Pick-by-Vision: A first stress test
ABSTRACT In this paper we report on our ongoing studies around the application of augmented reality methods to support the order picking process of logistics applications. Order picking is the gathering of goods out of a prepared range of items following some customer orders. We named the visual support of this order picking process using head-mounted displays ldquopick-by-visionrdquo. This work presents the case study of bringing our previously developed pick-by-vision system from the lab to an experimental factory hall to evaluate it under more realistic conditions. This includes the execution of two user studies. In the first one we compared our pick-by-vision system with and without tracking to picking using a paper list to check picking performance and quality in general. In a second test we had subjects using the pick-by-vision system continuously for two hours to gain in-depth insight into the longer use of our system, checking user strain besides the general performance. Furthermore, we report on the general obstacles of trying to use HMD-based AR in an industrial setup and discuss our observations of user behaviour.
- SourceAvailable from: Frerk Saxen[Show abstract] [Hide abstract]
ABSTRACT: In this paper, a mobile assistance-system is described which supports users in performing manual working tasks in the context of assembling complex products. The assistance system contains a head-worn display for the visualization of information relevant for the workflow as well as a video camera to acquire the scene. This paper is focused on the interaction of the user with this system and describes work in progress and initial results from an industrial application scenario. We present image-based methods for robust recognition of static and dynamic hand gestures in realtime. These methods are used for an intuitive interaction with the assistance-system. The segmentation of the hand based on color information builds the basis of feature extraction for static and dynamic gestures. For the static gestures, the activation of particular sensitive regions in the camera image by the user’s hand is used for interaction. An HMM classifier is used to extract dynamic gestures depending on motion parameters determined based on the optical flow in the camera image.Journal of Intelligent Learning Systems and Applications 08/2014; 6(3):141-152.
- [Show abstract] [Hide abstract]
ABSTRACT: There has been a rapid increase in research evaluating usability of Augmented Reality (AR) systems in recent years. Although many different styles of evaluation are used, there is no clear consensus on the most relevant approaches. We report a review of papers published in International Symposium of Mixed and Augmented Reality (ISMAR) proceedings in the past decade, building on the previous work of Swan and Gabbard (2005). Firstly, we investigate the evaluation goal, measurement and method of ISMAR papers according to their usability research in four categories: performance, perception and cognition, collaboration and User Experience (UX). Secondly, we consider the balance of evaluation approaches with regard to empirical-analytical, quantitative-qualitative and participant demographics. Finally we identify potential emphases for usability study of AR systems in the future. These analyses provide a reference point for current evaluation techniques, trends and challenges, which benefit researchers intending to design, conduct and interpret usability evaluations for future AR systems.Interacting with Computers. 11/2012; 24(6):450-460.
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ABSTRACT: This paper presents the design and evaluation of ProFi, a PROduct FInding assistant in a supermarket scenario. We explore the idea of micro-navigation in supermarkets and aim at enhancing visual search processes in front of a shelf. In order to assess the concept, a prototype is built combining visual recognition techniques with an Augmented Reality interface. Two AR patterns (circle and spotlight) are designed to highlight target products. The prototype is formally evaluated in a controlled environment. Quantitative and qualitative data is collected to evaluate the usability and user preference. The results show that ProFi significantly improves the users' product finding performance, especially when using the circle pattern, and that ProFi is well accepted by users.Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication; 09/2013
Pick-by-Vision: A First Stress Test
Bj¨ orn Schwerdtfeger∗
Technische Universit¨ at M¨ unchen
Willibald A. G¨ unthner‡
Irina B¨ ockelmann∗∗
Otto-von-Guericke Universit¨ at Magdeburg
Johannes T¨ umler‡‡
In this paper we report on our ongoing studies around the appli-
cation of Augmented Reality methods to support the order picking
process of logistics applications. Order picking is the gathering of
goods out of a prepared range of items following some customer
orders. We named the visual support of this order picking pro-
cess using Head-mounted Displays “Pick-by-Vision”. This work
presents the case study of bringing our previously developed Pick-
by-Vision system from the lab to an experimental factory hall to
evaluate it under more realistic conditions. This includes the exe-
cution of two user studies. In the first one we compared our Pick-
by-Vision system with and without tracking to picking using a pa-
per list to check picking performance and quality in general. In a
uously for two hours to gain in-depth insight into the longer use of
our system, checking user strain besides the general performance.
Furthermore, we report on the general obstacles of trying to use
HMD-based AR in an industrial setup and discuss our observations
of user behaviour.
PRESENTATION]: Multimedia Information Systems—Artificial,
augmented, and virtual realities; Evaluation/methodology; H.5.2
[ INFORMATION INTERFACES AND PRESENTATION]: User
Interfaces —User-centered design;
H.5.1 [ INFORMATION INTERFACES AND
The basic conditions in the field of logistics have changed rapidly
over the last years, with the market demanding customized prod-
ucts. As an example, 20 years ago automotive manufacturers of-
fered three model series, while nowadays they are offering nearly
ten. This is attended by an increase of the variants within one se-
ries. Thus, production and logistics systems have to become supra-
adaptive .This requires that production processes as well as sup-
porting IT systems be designed in a manner that enables workers to
quickly handle new working conditions and environments. Thereby
exists the need to make workers improve under such working con-
ditions, without increasing their stress level, and while preventing
them from making errors. This requires systems that support the
worker with just the right information at exactly the right time.
Such supporting systems have to provide detailed working instruc-
tions which have to be presented in a highly intuitive and precise
way. As a result, workers can then start executing arbitrary jobs
efficiently and error-free – and with minimal prior training. Aug-
mented Reality (AR) is adjudged to have the potential to provide
this very functionally.
Our use case in this context is the order picking process of logis-
mounted Display (HMD)-based visualizations (both AR and non-
AR) to support the order picking process. We named this HMD-
based support of the order picking process “Pick-by-Vision”.
Figure 1: A photograph through the HMD of the Pick-by-Vision (AR)
system ( compare fig. 4). The visualization shows a Tunnel twisting
to the left, with a square Frame at the end highlighting the label under
the box in a warehouse from which a worker has to pick items. The
article number and the amount to pick are shown at the top of the
There exist several obstacles when trying to apply AR to real life
scenarios. In fact, most AR systems remain laboratory prototypes,
mainly due to the lack of adequate hardware, but also due to not
yet resolved usability issues . Furthermore, we have to deter-
mine where the user can benefit from AR, while at the same time
not knowing how good our AR user interface is. Thus, we must
verify usability to determine if the system is effective , which
is tough as we develop user interfaces at the limit of what is known
or common practise in our field.
This goes along with the problem, that the utility of an AR ap-
plication heavily depends on how good it fulfills the user’s needs.
The Problem of Executing AR Evaluations
IEEE International Symposium on Mixed and Augmented Reality 2009
Science and Technology Proceedings
19 -22 October, Orlando, Florida, USA
978-1-4244-5419-8/09/$25.00 ©2009 IEEE
The problem is, to be able to elicit such user needs from future
users, we need high-fidelity AR prototypes . Those mature AR-
systems help elicit much more realistic user needs than low-fidelity
Due to those facts it is challenging to develop and evaluate such
new AR user interfaces and applications. The 2D information vi-
sualization community has similar problems and thus promotes the
report on long-term usage and field studies in natural settings and
the investigation of new evaluation methods .
Furthermore, the execution of experiments in an industrial envi-
ronment under realistic conditions with real tasks is complex, time
consuming, and expensive, especially when incorporating real fac-
tory workers. It is still an open question how to execute such user
studies in the field of AR. D¨ unser et al  state that, until now, it is
partially unclear how to analyse the different user-related questions.
In addition to that, the funding of such experiments usually comes
from industry partners that expect results in a meaningful and sum-
mative manner. But the development of nowaday’s AR systems has
not yet matured enough so that today no well-developed and per-
fect systems are available. This means that we have to compare
our not yet finished AR prototypes with established technologies in
In summary, we address the typical situation of an AR re-
searcher: a) we have a somehow mature AR prototype but b) it still
has unresolved usability problems, c) it does not yet fulfill all of the
important user’s needs, d) we have to show the benefits of our AR
system over an established technology in comparative experiments
to give incentives for further investment, e) and it is expensive to
solve all these problems one after each other.
In this paper we contribute insights about the various obstacles of
bringing our mobile Augmented Reality system from the lab to a re-
alistic industrial environment. In particular we adress the midterm
work with head-mounted AR systems. Moreover, we report on two
tests comparing our Pick-by-Vision system with traditional tech-
nologies. In the first test, we compared two different configurations
of our Pick-by-Vision system with a paper based list for the first
time in a realistic industrial scenario. In the second test, we aimed
at analyzing strain of the users when working with our system over
a longer period of time. Basically we tried to reproduce the results
from our previous tests executed in a completely different setup
(different warehouse, different AR system, see ). To this end,
we let subjects work for two hours with our Pick-by-Vision system
and compared this with two hours working with a paper-based sys-
tem. At the same time, we give a case study of how to “carefully”
drive a comparative experiment with a not yet finished prototype.
A side effect of this evaluation was the development of a well-
engineered Pick-by-Vision visualization1, which we fully present
in . However, this visualization alone is not enough to support
the picking process, the open problems of the system itself will be
adressed in this paper.
Before we start explaining our evaluations, it is necessary to under-
stand the environment in which our actions take place.
ORDER PICKING IN LOGISTICS APPLICATIONS
Order picking is the gathering of goods out of a prepared range
of items following customer orders . In automotive industry,
order picking is used to collect parts that are required for one cus-
tomer’s specific car. After picking these items into a basket it will
be brought to the according car and all parts from the basket will be
mounted to the car.
Order Picking and State-of-the-Art
1and probably the most outworn HMD
Errors have a strong influence on the quality of delivery and the
relationship between clients and suppliers. Thus, zero-defect pick-
ing is one major goal. Flexibility and fine motor skills of human
beings are needed because the product range, and thus the variety
of items, steadily increases while, by contrast, the size of orders
is decreasing. Thus, complete process automation (e.g. the use of
robots) is not the appropriate answer and humans are often the best
solution for order picking .
Figure 2: differrent common order picking technologies: MDT with
scanner (1), Pick-by-Light (2) and Pick-by-Voice (3)
Conventionally, workers execute their orders with paper lists
which are intuitive for human beings but laborious to handle. In
modern warehouses, worker support based on usual paper lists is
often replaced by Mobile Data Terminals (MDT), Pick-by-Voice
(PbV) or Pick-by-Light (PbL) systems  (fig. 2), .
All these technologies have specific advantages as well as dis-
advantages. PbV supports the worker by providing all instructions
throughthecomputer’sspeechoutput. Unfortunately, thesesystems
face difficulties in noisy industrial environments. Furthermore, it is
questionable whether the order picking worker likes being bossed
by a monotone voice the whole day. Compared to voice support
systems, PbL offers visual aid for the worker by installing small
lamps on each storage compartment. PbL systems have the prob-
lem that the displays have to be elaborately integrated into the shelf
suitable for order picking stations with a high throughput. Brynzer
et. al. state the a better information system can save a lot of time
in the order picking process.
mainly measured according to the time and error rate.
The order picking time is divided into four interleaved tasks. The
base time includes all tasks at the beginning and the end of one or-
der (e.g. login at the system, pick-up and delivery of the collecting
unit or the paper list). The way time is the time the user phys-
ically moves through the storage area, and the picking time con-
sists of actually grabbing the item with the following delivery in
the collecting unit or on a conveyor. Finally, the dead time includes
the search for information and the human information processing
and all process steps that are not necessary for the real task (e.g.
open the packages). Comparing the order picking time with the
The quality of a Pick-by-Vision system is
picked items, the order picking performance can be calculated in
order lines per hour.
Picking errors can be divided according to their causes: a) wrong
amount, b) wrong item / article, c) missing article / missing order
line (as one line can consist of several articles) d) damaged article.
The amount of errors is referred to in terms of the whole amount
of picked items. This is called the order picking rate. Among other
things, the error rate depends on the order picking technology. For
a paper list it is normally 0.35%, for Pick-by-Light 0.40% or Pick-
by-Voice 0.08% . Even one error within 1,000 picked parts is
not acceptable because, for example, in automotive industry each
mistake can lead to halting the production line.
Using the analogy of the state-of-the-art systems, we name all sys-
tems using HMDs to support the order picking process Pick-by-
Vision systems, as they primarily provide information via the visual
sense. However, we have to further subdivide this definition, as we
have systems making use of tracking technologies and those which
do not. The systems which do not make use of tracking technolo-
gies to estimate the users position mainly present textual informa-
tion in the form of a list of items or images, etc. Due to this, we
will name such systems Pick-by-Vision (2D) systems. We call other
systems, which use tracking and make explicit use of AR, Pick-by-
Pick-by-Vision - Definition
We investigated the use of AR in industrial environments using the
example of order picking (paper list and a simple Pick-by-Vision
(AR) system) . This evaluations focused on user-related issues
in long-term use, mainly measuring strain by analyzing “heart rate
variability” (HRV) during a two hour work phase. Since the heart-
beat sequence is subject to a variability that can represent current
strain of a person, the analysis of HRV can be used to analyze work-
places in the context of ergonomic examinations . This has
already been proven in the field of AR . Next to analysis of
HRV, the EZ-Scale  and discomfort questionnaire were used to
examine subjective user strain. As a result, the overall strain did
not differ significantly between paper list and Pick-by-Vision (AR).
Nevertheless, a higher strain for uses’ eyes was generated by the
In another study we evaluated the use of Pick-by-Vision (2D)
systems in industrial environments . In short-term studies the
subjects performed better than with other conventional order pick-
ing technologies and they had not more physical strain.
To support order picking by appropriate visualizations we iter-
atively developed and improved visualisations for Pick-by-Vision
(AR) systems [23, 25, 26].
Brau et al. did a long-term study, testing the general use of
HMDs in an industrial setup (40 hour workweek) using the exam-
ple of order picking . They used the Microvision Nomad HMD
(compare fig. 4) and provided textual order picking information, or
what we call Pick-by-Vison (2D). The study had to be aborted be-
cause the users reported heavy headaches and eye strain. To find
out about reasons for these problems a second study was started by
BrauandFritzsche [12,6]. Intheir study theyhadthreedifferent se-
tups: a) a Pick-by-Vision System, b) a paper-based list system, and
c) a paper-based list system with subjects wearing a switched-off
HMD. The experiment took place under HMD-friendly conditions,
with controlled artificial illumination. The result was that the test
subjects mainly complained about wearing an HMD in general, re-
sulting in headaches due to the weight they had to carry on their
head. Physiological problems for the eyes could not be found, but
20% complained about having to read from the HMD. Therefore it
was proposed that each potential HMD user should do a suitability
test before actually working with the system.
This section explains the setup we used for our evaluations. We
start by illustrating the warehouse and the articles we used. After-
wards we present the different Pick-by-Vision Systems. Finally, we
discuss the adjustment of the HMD’s focal plane.
THE PICK-BY-VISION SYSTEMS AND SETUP
Figure 3: The warehouse with 4 shelves. The test subject wears a
HMD with attached tracking markers. The WiFi-connected wireless
PC is carried in a small backpack. As system control, we mounted
a game show-like buzzer (adjusting knob) on the subject’s belt. The
tracking is done via 6 ART DTrack1/2 Cameras.
Our warehouse (Fig. 3(4)) consists of two aisles, each 3.4 meters
long and 1 meter wide. To the left and right of each aisle we
placed shelves consisting of 5 layers (distance 0.4 meters) and 14
columns (distance 0.22 meters). Altogether we had 280 stock lo-
cations, which were filled up to 98%. The highest layer was at 1.8
meters and the bottom-most at 0.2 meters, forcing the users to make
large body movements.
We used a standard and optimized convention of naming the item
location with a number for the shelf, a character for the layer and a
number for the column. This resulted, for example, in names like
“3 A 13” (3rd shelf, first layer, 13th column). The warehouse was
filled up - i.e. chaotic - instead of placing related articles close to
each other. The assortment itself ranged from little drug boxes to
heavy bolts requiring two-handed interaction, as well as paper ware
(boxed and unboxed).
The Experimental Warehouse
The base - the place in an order picking system for order receipt
and delivery - consisted of a table approximately three meters from
the storage area. The collecting units (boxes) were stored to the
right side of the base.
The Picking Process
At the beginning of an order, the subject
takes a picking list from the stack Fig.3(2)) and takes one collect-
ing unit from the station Fig.3(3)) and puts it on a picking trolley
(Fig.3(1)) with four steerable wheels and dimensions of 400 x 600
mm. In the case of a worker using a Pick-by-Vision system, the
step of picking up the list is dropped from the process.
During the actual picking process, the worker moves the trol-
ley (Fig.3(1)) through the warehouse. The worker handles the or-
der lines from his list sequentially. Each line contains information
about the location of the item, the amount to pick and the article
number. Before picking the item, the subjects have to make sure
that they pick the right item by comparing the article numbers on
the list with those on the label of the item. This control process has
to be done for each of the different picking technologies.
To finish an order, the subjects deliver the collecting unit to the
station (Fig.3(5)). In the case of pick by paper lists, the list has to
be signed and must be put in to the collection unit. If subjects had
discovered picking errors, they had to be noted on the list, while
they had to be entered in the Pick-by-Vision system when it was
used. All delivered articles were controlled and presorted (for the
refill of the warehouse) at station 6 (Fig.3(6)).
The Pick-by-Vision equipment, which was used for both Pick-by-
Vision systems AR and 2D, can be seen in Fig.4. Even though our
prototype seems quite large, the subjects did not complain about
the system weight or dimensions during the tests. The system con-
sists of the Microvision Nomad ND2000 HMD, with an attached
A.R.T. marker target, which was aligned over the head of the user.
For one thing, this prevents the user from sticking with the target
in the shelf, and that also allowed the user to be tracked even when
bending over. The Pick-by-Vision software itself runs at a 13-inch,
2kg tablet PC carried in a small backpack connected to the tracking
server via wifi. The control unit of the Nomad display is mounted
at the backpacks’ belt. On top of the unit we mounted a click-turn-
wheel adjusting knob, which is the only input device to the system,
allowing for four inputs: turn (left/right), short click and long click.
The user mainly has to enter the following system options: request-
ing an order, getting the next/last order line, annotating an error
(including its kind) and terminating an order.
The Pick-by-Vison Systems.
Figure 4: The Augmented Reality Equipment.
In several iterations, we have continuously developed and enhanced
our Pick-by-Vision (AR) visualization, which is shown in Fig.1.
The whole history of this evaluation is described in. We have
to explain some steps of this history as the iterative improvements
were part of the experiments described in Sec. 5 and Sec. 6.
The picking process consists of two navigation phases. Both
have to be somehow supported by the system:
• In phase A, the coarse navigation, the worker has to find the
way to the right shelf.
• In phase B, the fine navigation, the worker has to find the
specific box (to pick from) on this shelf.
Phase A is not the critical task. Most logistics experts agree that
the number of shelves is generally manageable. Due to that and the
fact that our warehouse only consisted of two aisles, we provide
this information only textually (e.g.: “Go to aisle 1”)
Much more critical is phase B, the fine navigation to one of the
large number of boxes to pick from. This visualization consists of
two elements, shown in Fig.1. The first element is the square high-
lighting the label under the box. We decided not to highlight the
box itself as boxes can vary in size. However, it is not enough to
just highlight the box/label with an augmentation. Due to the small
field of view of current optical see-through HMDs (somewhere be-
tween 15 to 40 degrees) such an augmentation only rarely appears
on the display. More often, it is outside the field of view because
users are not looking in the direction of the target box. We then
have to extend the actual visualization by a meta visualization to
guide the users’ gaze toward the box, thereby covering the entire
range of 4π steradians. To this end we developed a visual tunnel,
which can be best imagined like a hose of a vacuum cleaner starting
a few centimeters in front of the eye and ending at the box.
The maturity level of our Pick-by-Vision (AR) system strongly
depending on the maturity of the Tunnel visualization. The Pick-
by-Vision (AR) 1.0 system, which was used in the first evaluation
(Sec. 5), performed poorly when the user was turned away from the
box by more than 80 degres. The Pick-by-Vision (AR) 2.0 System,
which was used in the second evaluation (Sec. 6), performed good
until about 120 degrees. Our current version, 3.0, performs well
over the entire range of 4π steradians.
The Pick-by-Vision (2D) system is the same system as the Pick-
by-Vision (AR) system, except that we display the location to go as
text instead of making use of augmented reality. Due to this the user
is able to find the right box by following the labels on the shelves.
The problem of the accommodation discrepancy for the use of opti-
cal see-through displays (OST) (like the Nomad HMD) for display-
ing Augmented Reality information is widely known [13, 11, 10].
The user of an HMD can only focus on the virtual image of the dis-
play and the real world simultaneously when both are at the same
distance , which is rarely the case. Rolland et. al.  stated that
it can be shown, for example, that rendered depth errors are mini-
mized when the virtual image plane is located in the average plane
of the 3-D virtual object visualized. As a solution to the various
conflicts in accommodation, Rolland et. al.  suggest to allow
autofocus of the virtual image plane as a function of the location
of the user’s gaze point in the virtual environment, or to implement
multifocal planes. A frequent change of accommodation produces
fatigue, but placing the virtual focus in typical handling distances
of 0.6 meters forces a continuous contraction of the ciliary muscle
So what are the consequences for our application? Since cur-
rently none of the proposed autofocus / multifocal plane HMDs are
available, we have to deal with the single feature of our Nomad
Adjusting the Focus of the HMD
HMD allowing to manually adjust the virtual image plane between
0.3 meters to infinity. The users of our order picking system have
to regard two types of visualizations: 1) the 3D augmentation and
2) 2D textual information. The 3D augmentation consists of the
tunnel and square in front of the box and can stretch over a focus
area of about 0.4-3.5 meters. The user needs to read text from the
HMD in different situations, but the most relevant situation seems
to be when the user holds the picked item in his / her hand, at a
distance of about 0.6m. At that point the user has to compare the
article number shown on the HMD with the article number printed
on the article itself. This requires a multiple change of focus (be-
tween HMD and reality) in a short time periode, as the number is
mostly quite long. Finally, we put the focal distance of the HMD in
the middle of the discussed values, somewhere between 1.0 and 1.5
meters, as the Nomad does not allow an exact adjustment.
Up to that point of this evaluation, we could only evaluate our Pick-
by-Vision (2D) system in an industrial setup . So far our Pick-
by-Vision (AR) system was just tested in the lab [23, 25]. Having
the chance of equipping a real warehouse for a limited timeframe
with tracking cameras, we brought our Pick-by-Vision (AR) system
to the warehouse, to be able to compare it in an experiment with the
other technologies. Due to the limited timeframe, we had to find
an efficient way to adapt the visualization of our Pick-by-Vision
(AR) system to the real warehouse. To this end we did an informal
pre-evaluation with 8 participants described in . This helped
us remove the most important usability problems and to find good
initial parameters for our visualization.
In the following we describe the experiment in which we com-
pare our Pick-by-Vision (2D), our Pick-by-Vision (AR) ( at version
1.0 and, as a reference, a common paper-based system.
The only independent variable of this experiment was the picking
system used at the three levels described above. The main depen-
dent variables we discuss here were picking performance (time),
errors and strain, as a subjective measurement. To analyze the lat-
ter variable (strain), we used the common NASA Task Load Index
(TLX) test .
For our testing environment, we used the warehouse described
in Sec. 4.1. The experimental design was of type with-in subject.
Which means that each subject had to carry out the test with each
of the three technologies, whereby the start sequence of the tech-
nologies was permuted. The subjects had to fulfill six orders with
all in all 30 order lines (5.0 order lines/order) and 61 items (2.03
items/order line) for each technology. To reduce learning effects
the orders were different between the three picking technologies.
Within one order the order lines are different but the whole amount
of items, their weight, and the ranges to go and to pick were the
same. To compensate for learning effects, in particular when using
an HMD for the first time, we followed the important approach of
letting people try-and-ask, as long as they really felt comfortable
and (we thought) they fully understand the system .
We set up the following hypotheses, all while keeping in mind
that our systems are far from being perfect.
In previous tests we improved our Pick-by-Vision (AR) System
to support an efficient and error free-picking  , and for the Pick-
by-Vision (2D) system we knew that it performs better than a paper
based system , so we set up the following hypothesis:
Experimental Setup and Hypotheses
E1 H1: a) Picking with a Pick-by-Vision (AR/2D) sys-
tem produces less errors than picking with a paper
list, and b) Picking with the Pick-by-Vision (AR) sys-
tem produces less errors as picking with Pick-by-Vision
E1 H2: a) Picking with Pick-by-Vision (AR/2D) is
faster than picking with a paper list, and b) Picking with
Pick-by-Vision (AR) is faster thanpicking with Pick-by-
Besides improving the logistic performance figures, one design
goal is to not produce stress using the Pick-by-Vision systems.
Nevertheless, especially because of the weight of hardware and
the mentioned problem of switching between real and virtual focal
planes, we assume that using the Pick-by-Vision systems results in
larger strain than working with a paper list. So our third hypothesis
E1 H3: The NASA TLX shows a lower strain for work-
ing with the paper list compared to the Pick-by-Vision
Beside those metrics used in most 3D user interface evaluations,
we explored some other aspects by using questionnaires after the
test. We had conspicuousness between the general experience with
3D user interfaces and the performance in previous AR tests ,
and therefore asked the subjects about their experiences with 3D.
Furthermore, we used three positive questions (Likert scale) to gain
insight about the general comfort when wearing the Pick-by-Vision
system, as well as whether or not the subjects felt constrained by
the HMD or the visualization.
We had 19 subjects (16 male/3 female) between the ages of 18 and
45 (mean age: 27.2, std dev: 6.78). The subjects were mainly in-
dividuals from all areas of the university, friends, as well as three
professionalorderpickingworkers. Aswesaidatthebeginning, we
compare our not yet perfect Pick-by-Vision System (here at Version
1.0) with an established technology. For that reason we expect to
find several unlucky factors, which lead to a general bad perfor-
mance of our Pick-by-Vision systems. By an in-depth observation
of the experiment, we have to equalize the results, as shown in the
On the first view, according to the error rate, sub-
jects performed significantly worse using both Pick-by-Vision sys-
tems as compared to the paper-based order picking. However, most
errors can be traced back to a bad system configuration. Sometimes
to go back in the system. Hence, errors of this category recognized
by the subjects were subtracted. However, two missing order lines
within Pick-by-Vision (AR) were not recognized by the subjects.
After this correction, the most common error was the picking of
the wrong amount. Neither the paper list nor either of the Pick-by-
Vision systems had a function to avoid this error. Furthermore, a
wrong item was picked using the paper list and also using the Pick-
by-Vision (2D) system, but no wrong items were picked using the
Pick-by-Vision (AR) system. Finally, Pick-by-Vision (AR) has, in
average, the lowest error rate (0.7%), followed by Pick-by-Vision
(2D) (1.23%) and then paper list (1.4%). This result could not be
shown to be significant using the Friedman Test2(α = 5%) and thus
E1 H1 a/b cannot be proved. Figure 5 and 6 show the error rates
of the three systems.
The picking time performance for the Pick-by-Vision
systems seems to be about 10% better than that of the paper list, as
shown in Fig.7. However, we could not find significant differences
(ANOVA, α = 5%) Thus E1 H2 a/b could not be proven. Fur-
thermore, we observed that the distributions of the Pick-by-Vision
systems are skewed left because 12 of the 19 subjects were faster
2We use the Friedman Test instead of ANOVA, as the sample is not
paper listPick-by-Vision 2DPick-by-Vision AR
error rate [%]
Figure 5: Picking error rates for the three technologies before and
after the correction of systematic errors.
amount of errors (absolute values)
missing order line
Figure 6: Kinds of picking the overall errors for the three technologies
before and after the correction of systematic errors.
than the mean value. Which, on the other hand, means that one
third of the participants performed really slow. But we could not
trace this back to any specific reason.
The NASA TLX, test resulted in nearly the
same values (25-28) for all systems (see Fig.8). We could not find
and therefore could not proof hypothesis E1 H3.
Results from Questionnaire
people with previous experience with 3D user interfaces performed
better in both logistics operating figures (fewer errors and faster).
An analysis of the questionnaire did not show that subjects felt un-
comfortable or constrained by using the Pick-by-Vision system (it
should be noted that subjects had to only work for about half an
hour with the system).
We could find a tendency that
Our first field trial showed slight benefits for the Pick-by-Vison sys-
tems over paper-based picking. Even though none of the results is
the concept. If one takes into account that our technologies, in par-
ticular the Pick-by-Vision (AR) system (at version 1.0), still had
several open usability issues leading to bad performance in terms
of time, this is a quite good result for Pick-by-Vision. If we take a
deeper look into the structure of errors made we can see that, with
Pick-by-Vision (AR), none picked an item out of the wrong box.
Moreover, we could not find an increased level of strain produced
by the Pick-by-Vision system, but we just did a “short” test, where
subjects wore the HMD only for a short time period. However, we
figured out that some people had trouble reading the information
Conclusion of Evaluation 1
order picking time
paper listPick-by-Vision 2DPick-by-Vision AR
order picking time [min]
Figure 7: mean values of the order picking time for the three tech-
paper listPick-by-Vision 2DPick-by-Vision AR
Figure 8: NASA TLX for the three technologies. Values can be be-
tween 0 (no task load) - 100 (full task load).
from the HMD, and one third performed quite bad with the system
in general. Whereby we could not find a significant correlation be-
tween both groups. Apart from that, we identified several aspects of
our AR visualization to be optimized, leading to the fundamentally
improved Pick-by-Vision (AR) 2.0 system.
The previous experiment showed that Pick-by-Vision works in gen-
eral and is already comparable to the established paper-based pick-
ing support. That’s why we decided to test our Pick-by-Vision sys-
tem in an in-depth evaluation over a “longer” period of time. On the
one hand we wanted to get insight about how learning effects affect
errors and performance. Especially when comparing our error rates
with common error rates (see ), we could see a lot of potential
for learning effects to influence the results after a longer period of
use. On the other hand, we knew from  that long-term use of an
HMD can be a load for the user resulting in headaches, eye fatigue
To this end, we designed a new experiment following our first
study that analyzed user strain . As the execution of a mid-term
study goes along with a hugh expense, especially if users have to
do real tasks, we had to determine some limitations. We decided to
use only a few participants, and follow a rather in-depth qualitative
approach, than a quantitative one. Furthermore, we expected the
Pick-by-Vision (AR) system to deliver more interesting results than
the Pick-by-Vision (2D) system. Finally, we set up an experiment
comparing our Pick-by-Vision (AR) system in a mid-term study
with a common paper based system.
6.1Experimental Setup and Hypotheses
(AR) or a paper-based list. We designed a with-in subject experi-
ment in which each subjects had to work with each technology for
two hours, whereby the start sequence of each technology was per-
muted. A single test session for a technology lasts about four hours,
including the actual two hours picking, pre- and post-tests, recovery
lying (resting) phases and up to half an hour try-and-ask introduc-
tion into the technology and a structured interview at the end of the
test. Each test session for each subject was executed on two dif-
ferent days at the same time of day, to get comparable results for
the strain parameters. Within the two hours, subjects should fulfill
at maximum 100 orders (2.9 order lines/order, 2.2 items/order line).
Three rack compartments contained wrong articles. The wrong arti-
We measured the logistic operating figures (order picking time
and errors) for a whole run and for the single orders.
The analysis of user strain was done in the same way as we did in
. This means that we analysed the heart rate variability (HRV),
used an EZ-Scale and a discomfort questionnaire. In addition to
that, we again used the NASA TLX. The subjects wore a “Polar
RS 800 CX Multi” pulse recorder for analysis of HRV, the software
used to retrieve the data from the recorder was “Polar ProTrainer
Even if there was no difference in strain in the first evaluation,
we expected the Pick-by-Vision system to produce a higher strain
in this experiment, This was because this time the subjects had two
work with the system for two hours. That is why we set up the
The only independent variable of this
E2 H1: The analysis of HRV data reveals a higher user
strain for the Pick-by-Vision (AR) system than for the
E2 H2: The analysis of EZ-Scale data reveals a higher
user strain for the Pick-by-Vision (AR) system than for
the paper list.
E2 H3: The analysis of discomfort questionnaires re-
veals a higher user strain for the Pick-by-Vision (AR)
system than for the paper list.
E2 H4: The NASA TLX score is larger for Pick-by-
Vision (AR) than for the paper list.
The Pick-by-Vision (AR) system already showed good results in
the first evaluation and moreover we expect learning effects from
using the AR system over a longer periode of time. Because of
that, we set up the hypotheses that the Pick-by-Vision (AR) system
should perform better according to the logistic figures:
E2 H5: The error rate is lower or the same with the
Pick-by-Vision (AR) system compared to the paper list.
E2 H6: The order picking time with the Pick-by-Vision
(AR) system is equal or better compared to the paper
periment, we had several assistants besides the actual investigator,
mainly to help with checking and sorting the picked articles. More-
over, we created the role of a special observer, who was placed on
an observation deck a few meters above the actual experimentation
area. This physical position had three benefits: the observer could
perfectly observe, was not recognised by the test subject (like be-
hindatraditionalhalf-mirror)and, furthermore, wasallowedtogive
comments to the subject and even to interrupt the expriment.The
observer observed the user behaviour by mainly focussing on user
To get a deep insight into the ex-
strategies (how the user handles the system or how the user tries
to conquer usability problems). To this end, the observer tried to
analyze each little step / movement a user did. While observing the
user, the observer noted questions for an interview after the test.
In cases where the observer observed the test subject having severe
trouble, in particular leading to unnecessary bad performance, the
observer could intervene in the experiment. We stopped the time
during the breaks and solved the problem by giving a comment or
e.g. adjusting the HMD. We think this policy falsifies the results
less than not interrupting the test subject.
After each test the subjects were confronted with the obser-
vations in a semi-structured interview. Such interviews typically
lasted between fifteen and thirty minutes. The interviews were
recorded using a tape recorder.
We had 8 subjects (4 male/4 female) between the ages of 18 and 37
(mean age: 26, std dev: 5.37) performing the test. Subjects were,
again, from all around the university, friends and as well as two
professional order picking workers.
The objective user strain was measured by analyzing the HRV data
from the pulse recorder. In general, it can be said that the frequency
of the heart rate is not an indicator for the stress level, but rather
the variability in the heart rate. A high variability in heart rate indi-
cates a low stress level, whereas a constant heart beat is an indicator
for stress. For the analysis of HRV the standard deviation (SD) was
chosen as a parameter of the time domain which is used as a marker
for short term changes of the sympathetic and parasympathetic ner-
vous system indicating changes in user strain.
Analysis of User Strain
Beginning of Work Task
Middle of Work Task
End of Work Task
SD (Pick-by-Vision vs. Paper)
Figure 9: Development of SD through the test phases
Fig. 9 shows the development of SD through the test phases. The
analysis of SD does not show a significant difference (Wilcoxon
Test) between paper list and Pick-by-Vision (AR). This means that
no higher physiological strain can be seen between both systems,
E2 H1 thus can be rejected.
The EZ-Scale describes the subjective state of well-being of a
person by Stanine values. Altogether changes in different factors
can be seen for both systems indicating a rise in strain. Signifi-
cant differences before and after the test can be found (Wilcoxon
Test) for the Pick-by-Vision (AR) system in a reduction of activa-
tion (p = .020), rise in fatigue (p = .027) and decreasing relaxation
(p=.046). For the paper list the willingness for exertion is reduced
(p = .039) as well as social acceptance (p = .034) and sleepyness
(p = .041). From this data it is obvious that both systems result
in an increase of strain significantly influencing different factors of
personal well-being. Again it was not indicated that working with
the Pick-by-Vision (AR) system caused a higher strain than work-
ing without AR, so that E2 H2 can be rejected.
The discomfort questionnaire asked for current physical com-
plaints mostly focused on the visual system of the user. The anal-
ysis of the data revealed significant differences between Pick-by-
Vision (AR) and paper list only for the factor “headache” (p=.034,
fig. 10). On the one hand, this corresponds to the results presented
see blurred temporarily
sensitivity to light
skip words or lines
poor readability of letters
see double contours
tension of neck and shoulder
use finger when reading
have to go close when reading
white spots when reading
other discomfort concerning eyes
Discomfort (Pick-by-Vision vs. Paper)
Diff erence Point Values (before-after)
Figure 10: Result of discomfort questionnaire comparing differences
between Pick-by-Vision (AR) and paper list before and after testing
ences. But on the other hand, we found a change in another factor
(headache with p = .034 instead of burning eyes, Wilcoxon Test).
Regarding E2 H3 we cannot fully agree or disagree since only one
out of 15 items revealed a significant difference.
The NASA TLX shows a task load of 87.81 for Pick-by-Vision
(AR) and 71.98 for the paper list. However, there is no significant
difference (ANOVA, α = 5%) (compare Fig.11). So E2 H4 can
neither be accepted nor rejected. Compared to the first experiment,
both values are higher, even though the actual task was the same.
We lead this back to the fact that two hours of manual work without
a break can be straining in general.
Figure 11: NASA TLX for both technologies. Values can be between
0 (no task load) - 100 (full task load).
Vision (AR) was higher than for the paper list (figure 12) and
thus E2 H5 can be rejected. Within 5,873 order lines for the pa-
per list 29 (1.37% ± 1.09%) were picked wrong, compared to 58
out of the 5,519 order lines (3.03% ± 2.24%) for the Pick-by-
Vision (AR). We corrected the value of the Pick-by-Vision system
In this test series the error rate for the Pick-by-
to 2.34% ± 2.08%, as subjects had some problems with the adjust-
ing knob. The knob was not suited for long term use, and subjects
sometimes pressed the button twice accidentally, and just told us
about the mistakes rather than use the go-back function.The error
rates are shown in Fig.12
error rate [%]
Figure 12: picking error rates for both technologies before and after
the correction of the systematic errors
There are several reasons for the picking errors (compare
Fig.13). With the paper list most of the errors (14) were based on
the wrong amount, seven on the wrong article and six on a missing
order line. There were also two processing errors because the sub-
jects forgot to sign the list. With Pick-by-Vision (AR) the wrong
amount was also the most frequent error (23).
Despite of reducing errors caused by the systematic system er-
ror, there are still 15 missing order lines. For example the subjects
clicked the button when they entered the aisle. This was not neces-
saray and the first order line in this aisle was missing. The process
flow of the Pick-by-Vision (AR) system has to be improved in this
case. For AR the picking of wrong items is the most interessting
error. The subjects picked five wrong articles, three of which were
due to the wrong items we had integrated. These three errors were
not recognized by the subjects because they did not check the arti-
cle number. The subjects did, however, pick from the right storage
compartment, therefore this error does not directly depend on the
AR visualization. One subject picked two items of one order line
right and one item from the storing compartment beneath. He did
not recognize the error because the items had the same shape. A
more precise inspection of this leads to the following explanation:
The subject first took two items (three were not possible at the same
time because of the weight). When he took the third item, he did
not move his head back to see the augmentation highlighting, but
rather saw the box, while picking, only in the corner of his eye. One
subject picked an item far away from the actual box. As we had to
refill the warehouse manually and could not be 100% certain that
all articles were placed correctly we think this mistake was due to
an incorrect refillment.
When we compared the distribution of errors over the time, we
figured out that the errors for the Pick-by-Vision (AR) system de-
creased during runtime and increased for the paper list.
two hours. They performed 133 (± 35) order lines using the list and
124 (± 55) with Pick-by-Vision (AR). However, some subject ful-
filled the 100 given orders in less time, some subjects had to take a
break and we had a few breakdowns. After taking these corrections
into account, subjects performed about 7.6% faster using Pick-by-
Vision (AR): 145 order lines per hour compared to 134 order lines
per hour using the paper list ( see Fig. 14). Thus, order picking with
Pick-by-Vision (AR) is faster and E2 H6 was found to be true.
Subjects were supposed to work with both techniques for
paper list Pick-by-Vision
amount of errors (absolute values)
missing order line
Figure 13: kinds of picking errors for both technologies before and
after the correction of the systematic errors
order lines per hour
Figure 14: mean values of the order picking performance for both
technologies with and without regarding the interruptions
There were several problems directly related to the HMD. Two peo-
ple complained about headaches, which they traced back to the
HMD headband. Three people complained about pressure in their
eyes. Both observations were also made by [12, 6]. Two of our
subjects had serious problems focussing on the HMD and stopped
regularly during the experiment to try to focus the display. One of
them even needed a 15-minute break because otherwise “the eye
would have jumped out”. We thought about psychological reasons,
like being afraid of the technology, but the subject had a high affin-
ity with new technologies. The other subject said that as long as
the background was in the focal distance she could focus on the
HMD. We tested both subjects using a Landolt-C-Ring test, both
had neither perfect nor particularly bad eyes.
In general people said that they sometimes needed some time to
focus on the HMD. However, subjects only complained about read-
ing the text from the HMD, which was, in our opinion, displayed in
an appropriate size (compare Fig.1). None of the subject had prob-
lems seeing the actual 3d augmentation. We reason that one does
not need to focus on the 3d augmentation to be able to interpret and
work with it. On the other hand, 50 % of the subjects did not have
problems with the focus.
Problems using the HMD
Against our expectations, the real picking workers performed slow
compared to the other subjects. This has several reasons: They tried
to do an error-free job because they know about the consequences
of false delivered items. They placed all items in an organized way
with the label side up in the sump, to have a fast overview before
delivery, while other subjects just throw the items in the box. Fur-
thermore, we realized that this is their real job and they actually do
not do it in a rush, as they usually have to do it eight hours a day.
Besides this observation, we found out that there are two groups
of HMD users. One group works parallel: they make user input
while walking. The other ones stop walking for each user input.
Even though we interrupted people to suggest that they work paral-
lel, they did not change their behaviour. For that reason, that group
performed slower than those that worked using the parallel method.
Even if it does not look like it on the first view, the results of this
first endurance test are quite satisfying:
Subjects performed slightly faster using the Pick-by-Vison (AR)
system. We could identify several issues at Pick-by-Vision (AR)
2.0 system, which have slowed down the usage of the system in
this experiment: bad input device, too many system states to click
through, no direct back button, late display of the aisle to go to, par-
tial occlusion of the text by the augmentation and imperfect guiding
by the augmented reality visualization. We could address most of
these usability issues in the Pick-by-Vision (AR) 3.0 system, which
we developed after this experiment.
According to the error rate, subjects performed three times worse
than in the first evaluation. Even if the AR visualization seems to
be a perfect indicator for the right box, subjects could and did make
other errors. Mainly they ignored to control the article numbers or
the amount. On the one hand we traced this back to the fact that
they had problems focusing on the text of the HMD, and some of
them definitely ignored it. On the other hand, subjects probably
grew tired and were not concentrating anymore. Those errors have
to be avoided by the use of other mechanisms, for example a speech
input. In such systems users have to speak the amount and an
additional error checking number.
Another important result of this study is that the results of HRV,
EZ-Scale and discomfort questionnaire show no general differences
in strain changes between paper list and Pick-by-Vision which is
very similar to our first study of user strain . This means the
Pick-by-Vision system in general is no additional load to the user
compared to a paper list. From the EZ-Scale we can see that both
systems influence different subjective parameters. The results of
the discomfort questionnaire point out that working with an HMD
can result in a headache, even though the reason for that remains
unclear. Either the headband of the display was set too tight or the
changes between the virtual image plane and the surroundings are
a main reason for this finding. Current research projects deal with
investigations into that problem (e.g. ).
Discussion of the Results
Doing research in industrial augmented reality is a challenging task,
as industry partners want to know about the capability of a technol-
ogy they invest in. AR still has many unsolved problems but we
need to compare our AR applications with established technologies
to motivate its capabilities. In this paper we show how to do such a
comparison in a fair way and without whitewashing the results.
We present two user studies undertaken in an order picking sce-
nario to analyze picking performance, error rates and user strain of
our Pick-by-Vision systems. In summary, it can be stated that Pick-
by-Vision (AR) can increase the performance of an order picking
worker according to the main important logistic figures, namely
time and error rate. However, there were still some errors made,
using the Pick-by-Vision (AR) system. It was a perfect indicator,
for the right box, but people picked the wrong amount, or did not
look at the article number to see that a wrong article was in the box.
Regarding user strain, we found that even though we have
uncomfortable HMD headbands, a backpack to carry, and non-
addressable display focal planes, our system did not cause a higher
general user strain than the conventional paper list. Nevertheless,
the discomfort questionnaire shows that improvements of the dis-
play devices are necessary to reduce the potential for headaches.
The problem remains that about 20% of subjects had serious
problems using the HMD. Brau and Fritzsche [12, 6], figured out
the same problem for 20% of their subjects by also using the same
Nomad HMD. In our case users did not have problems with the 3D
augmentations, but just with reading the 2D text. Further investiga-
tions are necessary to find out if it takes some time for habituation
or if it is directly related to the people or the type of HMD.
Besides that, we showed the useful application of an active ob-
server, who had a complete overview and was allowed to intervene
in the experiment. This prevented subjects from performing poorly
and distorting the results, just because they, for example, wore the
HMD in a bad way. We draw a conclusion of following our phi-
losophy of observing a few people in-depth rather than following
an approach with a large number of users. If we had used more
subjects in the tests, we probably would have had more statistically
significant results, but the insights and conclusion would have been
pretty much the same.
Even though the Pick-by-Vision (AR) system was slightly bet-
ter than the paper list this improvement typically is not significant
enough to introduce and apply the technology in industry. Thus
further improvements and tests of our v3.0 system will follow.
This work was partially supported by the German Federal Min-
istry of Education and Research (AVILUS project, grant no.
01 IM 08 001 A) as well as the German Federal Ministry of Eco-
nomics and Technology (AiF-FV 14756).
The authors wish to thank ART GmbH for supporting us with
Tracking Cameras. Many students were involved in the project.
Two should be mentioned here: Michael Stather and Max Meister.
Thanks to Margarita Anastassova for the help with the evaluation
strategies. Finally, thanks to all participants who took part in the
different kinds of experiments and gave us feedback for optimizing
the Pick-by-Vision system.
 M. Anastassova, C. M´ egard, and J.-M. Burkhardt. Prototype evalu-
ation and user-needs analysis in the early design of emerging tech-
nologies. In Human-Computer Interaction. Interaction Design and
Usability, volume 4550 of LNCS. Springer Berlin / Heidelberg, 2007.
 H. Brau, C. Ullmann, M. Duthweiler, and H. Schulze.
In L. Urbas and C. Steffens, editors, Zustandserkennung und Sys-
temgestaltung Bd. 19. VDI-Verlag, 2005.
 H. Brynzer and M. I. Johannsonn. Design and performance of kit-
ting and order picking systems. International Journal of Production
Economics, 45:115–125, 1995.
 D. Drasic and P. Milgram. Perceptual issues in augmented reality. In
Proc. SPIE Vol. 2653, 1996.
 A. D¨ unser, R. Grasset, and M. Billinghurst. A survey of evaluation
techniques used in augmented reality studies (tr-2008-02). Technical
report, University of Canterbury, HITLabNZ, 2008.
 L. Fritzsche. Eignung von augmented reality f¨ ur den vollschichtein-
satz in der automobilproduktion. Master’s thesis, TU Dresden, 2006.
 T. Gudehus. Logistik. Springer, Berlin, 3. edition, 2005.
 W. A. G¨ unthner. Neue Wege in der Automobillogistik: Die Vision der
Supra-Adaptivit¨ at. Springer, 2007.
 S. Hart and L. Staveland. Development of NASA-TLX (Task Load
Index): Resultsofempiricalandtheoreticalresearch. InP.A.Hancock
and N. Meshkati, editors, Human Mental Workload, pages 139–183.
North-Holland, Amsterdam, 1988.
 A. Huckauf, M. H. Urbina, I. B¨ ockelmann, L. Schega, R. Mecke,
F. Doil, and J. T¨ umler. Perceptual issues in optical-see-through dis-
plays. In submitted to eighth IEEE and ACM International Symposium
on Mixed and Augmented reality, 2009.
 A. Huckauf, M. H. Urbina, F. Doil, J. T¨ umler, and R. Mecke. Distri-
bution of visual attention in head-worn displays. In Proceedings of the
ACM Symposium on Applied Perception in Graphics and Visualisation
2008 (APGV08), Los Angeles, California, USA, 2008. ACM.
 J. Kampmeier, A. Cucera, L. Fritzsche, H. Brau, M. Duthweiler, and
L. G. K. Eignung monokularer augmented reality – technologien in
der automobilproduktion. In Tagung der Deutschen Ophthalmolo-
gischen Gesellschaft ”Augenheilkunde in der alternden Gesellschaft
- Herausforderung und Chance”, 2006.
 S. Liu, D. Cheng, and H. Hua. An optical see-through head mounted
display with addressable focal planes. In 7th IEEE/ACM International
Symposium on Mixed and Augmented Reality, 2008. ISMAR 2008.,
 M. A. Livingston. Evaluating human factors in augmented reality sys-
tems. IEEE Comput. Graph. Appl., 25(6):6–9, 2005.
 J. R. Nitsch. Die Eigenzustandsskala (EZ-Skala) - Ein Verfahren zur
hierarchisch-mehrdimensionalen Befindlichkeitsskalierung. In J. R.
Nitsch and I. Udris, editors, Beanspruchung im Sport. Beitr¨ age zur
psychologischen Analyse sportlicher Leistungssituationen, pages 81–
102, Bad Homburg, Germany, 1976. Limpert.
 O. Oehme. Ergonomische Untersuchung von kopfbasierten Displays
f¨ ur Anwendungen der erweiterten Realit¨ at in Produktion und Service.
PhD thesis, RWTH Aachen, 2004.
 O.Oehme, L.Schmidt, andH.Luczak. Comparisonbetweenthestrain
indicator hrv of a head based virtual retinal display and lc-mounted
displays for augmented reality.
WWDU 2002 World Wide Work - 2002, pages 387–389, Berchtes-
gaden, 2002. Abindgon, Oxon, UK : Taylor & Francis.
 C. Plaisant. The challenge of information visualization evaluation. In
AVI ’04: Proceedings of the working conference on Advanced visual
interfaces, pages 109–116, New York, NY, USA, 2004. ACM.
 R. Reif, W. G¨ unthner, B. Schwerdtfeger, and G. Klinker. Pick-by-
vision comes on age: Evaluation of an augmented reality supported
picking system in a real storage environment. In 6th International
Conference on Computer Graphics, Virtual Reality, Visualisation and
Interaction in Africa (Afrigraph 2009), 2009.
 J. Rolland, A. D., and G. W. Towards quantifying depth and size per-
ception in virtual environments. Presence: Teleoperators and Virtual
Environments, 4(1), 1995.
 J. P. Rolland, M. Krueger, and A. Goon. Multifocus planes in head-
mounted displays. Applied Optics, 39(19), 2000.
 D. Rowe, J. Silbert, and D. Irwin. Heart rate variability: Indicator of
user state as an aid to human-computer interaction. In CHI98, pages
480–487. ACM Press, 1998.
 B. Schwerdtfeger, T. Frimor, D. Pustka, and G. Klinker. Mobile infor-
mation presentation schemes for logistics applications. In Proc. 16th
International Conference on Artificial Reality and Telexistence (ICAT
2006), November 2006.
 B. Schwerdtfeger and G. Klinker. An evaluation of augmented reality
visualizations to support the order picking, 2008. Technische Univer-
sit¨ at M¨ unchen, Report TUM-I-08-19.
 B. Schwerdtfeger and G. Klinker. Supporting order picking with aug-
mented reality. In Proc. of the seventh IEEE and ACM International
Symposium on Mixed and Augmented reality, September 2008.
 B. Schwerdtfeger, G. Klinker, R. Reif, and W. G¨ unthner. Pick-by-
vision: There is something to pick at the end of the augmented tunnel.
Technical Report TUM-I0921, TU M¨ unchen, 2009.
 M. ten Hompel and T. Schmidt. Warehouse Management. Springer,
 J. T¨ umler, R. Mecke, M. Schenk, A. Huckauf, F. Doil, G. Paul, E. Pfis-
ter, I. B¨ ockelmann, and A. Roggentin. Mobile augmented reality in in-
dustrial applications: Approaches for solution of user-related issues.
In Proc. of the seventh IEEE and ACM International Symposium on
Mixed and Augmented reality, 2008.
 VDI, Berlin. VDI guideline 3590: Order picking systems, 1994.
In Proceedings of the Conference