Frontiers in Psychology 01 frontiersin.org
Evaluation of an assistance
system supporting older
pedestrians’ road crossing in
virtual reality and in a real-world
field test
Rebecca Wiczorek * and Janna Protzak
Research group FANS, Department of Psychology and Ergonomics, Technische Universität Berlin,
Berlin, Germany
Older pedestrians are at a high risk of becoming victims of car accidents
because they tend not to pay sufficient attention to upcoming traffic. Within
our research project, an assistance system for older pedestrians has been
developed. It detects the street and communicates with the users through a
vibrotactile interface. Two evaluation studies have been carried out in order to
understand the potential benefits and drawbacks of the developed assistance
system. One study was conducted in a virtual environment (VR) with 23
participants, aged 65+. The other experiment was a field test in a real street
environment with 26 participants, aged 65+. Objective dependent variables in
both experiments were checking for traffic (operationalized via head tracking)
and stopping in front of the street (VR study), i.e., approaching time (field test).
Workload and acceptance served as subjective dependent variables. Analysis
of the VR experiment showed significantly more head rotation with the
assistance system than without it, as well as significantly more with cars than
without cars. The same was true for the frequency of stopping. No significant
difference was found concerning workload. With regard to acceptance, the
majority of participants indicated that the system was supportive and able
to reduce risks in traffic. In the field test, results for head rotation confirmed
the findings of the VR study. Analysis showed a marginally significant higher
head rotation frequency with the alarm system than without, and significantly
different patterns of checking for traffic at marked and unmarked crossings.
However, unlike in the VR study, no differences were found in approaching
time with and without the assistance system. Approaching time was slower at
marked crossings. No difference was found with regard to workload, meaning
the use of the assistance system did not increase the subjectively perceived
workload of participants. Analysis of the acceptance questionnaire showed
a positive attachment to the assistance system. However, most reported
that they did not experience any advantage from the use of the system, and
expressed no intention to buy such a system for themselves.
KEYWORDS
older pedestrians, assistance system, road crossing, virtual reality, field study
TYPE Original Research
PUBLISHED 20 December 2022
DOI 10.3389/fpsyg.2022.966096
OPEN ACCESS
EDITED BY
Evellin Cardoso,
Universidade Federal de Goiás, Brazil
REVIEWED BY
Luca Brayda,
Nextage Inc., Italy
Mihoko Niitsuma,
Chuo University,
Japan
*CORRESPONDENCE
Rebecca Wiczorek
SPECIALTY SECTION
This article was submitted to
Human-Media Interaction,
a section of the journal
Frontiers in Psychology
RECEIVED 10 June 2022
ACCEPTED 21 November 2022
PUBLISHED 20 December 2022
CITATION
Wiczorek R and Protzak J (2022) Evaluation
of an assistance system supporting older
pedestrians’ road crossing in virtual reality
and in a real-world field test.
Front. Psychol. 13:966096.
doi: 10.3389/fpsyg.2022.966096
COPYRIGHT
© 2022 Wiczorek and Protzak. This is an
open-access article distributed under the
terms of the Creative Commons Attribution
License (CC BY). The use, distribution or
reproduction in other forums is permitted,
provided the original author(s) and the
copyright owner(s) are credited and that
the original publication in this journal is
cited, in accordance with accepted
academic practice. No use, distribution or
reproduction is permitted which does not
comply with these terms.
Wiczorek and Protzak 10.3389/fpsyg.2022.966096
Frontiers in Psychology 02 frontiersin.org
Introduction
Older pedestrians are at a high risk of becoming victims of car
crashes, as official statistics show [Statistisches Bundesamt
(Destatis), 2020, 2021]. In 2019, around 20% of the victims of car
crashes in Germany were 60 years and older. Moreover, their risk
of dying as a result of an accident is three times higher compared
to younger victims, which is due to their higher fragility. Seventy-
eight percent of the accidents involving older pedestrians were
caused by the older pedestrians. The official statistics also indicate
an important reason for older pedestrians’ higher involvement in
such accidents: In more than half of the cases, the older pedestrians
did not pay sufficient attention to the upcoming traffic.
Prior research supports the conclusion that insufficient
attention paid to traffic is prevalent and offers some explanations.
One important reason for a lack of attention to traffic is
engagement in parallel visual tasks. Two laboratory experiments
(Zito etal., 2015; Tapiro etal., 2016) indicate higher frequencies
of checking the ground for obstacles in older compared to younger
pedestrians. Avineri etal. (2012) found a correlation between this
ground checking behavior and a fear of falling, which increases
with age (Tinetti etal., 1994; Schott, 2008). Further, Wiczorek and
Protzak (2022) show the negative impact of visual and cognitive
tasks on hazard perception in a road crossing simulation.
Another reason for insufficient attention paid to upcoming
traffic is engagement in parallel motor tasks, namely in walking.
An observation study and a photo-based questionnaire (Wiczorek
etal., 2016) suggests that both younger and older pedestrians do
not usually stop in front of a street to check for traffic. Instead,
they tend to keep walking and move their heads to check for traffic
while approaching the street. In an EEG-experiment using a dual-
task paradigm of combined real over ground walking and visual
signal detection, it was found that the number of missed visual
signals did significantly increase from standing to walking, but
only for older participants (Protzak etal., 2021).
Within the research group FANS, an assistance system has been
developed with the aim to support older pedestrians’ road crossing.
The system was developed to detect the street rather than
approaching cars because the latter is not possible yet. In order to
detect an approaching car, the sensors used for the system need a free
field. In a lot of urban street environments, this is not possible due to
obstacles such as trees, poles and, most importantly, parked cars. If,
in the future, cars are capable of car-to-car communication or, in this
case, car-to-device communication, detection of approaching cars
will bea helpful way to increase the safety of pedestrians.
However, the aim of the current system is to detect the street and
to remind the users to refrain from any parallel activities, namely, to
stop checking the floor and to stop walking. Instead, they should
focus their whole attention on the traffic. Detection of the street has
been realized through a combination of sensor fusion and machine
learning (Qureshi etal., 2018; Qureshi and Wizcorek, 2019). The
system, which is mounted to a walking frame, detects the curb stone
using a webcam in addition to an infrared-based LEDDAR sensor.
The detection rate has been optimized using CNN algorithms up to
an efficiency of more than 99%. The system was trained to detect
only the kerbstone between the pathway and the street when
approaching the street, but not the kerbstone between the street and
the pathway on the other side of the road.
Three different interfaces (auditory, thermotactile, and
vibrotactile) have been investigated in a laboratory experiment
with older participants (Wiczorek, under review).
1
The one that
was both efficient and had a high acceptance rate by the older
people was the vibrotactile interface. The vibrotactile interface was
realized through vibrating cuffs, worn at the upper arms. This
placement was chosen to direct users’ attention as close to the
traffic as possible when their first reaction is a shift of attention to
the application of stimulus (Bradley, 2009).
The prototype of the assistance system is shown in Figure1.
In two experiments, the assistance system was evaluated regarding
its efficiency to increase safety during road crossing as well as with
regard to the subjective workload and acceptance of the system.
The first experiment was conducted in a virtual reality (VR)
environment, and the second one was a field test.
VR evaluation study
For a long time, pedestrian simulation has been mainly video-
based and offered no or only short walking options. Since VR
technology is evolving, more sophisticated pedestrian simulators
have been developed, using head-mounted VR technologies.
These simulation environments have a higher coupling of
perception and action and allow for real walking. For a review see
Feldstein etal. (2018).
The advantage of highly immersive VR experiments compared
to video-based simulation environments is that they are much more
realistic and, thus, provide results closer to real-life behavior. VR
experiments allow for exposure of participants to traffic, without
putting them in actual danger. Furthermore, they are more controlled
than field tests. However, it has been shown that even with high
fidelity simulation, participants still do not exactly behave as in real-
world experiments (Feldstein etal., 2016). That is why wedecided to
combine both approaches, one controlled VR setting with actual
traffic and one field test in a very quiet zone with little traffic.
The aim of the VR experiment was to investigate the behavior
of older participants while road crossing with and without the
assistance system. They walked up and down a 10 m long track
and, during the walking, were presented with two different street
scenarios. For their own safety, they were equipped with a walking
frame during the whole experiment. The experiment was split into
two parts, one where the assistance system was activated, and
another one with the system turned off.
In both experiments, frequency of turning the head to the left
and the right, stopping in front of the street, i.e., approaching time,
1 Wiczorek, R. (under review). Evaluation of thermotactile and vibrotactile
cues to improve hazard perception of older pedestrians.
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Frontiers in Psychology 03 frontiersin.org
workload measures, and acceptance questions served as dependent
variables. It was expected that older pedestrians would stop more
often and move their heads more often when using the system than
when not using it. The workload measure was done to check
whether the assistance system would increase the workload as an
unintended side effect. No explicit hypotheses regarding acceptance
questions were made. They rather served to learn more about
participants’ attitudes towards the assistance system.
Materials and methods
“Ethik-Kommission des Instituts für Psychologie und
Arbeitswissenschaft (IPA) der TU Berlin” approved the study
under the name: “VR-Studie zur Wirksamkeit eines vibro-taktilen
Assistenzsystems für die Straßenquerung“(serial numbers
WI_06_20180817). All procedures were performed in accordance
with the Declaration of Helsinki, in compliance with relevant laws
and institutional guidelines. Written informed consent was
obtained from each participant and privacy rights were observed.
Participants
Twenty-three older subjects between the age of 65 and 83
(M = 73.3; SD = 5.6) were included in the analysis of this study. Ten
of them were male and 13 were female. They all walked on foot on
a regular basis. Participants were recruited via a participant tool
of the research group fans. For participation, they received a
compensation of 12€ per hour.
Research environment
The experiment took place in the “Berlin Mobile Brain/Body
Imaging Lab” (BeMoBIL) of the Dep. of Biological Psychology and
Neuroergonomics, Technische Universität Berlin. Participants
wore HTC VIVE VR glasses and were additionally equipped with
five trackers (feet, hands, and belly). The trackers and the glasses
were tracked by a room-wide installed camera system. Figure1
presents a participant with HTC VIVE during the experiment.
The scenarios were programmed with Unity. The VR scenes
covered a 10 m × 5 m corridor. Two street scenarios had been
developed for the experiment. Both showed urban environments.
Pictures of each scene are presented in Figures2, 3. For reasons of
safety and logistics, it was decided not to use a height difference
between the street and the footpath. Instead, the crossing consisted
of a so-called “drop kerb,” which in Germany is often realized by
raising the street instead of lowering the kerbstone. That allowed
participants to walk on even ground through the whole scene,
with a consistent view in the VR.
The whole walking distance inside the VR environment was
10 m, of which 7 m were in the street scene and 1.5 m to turn
around at each end. Participants started 3.5 m before the curb
stone. Vibration feedback was triggered when the subject was
2.25 m away from the kerbstone. This distance was chosen for
practical reasons to assure enough time to check for cars. When
entering the street, participants walked 3.5 m until the scene
stopped automatically (0.5 m before the end of the virtual street).
In half of the scenes, cars were crossing. They crossed before
and/or after the vibration feedback. Cars drove with a velocity of
28 km per hour. Cars appeared in a pseudorandom order. The time
the cars started was varied to make prediction impossible for
participants. Cars were triggered by the distance of the subject to
the street. This distance varied between 5.5 m, 4 m, and 3 m before
the vibration feedback, and 2 m, 1.5 m, and 1 m after the vibration
feedback was given. The blocks consisted of 18 trials each, with
nine trails containing cars. As it was a double lane road, cars could
come from both directions. The number of cars from the left and
right was counterbalanced.
FIGURE1
A participant wearing the HTC VIVE and using the walking frame during the VR experiment.
Wiczorek and Protzak 10.3389/fpsyg.2022.966096
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When a new scene started, participants could decide when to
start walking. When participants arrived at the other side of the
road (i.e., 0.5 m before the end of the road), the scene ended,
participants went into a grey room, where they received text-based
information in addition to a symbol that indicated to turn around.
They were then instructed to place their feet at a marked position
on the floor. When they were in the right spot, the next scene
started. The two different street environments were alternating.
The subject’s body was represented by either a male or female
avatar to improve immersion (Slater, 2009). The representation of
the walking frame, followed the hybrid prototyping approach
(Exner etal., 2016). It was physically present and touched by the
subjects, as well as also equipped with a tracker and visually
represented in the VR.
The original assistance system that detects the kerbstone was
simulated in the VR experiment. Thus, unlike the real system the
one used in the VR experiment was 100% reliable.
Procedure
At arrival, participants filled in the consent form and read the
instructions. Afterwards, they conducted the MoCA (Montreal
Cognitive Assessment, Nasreddine et al., 2005), an acuity test
(Landolt ring chart), and a test regarding contrast sensitivity (Pelli-
Robson chart), before answering a simulator sickness
questionnaire. Then, participants read the VR instruction, and
trackers were put on the hands, feet, and belly. The avatar was
calibrated to the person’s height. When everything was prepared,
participants had a 10- to 20-min training phase to familiarize
FIGURE2
Street scene 1 of the VR experiment.
FIGURE3
Street scene 2 of the VR experiment.
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themselves with the VR environment. Before and after the training
phase, they answered the SSQ (Simulator Sickness Questionnaire,
Kennedy et al., 1993). Afterwards, the assistance system was
introduced and its functions were demonstrated. Participants were
instructed to cross the streets as normally as possible, i.e., to take
safe decisions, but not to beunnaturally cautious. They were
informed that the assistance system was there to support road
crossing. However, they did not receive any instruction on how to
behave as a response to the vibration signal. It was not mentioned
that the system should support stopping and checking for traffic.
The actual experiment consisted of two blocks with 18 trials each.
Both blocks contained nine trials with cars and nine trials without.
The walking frame was used during the entire experiment for
safety reasons, but one block was conducted with the assistance
system switched on, and the other one with the system switched
off. The order of blocks was counterbalanced. After each block,
participants filled in the NASA TLX (NASA Task Load Index, Hart
and Staveland, 1988). When the experimental blocks were over,
they answered the acceptance questionnaire. Finally, participants
received financial compensation and were thanked for
their participation.
Dependent measures
Objective dependent measures were stopping (both feet on
the floor with a max. length of 5 cm between feet, for a min. time
of 2 s) frequency per block and head rotation frequency (straight,
medium left, medium right, complete left, and complete right).
Every orientation was defined as a window of 36° in the rotation
field of 180° in front of the participant. NASA TLX served as a
measure for subjective workload. Acceptance was assessed via the
three questions that are listed in Table1.
Results
Stopping frequency and workload were analyzed with 2 × 2
ANOVAs with repeated measures. Head rotation frequency was
analyzed with a 2 × 2 × 5 ANOVA. Significance level alpha was set
to 0.05. Values between 0.05 and 0.1 are classified as marginally
significant. Acceptance was analyzed descriptively. Assumptions
of sphericity were tested using the Mauchly test. In case of
violation, Greenhouse–Geisser corrected values are reported.
Stopping frequency
Stopping frequencies were analyzed using the sum of all stops
for the respective number of trials (i.e., 18 trials with/without cars,
and 18 trials with/without an assistance system). The main effect
for cars revealed significance with a large effect size, F(1, 22) = 9.27;
p = 0.006; η2p = 0.3. When cars were crossing, participants stopped
with a higher frequency (M = 2.72 SD = 3.82) than without cars
(M = 1.09; SD = 2.67), but the standard deviation was higher with
cars than without. Analysis of the main effect of the assistance
system revealed only a marginally significant result but had a large
effect size, F(1, 22) = 3.53; p = 0.07; η2p = 0.14. Participants stopped
more often with the assistance system (M = 2.2, SD = 3.38) than
without it (M = 1.63, SD = 3.1), and the standard deviation was
similar for both conditions. The interaction effect did not reveal
significance. Results are presented in Figure4.
Head rotation frequency
Head rotation frequencies were analyzed for the five
orientations within single trials. The main effect of cars was
significant and based on a large effect size, F(1, 22) = 7.68; p = 0.01;
η2p = 0.26. Participants moved their heads more frequently when
cars were passing (sum of all orientations: M = 8.0, SD = 6.1) than
without cars (sum of all orientations: M = 6.4, SD = 4.16), but the
standard deviation was higher with cars. The main effect of the
assistance system revealed significance and the effect size was large,
F(1, 22) = 5.72; p = 0.03; η2p = 0.21. The frequency of head movement
was higher with the assistance system (sum of all orientations:
M = 7.92, SD = 5.14) than without the system (sum of all orientations:
M = 6.51, SD = 5.07), and the standard deviation was similar for both
conditions. Results are presented in Figure5. The main effect for
orientation was also significant with a very large effect size, F(1.76,
38.81) = 44.78; p < 0.001; η2p = 0.67. The highest frequency was found
for the straight head orientation (M = 2.12, SD = 1.16), followed by
the medium right (M = 1.62, SD = 1.17) and the medium left
orientation (M = 1.67, SD = 1.38), and the lowest frequencies were
found for complete right (M = 0.91, SD = 0.66) and complete left
orientations (M = 0.89, SD = 0.74). The interaction between the
assistance system and orientation was marginally significant, with a
medium effect size F(2.39,52.52) = 2.82; p = 0.06; η
2p
= 0.11. The other
interactions were not significant. Results are presented in Figure6.
Workload
The overall workload did not differ significantly. It was
perceived as low (on a scale of 0–100) in both conditions, with the
TABLE1 Frequencies and percentages of answers to the three
acceptance questions regarding the assistance system in the VR
study.
Questions Answers
The
assistance
system
increases
traffic safety
Totally
disagree
Rather
disagree
Indifferent Rather
agree
Totally
agree
1 4.3% 2 8.7% 4 17.4% 9 23.1% 7 30.4%
A lot of
people would
like the
assistance
system
Totally
disagree
Rather
disagree
Indifferent Rather
agree
Totally
agree
0 0% 5 21.7% 5 21.7% 11 47.8% 2 8.7%
Would
youbuy such
a system?
Most
unlikely
Rather
unlikely
Indifferent Rather
likely
Most
likely
4 17.4% 2 8.7% 10 43.5% 3 13% 4 17.4%
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Frontiers in Psychology 06 frontiersin.org
assistance system (M = 10.17; SD = 10.1), and without it (M = 11.96;
SD = 10.96). The workload on the single scales was not significantly
different as well.
Acceptance
Participants were asked three questions regarding
acceptance of the assistance system, which are analyzed
descriptively. When being asked whether the assistance system
“increases traffic safety,” 70% of the participants indicated that
this was rather true or totally true. Moreover, 57% stated that it
was rather true or totally true that “a lot of people would like the
assistance system.” However, when asked how likely they would
beto “buy such an assistance system,” only 31% thought that was
rather or most likely. Response frequencies are presented in
Table1.
Discussion
In this evaluation study, the prototype of an assistance system
has been evaluated with regard to its capacity to change users’
behavior towards safety. The aim of the system was to make users
stop more frequently and to check for traffic more often. Results
indicate that the system had a positive effect on both.
Participants stopped more often when the assistance system
was activated than when it was switched off. However, the total
FIGURE4
Means of stopping frequencies in the VR experiment with and without cars, with (AS) and without (nAS) the assistance system.
FIGURE5
Means of head rotation frequencies in the VR experiment with and without cars, with (AS) and without (nAS) the assistance system.
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Frontiers in Psychology 07 frontiersin.org
numbers for stopping with the assistance system are still very low
(medium of 2.2in 18 trials).
Participants’ head rotation was used to measure their
frequency of looking for traffic. The assistance system increased
rotation frequency, independent of orientation. When the system
was switched on, participants looked for traffic more often. This
behavior was shown without explicit instructions on how to
behave in response to the vibration signals.
In addition, it was shown that the approaching cars were an
external trigger for stopping as well as for head rotation, which is
plausible. Interestingly there was no interaction between the
assistance system and crossing cars. That means, the system did
not only improve behaviors that were already in place but did
trigger the safer behavior also in trials without crossing cars.
Limitations
The floor of the laboratory environment was free from any
steps and other potential obstacles in both the external and the VR
vision. Thus, the main reason for engaging in parallel visual tasks,
checking the ground to prevent falling, was not really an issue in
this setting. Thus, it is possible that frequency of head rotation
would belower in an environment that requests more visual
checking of the floor, as was the case in the field test.
From a theoretical point of view, the experiment could only
show an increase in stopping and an increase in head rotation
behavior. With the current setup, it was not possible to combine
these two measures to understand whether they are related.
Further studies should investigate whether the increase in head
rotation is higher during the time the participants stop in order to
allow for a valid interpretation regarding the reduction of dual-
task activities.
Field test
Based on the promising results from the VR study, the next
step was to validate its results in a real street environment, i.e., to
evaluate the prototype of an assistance system in the field. The
behavior of pedestrians differs dependent on the type of crossing,
especially with regard to marked crossings (e.g., zebra, sunken
curb) versus other crossings (e.g., Schüller etal., 2020). There is
considerable dissent in the literature regarding the advantage of
marked crossings. On the one hand, crossing where there is no
official crossing is unexpected for drivers and, thus, adds the risk
of drivers overlooking pedestrians. On the other hand, pedestrians
behave less carefully at marked crossings, because they expect
drivers to stop, which is not always the case. To investigate whether
participants’ behavior differed at marked versus unmarked
crossings and, more importantly, to investigate whether this could
have an impact on the use of the assistance system, about half of
the crossings in the experiment were marked, and the other half
were unmarked.
The current experiment does not focus on road crossing
behavior, but rather on the use of the assistance system. When
being in a completely new situation, such as a VR environment,
the use of a walking frame that is normally not used, may not have
the same (potentially distracting) impact as it can have in the real-
world. To reduce the impact of the walking frame, participants
were using it in both conditions, with and without the assistance
system, as was also done in the VR study.
While there are, of course, already a lot of differences between
an experiment in VR and the field, in this case there was another
very important difference regarding the assistance system. The
aim of these two experiments was not to compare behavior in the
VR and the real environment but to evaluate the prototype of the
assistance system with the best combination of internal and
external validity. While the assistance system in the VR study was
perfectly reliable because its signals were triggered by the
simulation software, the assistance system used in the field test
was a real functioning prototype. Thus, there are a lot of
possibilities for errors made by the system (missing the curb stone
as well as generating false alarms), that can have an impact on the
behavior of the participants.
Results regarding the comparison of VR and pedestrians in
real environments are mixed. Schwebel etal. (2008) suggest that
decisions in VR and real street environments were highly
FIGURE6
Means of frequencies of different orientations of head rotation in the VR experiment with and without cars, with (AS) and without (nAS)
assistance system.
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correlated. However, Feldstein (2019) argues that the absolute
numbers can still differ significantly, even being highly correlated.
Feldstein and Dyszak (2020) found participants in the VR to take
riskier crossing decisions compared to the real-world.
Based on the previous studies that found similar but safer
behavior in real environments (Schwebel etal., 2008; Feldstein,
2019; Feldstein and Dyszak, 2020) compared to VR, it was
expected to find the same behavioral patterns. The workload was
assessed to make sure that the assistance system did not impose
additional workload on the users. Three questions regarding the
acceptance of the system were asked at the end of the experiment
to learn more about users’ needs and potential drawbacks of using
an assistance system.
Materials and methods
“Ethik-Kommission des Instituts für Psychologie und
Arbeitswissenschaft (IPA) der TU Berlin” approved the study under
the name: “Studie zur Nutzung eines Fußgängerassistenzsystems im
Straßenverkehr“” (serial numbers BRE_02_201808803). All
procedures were performed in accordance with the Declaration of
Helsinki, in compliance with relevant laws and institutional
guidelines. Written informed consent was obtained from each
participant and privacy rights were observed.
Participants
Twenty-six older subjects between the age of 65 and 85
(M = 73.15; SD = 5.38) were included in the analysis of this study.
Thirteen of them were male, 12 were female, and one preferred not
to say. They all walked on foot on a regular basis. Participants were
recruited via a participant tool of the research group fans. For
their participation, they received compensation of 12€ per hour.
Procedure
Participants started the experiment in a laboratory room at the
university. Upon arrival, participants filled in the consent form and
read the instructions. Afterwards, they conducted the MoCA
(Montreal Cognitive Assessment, Nasreddine etal., 2005), and an
acuity test (Landolt ring chart). The walking frame was used during
the entire experiment to keep the situation comparable with regard
to walking speed, etc. One way was conducted with the assistance
system switched on, and the other one with the system switched off.
The order of system use was counterbalanced. After all the tests had
been conducted, participants were brought outside. A helmet
equipped with a GoPro camera and vibration cuffs were placed on
the participants. The functioning of the assistance system was
demonstrated, and, subsequently, participants had time to familiarize
themselves with the walking frame and the assistance system. When
they were ready, the experiment started. Participants started at the
university and walked around the surrounding streets. In this area,
there is rather low traffic. The route they had to follow was marked
with chalk on the floor. They walked alone but knew that the
experimenters were nearby to help them in case they needed it.
When they arrived, they sat down on a bench with the experimenters
and filled in the SEA scale. After a break, they went the same way
back. When participants came back to the starting point, they
answered the SEA scale again as well as the acceptance questions.
Finally, participants received financial compensation and were
thanked for their participation.
The assistance system
The prototype consists of two sensors, one webcam and one
infrared sensor, a laptop, and an Arduino, as well as two vibration
cuffs. Sensors and laptop are mounted to a walking frame, the
Arduino was placed in a backpack, carried by the participants, and
vibration cuffs were placed on the upper arms of the users. The
system needs about 1 s (i.e., 15 frames) to analyse the surroundings
and to decide whether to generate an alarm or not. The system is
programmed to detect the kerbstone within a predefined window of
2 m +/− 1 m. The actual distance depends on the approaching angel
of the person and can thus vary between trials. If the system detects
a kerbstone it generates vibration signals at the cuffs via the Arduino.
The signal duration was 500 ms. Figures7, 8 show a participant using
the walker with the assistance system. After an alarm was generated,
the system did not generate another alarm within a time window of
5 s. This was done to avoid continuous alarms when participants had
to wait at the street. The system had a reliability of over 99% with the
test data (Qureshi and Wizcorek, 2019). However, in the real street
environment, a lot of objects were present, which had not been part
of the training data (trees, people, etc.) that caused the system to
generate false alarms. Since the system had not yet been trained for
this data and because it is not possible to calculate a false alarm rate
(the underlying number of events is unknown), correct rejections
(true negatives), and false alarms (false positives) are not analyzed.
Instead, the hits (true positives) and the misses (false negatives) of
the current study are used to calculate the hit-rate of the system,
which can becompared to the validation data from the laboratory
(Qureshi and Wizcorek, 2019). A more detailed analysis of the
functional part of the system in this study can befound elsewhere
(Qureshi, submitted).2
The route
The route was 0.5 km long and included 13 crossings, of which
seven were marked and six were unmarked. Examples of crossings
can beseen in Figures7 (marked), Figure 8 (unmarked). The route
consisted of normal crossings in a normal street environment but
was in a quiet area with low traffic density. In the streets with the
unmarked crossings, there existed no marked crossings nearby, so all
the pedestrians in this area were using these crossings on a frequent
basis. Unmarked crossings were chosen in spots where people
normally cross and it was made sure that the spots were
comparatively safe (free view, no junctions or merging traffic, etc.).
2 Qureshi, H. S. (submitted). User-Centered Development of a Pedestrian
Assistance System Using End-to-End Learning. Doctoral dissertation,
Technische Universität Berlin.
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Dependent measures
Dependent measures were the following: Objective measures
were head rotation frequency (left vs. right) per trial and
approach duration when walking towards the street. Head
rotation was measured via IMU (Inertial Measurement Unit). As
it turned out after the experiment that it was impossible to
measure stopping frequency via IMU, due to the near
nonappearance of this behavior, it was decided to use the
approaching time as an objective behavioral measure instead.
This was assessed via a GoPro camera. The workload was assessed
using the SEA scale (Eilers etal., 1986). The scale consists of a
vertical scale from 0 to 220 with verbal anchors. The SEA scale
was chosen instead of the NASA TLX because it can befilled in
faster. As the experiment took place between November and
December, it was aimed to keep the time participants had to sit
in the cold to answer questionnaires as short as possible.
Acceptance was assessed via three questions that are listed in
Table2.
Results
Head rotation frequency, approaching time, and workload
have been analyzed with ANOVAs for repeated measures.
FIGURE7
Participant in the field study using the walker with the assistance system at a marked crossing.
FIGURE8
Participant in the field study using the walker with the assistance system at an unmarked crossing.
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Significance level alpha was set to 0.05. Values between 0.05 and
0.1 are classified as marginally significant.
Hit-rate of The assistance system
According to the signal detection theory (cf. Swets, 1964), the
hit-rate of a system indicates how well the system detects the
defined targets. The hit-rate of the current experiment ranged
from 0.769 to 1 with M = 0.922, and SD = 0.068. This means that
the system failed to generate an alarm in 8% of the cases in which
a person approached a street. The actual hit-rate was significantly
lower than the hit-rate of 0.988 that was reached in the laboratory
(Qureshi and Wizcorek, 2019), t(51) = −6.944; p < 0.001.
Head rotation frequency
Rotation frequency was analyzed per trial. The main effect
of assistance system was marginal significant, F(1, 25) = 3.60;
p = 0.07; η2p = 0.13. Participants moved their heads more often
when using the assistance system (M = 0.82, SD = 0.4) than
without the assistance system (M = 0.76, SD = 0.41). The main
effects for type of crossing, F(1, 25) = 35.41; p < 0.001;
η2p = 0.59, and for position, F(1, 25) = 15.24; p = 0.001;
η2p = 0.38, revealed significance, but were further qualified by
two interaction effects; namely the significant interaction
between type of crossing x position, F(1, 25) = 105.18;
p < 0.001; η2p = 0.81, and the significant interaction between
position x head orientation, F(1, 25) = 54.52; p < 0.001;
η
2p
= 0.69. The first implies that participants showed a different
pattern of head movement depending on the type of crossing.
When at a marked crossing, they moved their heads more
often when still being on the footpath and less often while on
the street (marked crossing/footpath: M = 0.77, SD = 0.37;
marked crossing/street: M = 0.63, SD = 0.4). The opposite was
found for unmarked crossings. Participants moved their heads
less often when still on the footpath and more frequently while
already on the street (unmarked crossing/footpath: M = 0.49,
SD = 0.32; unmarked crossing/street: M = 0.1.28, SD = 0.53).
The second interaction describes that participants looked
more often to the left than to the right side, when still being
on the footpath (footpath/left orientation: M = 0.76, SD = 0.34;
footpath/right orientation: M = 0.49, SD = 0.34), while doing
the opposite while on the street; looking more often to the
right than to the left side (street/left orientation: M = 0.85,
SD = 0.46; street/right orientation: M = 1.06, SD = 0.48). The
main effect of orientation was not significant. Results can
beseen in Figures9, 10.
Approaching time
The main effect of type of crossing revealed significance, F(1,
25) = 117.38; p < 0.001; η
2p
= 0.82. The approaching time was longer
at marked crossings (M = 5.39 s, SD = 1.23 s) compared to
unmarked crossings (M = 3.64, SD = 1.34). The main effect of
assistance system and the interaction effect did not reveal
significance. Results can beseen in Figure11.
Workload
The workload assessed with the SEA scale did not differ
significantly. It was perceived as low (on a scale of 0–220) in both
conditions, with the assistance system (M = 25.0; SD = 23.93) and
without it (M = 25.87; SD = 22.85).
Acceptance
Participants were asked three questions regarding acceptance
of the assistance system, which are analyzed descriptively. When
being asked, “how did youperceive the assistance system during
the road crossing?,” only 30% answered helpful or rather helpful,
while more than 50% considered it irrelevant. Consistently, when
being asked how likely they would beto “buy such an assistance
system for themselves,” 70% stated they would not or rather not.
However, when being asked whether they would “recommend
such a system to an older person with problems in road crossing,”
almost 60% said yes or rather yes. Response frequencies are
presented in Table2.
TABLE2 Frequencies and percentages of answers to the three acceptance questions regarding the assistance system in the field study.
Questions Answers
How did youperceive
the assistance system
during road crossing?
helpful Rather helpful irrelevant Rather disturbing disturbing
1 3.7% 7 25.9% 15 55.6% 3 11.1% 0 0%
Would yourecommend
such a system to an
older person in need of
support with road
crossing
yes Rather yes indifferent Rather no no
13 48.1% 3 11.1% 5 18.5% 3 11.1% 2 7.4%
Would youbuy such a
system?
yes Rather yes indifferent Rather no no
2 7.4% 3 11.1% 2 7.4% 8 29.6% 11 40.7%
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Discussion
Results regarding head rotation offer interesting insight into
the crossing strategies of older pedestrians. In line with other
research (cf. Schüller etal., 2020), a different behavioral pattern for
marked and unmarked crossings was found. Participants in the
current study moved their heads less often to check for traffic
while at an unmarked crossing. Moreover, when crossing marked
crossings, they checked for traffic more often when still being on
the footpath before entering the street and less often when already
on the street. The opposite was found for unmarked crossings.
Participants did check less often before entering the street and
more often when already being on the street. In addition, they
looked to the left more often while still on the footpath and more
often to the right, while on the street.
Of course, there may be systematic differences between
marked and unmarked crossings that support this behavior apart
from the type of crossing itself (the size of the road, the visibility,
etc.). Thus, interpretation of different behavioral patterns at
marked and unmarked crossings is difficult. However, the
tendency to look more to the left on the footpath and more to the
right on the street is independent of the type of crossing. This
FIGURE9
Means of head rotation frequencies in the field experiment at marked and unmarked crossings with (AS) and without (nAS) assistance system.
FIGURE10
Means of frequencies of different orientations of head rotation in the field experiment on the footpath (t1) and on the street (t2).
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behavior may seem logical on a double lane road. However, it
incorporates the risk of neglecting the right side until being
physically on the street. A behavior that has been observed before
for older pedestrians (cf. Dunbar etal., 2004). It is due to the
age-related limitation of working memory, making it more
difficult for older pedestrians to integrate information from the
two orientations. When streets are bigger, islands are an important
instrument to improve road crossing safety for older pedestrians.
However, in small but still double lane streets, like the ones in the
current experiment, it may bethe only suitable strategy for older
pedestrians. Unfortunately, the use of the assistance system cannot
tackle this problem of limited workload. The only technological
solution that could overcome this dangerous situation is the
P2C-communication (pedestrian to car communication).
Results of the approaching time and type of crossing are in
line with findings for head rotation. Participants behave differently
at marked and unmarked crossings. A shorter approaching time
at unmarked crossings corresponds to the finding of increased
checking for traffic when being on the street. The assistance
system, however, had no impact on the approaching time. It did
not make people stop or even walk slower. In our opinion, the
absence of any effect of the assistance system on approaching time
may bethe result of a very strong automation of the road crossing
behavior that has been trained for years.
General discussion
The aim of these two studies was to evaluate whether the
prototype of the assistance system promotes safe behavior of older
pedestrians. It was supposed to increase head rotation as the
operationalization of looking for traffic and to increase stop
frequencies to reduce multitasking requirements. The VR study
offered the possibility to control the functioning of the assistance
system and investigate the potential interaction of the assistance
system with cars in a safe, but realistic environment. The field
study served to test the functioning of the real prototype and to
evaluate the impact of the assistance system on the behavior in a
real-world environment as well as potential interactions with the
type of crossing.
Stopping frequency
Even though the effect of cars on stopping frequency was
much higher than the effect of the assistance system,
participants in the VR study stopped significantly more
frequently when using the assistance system than without it. It
is important to mention that this effect occurred without
participants being instructed to do so and was also unrelated to
the stopping of cars. This finding is encouraging because it can
be interpreted as evidence for the intuitive design of the
assistance system.
However, in the real-world environment, the assistance system
did not approaching time speed significantly, much less did it
increase stopping frequency (which could not bemeasured due to
its rare occurrence).
In this measure, wefound the only difference between the two
evaluation studies. In our interpretation, the behavior shown in
the VR resembles the normative road crossing behavior, including
stopping at streets. The VR environment triggers behavior close to
normal, but at the same time allows the participants to reflect and
adapt their behavior. In the field study, however, it was not possible
to interrupt the routine of checking for traffic while walking
through the use of the prototype. It has to benoted, again, that
people were not instructed to stop in response to the vibration
signal. Thus, it is possible that the desired behavior could
beachieved with the help of instruction and training.
FIGURE11
Means of approaching time in the field experiment at marked and unmarked crossings with (AS) and without (nAS) assistance system.
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Head rotation
In contrast to the stopping frequency, i.e., approaching time,
that could not bemanipulated by the assistances system in the
real-world environment, participants did significantly increase
their head rotation in both experiments. The effect was stronger
in the VR experiment (η2p = 0.21) compared to the field test
(η
2p
= 0.13), where it was only marginally significant. One possible
reason could bethe flat and even ground in the VR experiment
not requiring further visual attention unlike the real street
environment. In both experiments, external factors such as
passing cars and type of crossing did also increase the frequency
of head rotation. However, in both cases no interaction with the
effect of assistance system was found. Furthermore, the increase
of head rotation frequency took place in both settings without
instructing participants to do so. This finding is a major success in
the development of the assistance system, and another strong
evidence for the intuitive design of the prototype. However, as
there are a lot of potential visual distractions in the real-world, it
will benecessary to instruct and to train the users in order to
achieve a long lasting behavioral change.
Workload
It is very important to remember that new technical systems
incorporate the risk of unintended negative side effects. One
common problem is them imposing additional workload. Thus, it
is a positive finding that the prototype of the assistance system did
not increase workload in the VR or in the field study. In both
experiments, the workload was experienced as low. That reflects
the quotidian nature of the task.
Acceptance
In line with the workload results, only 11% of participants in
the field study reported that the system interfered with their task
of road crossing, and more than half of the participants in the VR
thought the system increased traffic safety. However, only 30% of
participants in the field test felt the system would support them.
Consistently, the likelihood of buying such a system for themselves
was low in both studies (VR: 26%; field: 19%). That may seem
disappointing, considering the older pedestrians are the target
group of the prototype. However, the older people participating in
the two studies were all healthy and did not experience any
problems themselves. Thus, it seemed that they did not consider
themselves the real target group. That is supported by 59% of the
participants of the field study saying they would recommend the
system to people in need and 57% of the VR study saying that
people would like the system, indicating an overall appreciation
for the prototype. The likelihood of someone buying such a system
seems to be related to their self-perception. This might
be problematic as aging is a gradual process, and not always
transparent to oneself. Therefore, in addition to the development
of supporting technology, the awareness of potential risks must
beraised in the older population.
Comparison of VR study versus field test
Overall, the comparison of VR and field test results are in line
with previous findings: the overall behavioral tendencies are the
same (e.g., Schwebel etal., 2008). However, unlike in previous
comparisons (Feldstein, 2019; Feldstein and Dyszak, 2020), the
behavior in VR was not found to beriskier than in the real-world.
In fact, the opposite is true, as the behavior in the VR, including
stopping in the street was safer compared to the real-world
behavior. Conversely, it can beargued that results are in line with
the previous findings, as behavior in VR is more extreme than in
the real-world. From our experience, weconclude that VR is a
highly valuable way to do pedestrian research and research
regarding the technical support of pedestrians. Especially for
learning more about general behavioral patterns in a safe and
controllable, but still very realistic, environment. Nonetheless,
weconsider the validation of results in the real-world as inevitable
before drawing conclusions and giving suggestions.
Limitations
There are several differences between the two experiments
that reduce comparability of results.
The trigger mechanism for the two experiments was different.
The vibration in the VR was triggerd at the distance of 2.25 m and
was the same for each person in every trial. This distance was
chosen for practical reasons to assure enough time to check for
cars. In the field test, the system needed 1 s to analyse the data,
detection of kerbstone took place at a distance of 2 m +/− 1 m
from the street. Thus, the moment the vibration was given
depended on the walking speed and the approaching angle of the
person, which could vary between people and trials.
Reliability of the assistance systems used in the two
experiments was different. The functioning of the system in the
VR study was only simulated, thus a perfectly reliable system
could be provided. In the field test, the system was actually
working. It’s hit-rate was 0.922, which means it failed to generate
vibration cues in 8% of the cases when people approached the
street. Subjective trust was not assessed in the experiments.
However, it is very likely that participants trusted the perfect
system in the VR experiments more than the actual system.
Of course, the use of the walking frame is not ideal. Majority
of the older population does not use a walking frame. All
participants in the two experiments were healthy subjects not
using any type of walking aid in their normal life. Thus, the
walking experience in the two experiments was different from
their normal walking behavior. It is also possible that the use of
the walking frame did add additional workload to the situation.
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The use of the walking frame in the two experiments was
necessary, because it had to carry the system (including a laptop)
in the field test, and it was needed for safety reasons in the VR
experiment. However, for a later stage of development of the
assistance system, it is aimed to create a wearable device, that
could beused without a walking frame in a field experiment and
in reality.
The acceptance questions were chosen based on what seemed
to bemost suited for the respective experiment. However, two out
of three questions differed between the experiments, which makes
it difficult to compare the results. Data collection overlapped,
which made it impossible to “learn” from the previous experiment
for the next one.
It is important to mention that the walking situation was
artificial, even in the field test, and that there are several differences
between the two experiments. However, as both experiments are
within-subjects designs, the potential impact of cars, surrounding,
use of the walking frame, etc. were always present in both
conditions. Thus, effects found for the assistance system are not
confounded with anything else and can, therefore, becompletely
ascribed to the use of the system.
The two experiments represent two different ways of
evaluation, using the benefits of the respective methods (VR vs.
field). Even though the data is not comparable in a statistical
manner, the joint description of the two different studies offers a
lot of complementary information that allows for a solid overall
evaluation of the assistance system.
Practical implications
The assistance system that was evaluated is still in the state of
a very early prototype. Albeit encouraging results, a lot of
improvements are required. First of all, training of the detection
algorithm has to bedone again including the elements that caused
false alarms (such as trees or other people). In the future course of
further development, choice and placement of sensors should
allow the use of a wearable device and to dispel the need for a
walking frame. That would likely make the system appeal to a
larger subset of the older population. Additionally, a long-term
study has to beconducted in order to investigate whether positive
behavioral changes during the use of the assistance system
are stable.
Along with the development of technical assistance to support
older pedestrians, awareness campaigns are needed. Older people
should learn more about the underlying reasons for their higher
risk during road crossings. Of course, it is very difficult to change
behavioral patterns that have been practiced for years, but maybe
the understanding of underlying mechanisms can help.
Active pedestrian assistance like the prototype that was
evaluated in the current study is important alongside passive
support through driver assistance targeting pedestrian protection.
One approach that could close the gap between active and passive
support is the P2C communication, which would offer both
parties better options to avoid crashes.
Conclusion
The prototype of the assistance system that has been developed
by the FANS research group has been successfully evaluated in VR
and the real-world. Findings are promising as the assistance
system is able to increase the safety behavior of pedestrians in
terms of checking for traffic. Furthermore, the system did not
increase the subjective workload of participants, which could have
been an unwanted side effect.
Data availability statement
The raw data supporting the conclusions of this article will
bemade available by the authors, without undue reservation.
Ethics statement
The studies involving human participants were reviewed and
approved by Ethik-Kommission des Instituts für Psychologie und
Arbeitswissenschaft (IPA) der TU Berlin. The patients/
participants provided their written informed consent to
participate in this study. Written informed consent was obtained
from the individual(s) for the publication of any potentially
identifiable images or data included in this article.
Author contributions
RW was responsible for planning, conducting, and analyzing
the experiment as well as for writing the article. JP was responsible
for planning and conducting the experiment, and assisted with the
analysis and the writing of the article. All authors contributed to
the article and approved the submitted version.
Funding
This research was funded by the Bundesministerium für
Bildung und Forschung and the open access publication fee was
funded by the Technische University Berlin.
Acknowledgments
Thanks to Hasham Qureshi for development of the prototype
and data collection, to Youssef Bagueri and Benjamin Paulisch for
assistance with hardware and software for the experiment and data
analysis, to Florian Breitinger, Maciej Filipkowski, and Max Neufeld
for data collection, and to Samira Yarollahi for data analysis.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could
beconstrued as a potential conflict of interest.
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Frontiers in Psychology 15 frontiersin.org
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
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