of peaks in theabsolute value of the three axes derived
using hill climbing with a threshold; thesixth is the median of these peaks.
We computed these features over a
2.5-sec jumping window (overlapping
1.25 sec); we then applied another sixminute window on top of the alreadywww.computer.org/pervasive

windowed features and fed them into
a continuous hidden Markov model(HMM) with five hidden states.
We evaluated the system on fivedatasets:

The maximum accuracy achieved
for the six-minute windows was
82 ­percent, with roughly 80 percent
at five minutes, as depicted in Figure 7.
Because we recorded unconstrained
real-life activities for our experiments,
the dataset contained many segmentsthat had no significant motions characteristic of body parts that we could
use for classification, which explains
the relatively low recognition rate. If
we took just walking segments into
account, for example, the recognition
rates improve to 94 percent.6
To improve our on-body placement
recognition work, we tried several filtering approaches. As a majority window is too crude to filter out uncharacteristic movements, we applied asequential Monte Carlo method, also
called a ­particle filter; the basic method

october–december 2014

0.8

0.6
Accuracy• Household work. We gathered six
hours of real-life activities from a
70-year-old housewife, a 50-year-old
female office worker, and 28-yearold male student.
• Opportunity. The largest dataset recorded as part of the Opportunity EU
project includes activities from everyday living such as making a sandwich,pouring coffee, and eating. Two users
repeated the setup five times.23• Drink and work. The drink andwork dataset contains mostly sitting
activities, such as working on a computer and consuming food and drinks.
In total, we studied six subjects withfour repetitions each; one experimental run took around 30 to 40 minutes.
• Bicycle repair. The last experimental
setup included repair activities on abike (attaching a tire, tightening screws,
and so on) with six test subjects.24• Office Work. A set of simple officeactivates such as typing, photocopying, or participating in a meeting.

1.0

0.4

Opportunity (accel)
BicycleHouse work
Opportunity (3 sensors)
Drink and work

0.2

Office work
0.0

0

2

8

4
6
Time in minutes

Figure 8. Method overview for the on-body placement recognition based on walking(left) and unconstrained using HMMs/particle filtering (right).

Table 2On-body recognition rates (%).
Head

Wrist

Torso

Front pocket

Back pocket

Head

74.0

1.0

12.5

0

12.5

Wrist

0

87.2

6.4

2.6

3.8

Torso

7.1

5.9

84.7

0

2.4

Front pocket

0

2.1

14.9

37.2

45.7

Back pocket

0

2.0

11.9

2.0

84.2

for the HMM recognition remained thesame. When we applied the particle
filtering, we could reduce the sliding
window for each individual HMM to
45 seconds. We input these 45-second“sliding window” HMM classifications as observations into a particle filter. To our knowledge, particle ­filteringhasn’t been applied to this type of activity-sensing problem. Figure 8 gives
an overview of all methods used.
To test our filter design, we randomly (uniformly) picked 100 10-minute segments from the datasets (there
were duplicates), did feature extractionand HMM classification, and then fed

them into the particle filter. Afterward,
we evaluated what percent of the 100
filtered placements we detected correctly. Figure 7 shows the results forthe different datasets. As can be seen,
the office work scenario performed
best (using gyroscope and accelerometer), and the opportunity dataset wassecond (­ gyroscope and accelerometer).
House work started off quickly (accel+ gyro), but didn’t achieve the average
90 percent accuracy and had the most­variance from one filtered classification to the other. For all datasets, wereached a recognition rate of more than90 ­percent after 2 to 4 minutes.

PER VA SI V E computing9Wearable Computing

25
Back

Accuracy

20

Knee

15

Upper arm

10

Wrist

5

Ground truth
Hmm
Particle filter

Hand0
Lower arm

Lower leg

Head

Back

0

Placement

(a)

2,000

4,000
8,000
Samples

12,000(b)

Figure 9. On-body location recognition using HMMs and particle filter smoothing: (a) Mean and standard deviation of thetime it takes before the particle filter recognizes the correct placement using 100 segments between 10 and 20 minutes,
uniformly randomized for the opportunity dataset. (b) Scatter plot depicting HMM performance with and without particle
filter smoothing; 20-minute segments taken from the opportunity dataset accelerometer only, with 45-sec. HMM classification(around 59 percent correct) and achieving a 78 percent particle filter with 40 particles.

The recognition for body parts withless movement didn’t perform that well(head and torso), as can be seen fromthe confusion matrices in Table 2 andFigure 9a. The classification was worse
for the HMMs alone, but it can be significantly improved by applying theparticle filter (Figure 9b).

W

e discussed many issues here with respect
to smartphones andmobile appliances, but
our points are also relevant for othertypes of wearables—in particular, thebody part shifts and rotations that are
well-known problems for smart textiles, which are never firmly fixed tothe body (sleeves may be rolled up or
sweatshirts carried around the hip orin a backpack).

10

2. K. Forster et al., “Evolving Discriminative
Features Robust to Sensor Displacement for
Activity Recognition in Body Area Sensor
Networks,” Proc. 5th Int’l Conf. Intelligent
Sensors, Sensor Networks and Information
Processing (ISSNIP), 2009, pp. 43–48.
3. J. Wiese, T. Scott Saponas, and A.J.
Brush, “Phoneprioception: Enabling
Mobile Phones to Infer Where They Are
Kept,” Proc. SIGCHI (CHI 13), 2013,
pp. 2157–2166.

10. T. Franke et al., “Can a Mobile Phone
in a Pocket Reliably Recognize Ambient
Sounds?,” Proc. Int’l Symp. Wearable
Computers, 2009, pp. 161–162.
11. L.H. Grokop et al., “Activity and Device
Position Recognition in Mobile Devices,”
Proc. UBICOMP, 2011, pp. 591–592.

4. K. Kunze and P. Lukowicz, “Dealing with
Sensor Displacement in Motion-Based
On-Body Activity Recognition Systems,”
Proc. UBICOMP, 2008, pp. 20–29.

12. E. Miluzzo et al., “Pocket, Bag, Hand,
etc.: Automatically Detecting Phone Context through Discovery,” Proc. PhoneSense, 2010, pp. 21–25.

5. B. Steinhoff and U. Schiele, “Dead Reckoning from the Pocket: An Experimental
Study,” Proc. PerCom, 2010, pp. 162–170.

13. N. Amini et al., “Accelerometer-Based
On-Body Sensor Localization for Healthand Medical Monitoring Applications,”
Pervasive and Mobile Computing, vol. 7,
no. 6, 2011, pp. 746–760.

6. K. Kunze et al., “Where Am I: Recognizing On-Body Positions of Wearable Sensors,” Location and Context Awareness,
Lecture Notes in Computer Science, vol.
3479, 2005, pp. 264–275.

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the Authors
Kai Kunze is a research assistant professor in the Intelligent Media Processing
Group at Osaka Prefecture University. His research includes the exploration ofknowledge acquisition tasks, combining computer vision and other embedded
sensing approaches with a focus on reading activities and learning behavior.
Kunze received a PhD from University Passau, Germany. Contact him at kai.
kunze@gmail.com.

Paul Lukowicz is a professor of artificial intelligence at the Technical Universityof Kaiserslautern Germany, and scientific director for embedded intelligence at
the German Research Center for Artificial Intelligence (DFKI). His research interests are in wearable systems, ubiquitous and crowdsourced sensing, activity
and context recognition, sociotechnical systems, and self-organization. Lukowicz received a PhD in computer science from the University of Karlsruhe. He
is a member of the editorial board of the Hindawi Advances in Human Computer
Interaction and IEEE Journal of Biomedical and Health Informatics. Contact him atpaul.lukowicz@dfki.de.

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Troster, “Modeling and Simulation of
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Opportunistic Activity and Context Recognition Systems,” Proc. Int’l Symp. Worldof Wireless, Mobile and Multimedia Networks (WowMoM), 2009, pp. 1–6.

22. G. Gioberto and L.E. Dunne, “Garment Positioning and Drift in GarmentIntegrated Wearable Sensing,” Proc.
Int’l Symp. Wearable Computers, 2012,
pp. 64–71.

24. G. Ogris et al., “Continuous Activity Recognition in a Maintenance Scenario: Combining Motion Sensors and ­Ultrasonic Hands
Tracking,” Pattern Analysis Applications J.,
vol. 15, no. 1, 2012, pp. 87–111.

october–december 2014

25. K. Kunze et al., “Which Way Am I Facing:
Inferring Horizontal Device Orientationfrom an Accelerometer Signal,” Proc. Int’l
Symp. Wearable Computers (ISWC 09),2009, pp. 149–150.

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