Climate-Friendly Online Shopping Within the Green eCommerce

Project: A Fitting Tool to Determine T-Shirt Sizes Using Active Depth

Sensing

Alexander K. Seewald

, Thomas Wernbacher

, Thomas Winter

, Mario Platzer

and Alexander Pfeiffer

Seewald Solutions GmbH, L

archenstraße 1, 4616 Weißkirchen a.d. Traun, Austria

yVerkehrsplanung GmbH, Brockmanngasse 55, 8010 Graz, Austria

Universit

at f

ur Weiterbildung Krems, Dr.-Karl-Dorrek-Straße 30, 3500 Krems, Austria

Liberacerta e.U., Am Lindenhof 37/11, 8043 Graz, Austria

alexander.pfeiffer@donau-uni.ac.at

Keywords:

AI for Green, Characterizing Returns, Fitting Tool, Depth Cameras.

Abstract:

Within the context of the Green eCommerce project where we build tailored add-ons for webshops to in-

crease climate-friendly shipping, we analyzed reasons for returns using a modiﬁed rule learning algorithm

but found no actionable rules. However, since many returns are driven by wrong size information, we have

also developed a prototype Fitting Tool app that uses active depth sensing to measure several relevant body

measurements and uses these to estimate T-Shirt sizes. Although these body measurements could be shown

to be quite precise, T-Shirt sizes could only be predicted at low accuracy. On the other hand, self-reporting by

test users showed that the perceived accuracy was considered 1.5-3x higher. Analyzing this issue, it was found

that the reason for this is most likely manufacturer bias in reported size, which will be addressed in future

work.

1 INTRODUCTION

In recent years, online shopping has increased rapidly.

This trend was also being fuelled by the Covid 19 pan-

demic and, according to many experts, will continue

unabated in the future. As a result, e-commerce in the

Business-to-Consumer (B2C) sector recorded record

ﬁgures in 2021 in terms of turnover (9.6 billion eu-

ros in Austria according to Knabl et al. (2021)) and

in terms of postal parcels delivered (Sievering, 2020).

However, this ﬂood of parcels goes hand in hand with

the many negative consequences of a rapidly grow-

ing volume of goods transport on the last mile, which

manifest themselves in trafﬁc jams, noise pollution,

air pollution and a decreasing quality of stay in public

spaces.

Many study authors (BMVIT, 2015; DCTI, 2015;

BMK, 2020; Kolf, 2021) come to the conclusion that

online shopping only has a better ecological balance

than shopping in stationary retail stores under opti-

mal framework conditions (e.g. promotion of collec-

tive orders, climate-friendly means of transport and

avoidance of return shipments and same-day deliver-

ies). However, the package delivery situation is cur-

rently characterized by frequent multiple deliveries,

climate-damaging and underutilised means of trans-

port and, above all, high return rates, which amount

to up to 47% in the Austrian clothing sector (Kn-

abl et al., 2021). On the demand side, the situa-

tion is aggravated by the fact that end consumers in

online shops are often offered no or only very lim-

ited climate-friendly delivery options, which contra-

dicts the increasing sustainability awareness (Holt-

mann and Klitzsch, 2021) of many end customers.

This is where the preventive and customer-

oriented approach of the Green eCommerce research

project (Wernbacher et al., 2023) comes in. Within

a continuation of the previous project Think!First

(Wernbacher et al., 2019), contextually tailored add-

ons based on behavioural, technology-based and lo-

gistical interventions for the existing online shops of

the participating partners are designed, developed and

tested in practice. With the help of an unique com-

bination of a gamiﬁed loyalty system that rewards

920

Seewald, A., Wernbacher, T., Winter, T., Platzer, M. and Pfeiffer, A.

Climate-Friendly Online Shopping Within the Green eCommerce Project: A Fitting Tool to Determine T-Shirt Sizes Using Active Depth Sensing.

DOI: 10.5220/0012419500003636

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 16th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2024) - Volume 3, pages 920-927

ISBN: 978-989-758-680-4; ISSN: 2184-433X

users for high compliance, persuasive design princi-

ples that are characterized by visually highlighting re-

gional products with short delivery routes or collec-

tive orders, as well as AI-supported ﬁtting tools and

chat bots that automatically measure clothing sizes

and point out environmentally friendly delivery op-

tions, customers are encouraged to shop more con-

sciously – in the sense of a trafﬁc shift, trafﬁc avoid-

ance and trafﬁc optimisation.

Through the active participation of the practical

partners Julius Meinl am Graben, kauftregional and

ZERUM, the innovative add-ons can be tested com-

prehensively and practically for different objectives,

target groups and different product groups in real op-

erations over several months. In addition, the inte-

gration of the innovative logistics service Green to

Home from logistics partner ERIVE makes it possible

to analyze the entire process between online shop op-

erators, online end consumers, and package delivery

service providers. Thus, this holistic approach gen-

erates new and in-depth insights into the acceptance,

suitability and impact of innovative interventions in

online shops.

Non-ﬁtting garments are also a known factor to

strongly drive returns (Kristensen et al., 2013; Singh,

2015). To obtain precise body measurements, we de-

veloped a Fitting Tool in cooperation with partner

ZERUM. The Fitting Tool is an Android app that runs

on a small subset of mobile phones with active depth

sensing cameras. Due to the ability of such cameras

to exactly measure distances it was possible to rapidly

develop a prototype app to obtain body measurements

directly from depth images, using a pretrained body

pose keypoint detector.

In this paper we focus on reducing returns, both

by characterizing rules and by obtaining precise body

measurements using the Fitting Tool. We initially de-

scribe our results on using understandable machine

learning to analyze reasons for returns. In this project,

only ZERUM tracked and expressed concerns with

high returns, so we focus on its returns data. The anal-

ysis follows (Seewald et al., 2019) and also uses the

modiﬁed rule learning algorithm presented there.

Concerning the Fitting Tool, we ﬁrst present an

algorithm to exactly measure body sizes from depth

camera images and body pose estimates and evaluate

its accuracy on a small set of persons. We then use

a subset of these measurements to determine T-shirt

sizes using a standard size table as well as machine

learning algorithms.

2 RELATED RESEARCH

Zalando Corporate (2023) introduced a new feature in

their app that measures body shape by one front and

one side photo of a person in tight-ﬁtting clothes. It

is based on technology by company Fision which was

acquired by Zalando in 2020. Processing is locally

on the smartphone and photos are deleted afterwards.

The exact measurements are then used to search for

ﬁtting clothes. Currently, only women’s tops – includ-

ing dresses – can be searched for. Contrary to our ap-

proach, their approachs works on any smartphone and

does not require special sensors. However, no quan-

tative data on the precision of the obtained measure-

ments were reported and the integrated search is likely

optimized to deal with the expected inaccurate mea-

surements. The requirement for tight-ﬁtting clothes is

something that our system also needs as depth cam-

eras cannot see through clothes.

Singh (2015) analyzed reasons for returns within

Indian online market Flipkart, where mainly wom-

ens’ garments are sold directly by the manufacturers.

Apart from a detailed analysis of returns reasons they

also provided a minimal set of measurements for size

tables to reduce returns.

Simply changing the shown

size tables for nine manufacturers according to his

recommendations reduced returns dramatically: An

average reduction of absolute returns rate of 9% was

reported with a maximum of 46% – so manufacturers

saw their returns rate at best almost halved. They also

provided an analysis of returns reasons due to prod-

uct quality issues which were also a major cause for

returns within this online market, albeit less relevant

for our project.

Kristensen et al. (2013) present TrueFit, a sys-

tem to determine precise body measurements which

can reduce returns by up to 30%. However it re-

quires much effort by potential customers. TrueFit

works by combining extensive information provided

by customers on their height, age, weight as well as a

set of previously bought ﬁtting clothes with manufac-

turer, model type and given size to determine best ﬁt.

While it therefore tries to compensate both customer

and manufacturer bias, in its present form it ignores

body size temporal drift (i.e. changes in body size

over time).

Toktay (2003) analyzed different models to pre-

dict returns via synthetic data. They differentiated be-

tween modelling via periodical data where only the

number of sold and returned products is known (i.e.

Minimal set of measurements: breast width, waist cir-

cumference, shoulder circumference, sleeve diameter at

height; provide at least UK, US and EU-Sizes and at least

S,M,L,XL,XXL for simple sizes.

Climate-Friendly Online Shopping Within the Green eCommerce Project: A Fitting Tool to Determine T-Shirt Sizes Using Active Depth

Sensing

921

where it is not possible to identify products and de-

termine exactly which products were returned), and

modelling via individual data on product level (i.e.

where such a identiﬁcation is feasible). For the sec-

ond case – which corresponds to our data – they pro-

posed an Expectation Maximization model. No ex-

plicit modelling of the reasons for returns took place.

3 CHARACTERIZATION OF

RETURNS

ZERUM provided a list of 2,543 articles ordered of

which 325 had been returned. We combined the fol-

lowing data on products into one dataset:

• Order Information: Data and time of order and

payment, total amount paid, taxes by category,

customer, shipping and billing address.

• Product Information: Label, size, material,

price per item, description, manufacturer name.

Due to the small amount of data we used a random

sample of one third of the data (678 samples) biased

towards a higher proportion of returns (203 of the

325 returns) for training and two thirds (1,865 sam-

ples with 122 returns) for testing. Initial experiments

led us to remove features that are redundant, those

with unique and almost unique id values, and the ﬁeld

payment state which partially leaks the returns status

and thus yields unrealistically high predictive perfor-

mance.

The ﬁelds material, size, description, label, billing

address, shipping address and customer were free text

ﬁelds containing multiple words. We initially consid-

ered creating combined or separate word vectors for

them, however performance as measured by balanced

was always worse.

We characterized returns by the modiﬁed version

(described by Seewald et al. (2019)) of the well-

known rule learning algorithm, JRip, which is an

Open Source implementation of RIPPER (Cohen,

1995) within the data mining suite WEKA (Frank

et al., 2005). We chose JRip for its ability to produce

small concise rule sets that are easy to interpret.

We obtained a rule set with only three rules that

gives a precision of 0.247, recall of 0.385 and a bal-

anced F

measure of 0.301 on the test set – unfortu-

nately not much better than random guessing. Fur-

thermore, the obtained rules did not make empiricial

sense so we do not describe them in detail. We also

tested three other algorithms from the WEKA suite:

SMO, a support vector machine classiﬁer with lin-

ear kernel; Logistic Regression; and J48 which is a

reimplementation of C4.5; but obtained comparable

measures of 0.270, 0.263 and 0.307 respectively.

Deep learning algorithms were not considered due to

the smallness of the data set, and also because under-

standability of the models was a primary requirement.

We therefore proposed to ZERUM to provide bet-

ter size information in the webshop as it is well known

this leads to smaller returns rates (Kristensen et al.,

2013; Singh, 2015), and since we also found in earlier

work (Wernbacher et al., 2019) that a combination of

detailed size tables with persuasive design to empha-

size size tables by color and layout (phase II) reduced

the returns rate by 10.4%.

4 FITTING TOOL

As it is well known that incorrect size information

can lead to returns, and that adding manufacturer size

tables to a website can reduce returns signiﬁcantly

(Kristensen et al., 2013; Singh, 2015) by allowing

customers to more easily determine whether a clothes

item will ﬁt them, we developed a Fitting Tool app to

measure body size automatically and thus apply both

insights at once.

4.1 Hardware

Active depth cameras (Seewald and Pfeiffer, 2022)

were an obvious choice for this task, since they al-

low precise measurements without requiring large

amounts of training data that we would not have been

able to produce within the scope of this project.

First, to prevent having to use manufacturer-

speciﬁc interfaces to access integrated depth cameras

– which normally need a rooted device and would

thus never to able to run on the vast majority of mo-

bile phones – we chose Google ARcore as frame-

work and selected several phones from its compatibil-

ity list

which supported Time-of-Flight (ToF) hard-

ware depth sensors. We focussed on ToF as it is the

state-of-the-art for active depth sensing and in fact al-

most all currently available depth cameras use ToF as

sensing technology.

Due to view angle and since depth data was only

available from a certain minimum distance, the Fit-

ting Tool app needed two persons to operate it: one

person – the one to be measured – in front of the mo-

bile phone and one person behind it.

See https://developers.google.com/ar/devices?hl=en

ICAART 2024 - 16th International Conference on Agents and Artiﬁcial Intelligence

922

Figure 1: Left: Color image with overlaid OpenPose-

estimated body keypoints. Right: Depth image with over-

laid estimated body shape due to tracking. Both images

were manually cropped to improve visual alignment.

4.2 Body Pose Keypoint Detection

For detection of human pose keypoints, we chose the

Tensorﬂow Lite model of OpenPose (Cao et al., 2019)

since it was easy to integrate into an Android app that

uses Google ARcore and also relatively fast (about

0.6s to analyze one 640x480 image). For its body

pose detection, OpenPose only analyzed the color im-

age, so it was also necessary to create a spatial map-

ping from color to depth image. Google ARcore pro-

vides such a mapping, however it was insufﬁciently

accurate

, so it was necessary to extend it with a

general planar homography correction (Chum et al.,

2005). This correction reduced the residual error to

2.41 ± 1.15 pixels on a 640x360 depth image (i.e.

0.32% when compared with the diagonal) which was

deemed acceptable.

4.3 Measurement Module

The distance between body keypoints cannot be di-

rectly used to estimate body size measurements, since

the position of body keypoints always has some jitter

so their relative position versus the body border is by

no means ﬁxed, and human bodies are never perfectly

ﬂat.

We therefore implemented a 2D tracking/mea-

surement algorithm, MarkPose – shown in Alg. 1

– that starts at various body keypoints estimated by

OpenPose (already translated to depth image coor-

dinates) and tracks along lines derived from rela-

tive keypoint positions until the depth increases suf-

ﬁciently to ascertain the tracked point is outside

86.65 ± 5.61 pixels difference for eight manually

tagged points, however most of the difference seems to have

been a scale factor and a translation due to different aspect

ratios and sizes between depth and camera image.

the body. Each 2D point in the depth image can

be easily converted to 3D by utilizing the ARcore-

provided f

, f

parameters

, which was used

to measure the length of corresponding tracked lines

based on 3D points, and doubling this value to obtain

circumference. The tracking algorithm main func-

tion computeMeasurement is shown in Alg. 1 and

needs the parameters sP (starting point from body 2D

keypoints already converted to depth image coordi-

nates), min/max/stepAl pha (range and steps for lo-

cal search), depthDi f f (for body border recognition),

and depthImage (the depth image to be processed,

represented as two-dimensional image with millime-

ter distance values for each pixel). For comput-

ing SHOULDER, use sP = Neck, minAl pha = 0.0,

maxAl pha = 0.1, stepAl pha = 0.1 and depthDi f f =

75. For computing WAIST, use sP =

LHip+RHip

minAl pha = −0.25, maxAl pha = 0.25, stepAl pha =

0.025 and depthDi f f = 125.

We also tracked neck, left and right wrist, and left

and right elbow in a similar manner but did not use it

later for T-Shirt size estimation, so they are not shown

here. Fig. 1 shows a sample color and depth image

with all tracked keypoints and distances, including the

ones not used for T-Shirt size estimation.

This algorithm yielded measurements for a sin-

gle depth frame in around 2s, already including

OpenPose processing. To improve data quality, we

recorded 15 frames in sequence (taking about 0.5s)

and then analyzed all frames, reporting the arithmetic

mean over all measured values, resulting in a total

processing time of about 30s. We also tested report-

ing the median value which performed slightly better.

These values were later used for T-Shirt size estima-

tion.

To estimate the proposed system’s error, we manu-

ally determined SHOULDER and WAIST measures

from ﬁve people (three men, two women) using a

band measure (unit: centimeters), and compared them

with the median values averaged over 15 frames from

each person analyzed by our system. SHOULDER

had an error of 4.1% ± 2.34 versus the true value,

and WAIST had an error of 4.7% ± 4.38 versus the

true value.

4.4 T-Shirt Size Estimation

Initially we used a ﬁxed size table for SHOULDER

and WAIST, see Table 1. For T-Shirt size estimation

we chose the smallest size where both measured val-

ues – SHOULDER and WAIST – were below the

corresponding values according to the size table.

See function get3D() in Alg. 1

Climate-Friendly Online Shopping Within the Green eCommerce Project: A Fitting Tool to Determine T-Shirt Sizes Using Active Depth

Sensing

923

Algorithm 1: Tracking algorithm MarkPose with associated helper functions. The main function is computeMeasurement.

Function getDepth(p,depthImage)

/* Returns depth in millimeters at 2D position p = (p

, p

) from depth image. */

return depthImage

end

Function get3D(p,depthImage)

/* c

, f

are provided by the Google ARcore API. All units are converted

from millimeters to meters here. */

′

←

getDepth(p,depthImage)

1,000

;

′

← z

′

−c

;

′

← z

′

−c

;

p3D ←





′





;

return p3D;

end

Function getDist(s3D,e3D)

/* Standard vector distance between two 3D points */

return |s3D −e3D|;

end

Function computeMeasurement(sP,minAlpha,maxAlpha,stepAlpha,depthDiff,depthImage)

l ← Neck −

LHip+RHip

;

′

← l



0 −1

1 0



|l|

;

maxDist ← 0;

for α ← minAl pha; α ≤ maxAl pha; α ← α + stepAl pha do

′

← sP + α ∗ l;

initialDepth ← getDepth(sP, depthImage);

sLe f t ← sP

′

; stepLe f t ← 0;

while getDepth(sLe f t,depthImage) < initialDepth + depthDi f f do

sLe f t ← sLe ft + l

′

; stepLe f t ← stepLe f t + 1;

end

sRight ← sP

′

; stepRight ← 0;

while getDepth(sRight,depthImage) < initialDepth +depthDi f f do

sRight ← sRight − l

′

; stepRight ← stepRight + 1;

end

if stepLe ft > 0 and stepRight > 0 and

min(stepLe f t,stepRight)

max(stepLe f t,stepRight)

≥ 0.5 then

s ← sRight; dist ← 0; s3D ← get3D(s, depthImage);

while s ̸= sLe f t do

s ← s + l

′

; e3D ← get3D(s,depthImage);

localDist ← getDist(s3D,e3D);

dist ← dist + localDist; s3D ← e3D;

end

if dist > maxDist then maxDist ← dist;

end

return maxDist ∗ 2;

end

ICAART 2024 - 16th International Conference on Agents and Artiﬁcial Intelligence

924

Table 1: Fixed size table that was initially used for T-shirt

size estimation. All units in cm.

Size SHOULDER WAIST

XS 45.0 65.0

S 48.0 67.0

M 51.0 69.0

L 54.0 71.0

XL 57.0 73.0

XXL 60.0 75.0

At the time of writing this paper, we had obtained

data set with 73 different persons with known (self-

reported) T-Shirt sizes. On this dataset, using the

ﬁxed size table from Table 1, an accuracy of only

21.91% was obtained.

To improve on this, we reformulated this task as

a machine learning problem, mapping the measured

body sizes SHOULDER and WAIST (as arithmetic

mean over the measured 15 frames) to the known T-

shirt size. To enhance the data set, we added ad-

ditional features, namely the median of each mea-

surement (computed over the 15 analyzed frames)

and also included standard deviation for each mean,

plus the number of samples that could be processed

(i.e. which were not excluded by tracking errors). In

this way we obtained a model with three rules and

an leave-one-out cross-validation accuracy of 43.66%

which is much better but still not satisfactory.

(shoulderMedian <= 0.94) => class=XS

(20.0/9.0)

(waistMedian <= 0.76) => class=S (6.0/2.0)

=> class=XL (45.0/41.0)

Currently, we are awaiting new data to test this model.

However it should be noted that the new model only

predicts XS, S and XL – three of six classes – and

no other sizes, contrary to the size table-derived map-

ping.

Undetected tracking errors may explain the un-

satisfactory performance observed above. However a

manual analysis of 50 randomly chosen depth images

indicated that for 80% both measurements SHOUL-

DER and WAIST were correct; 14% had a tracking

error for SHOULDER (it was too short) and 6% had

a tracking error for WAIST (it was too long, in most

cases because hands were too near to the waist and

were measured together with it). Assuming these er-

rors are randomly distributed, the expected error after

averaging 15 frames is negligible.

Surprisingly, the qualitative evaluation reported

by the measured people themselves via question-

naire directly after the measurement was much better.

33.33% of people reported the measurement as ok,

27.78% as partially ok and 38.89% as incorrect. So

about 61.11% of the people considered the reported T-

0.2

0.4

0.6

0.8

1.2

1.4

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3

WAIST (in meters)

SHOULDER (in meters)

Figure 2: T-Shirt sizes reported by users versus SHOUL-

DER and WAIST measured sizes. Both SHOULDER and

WAIST measures are the arithmetic mean of at most 15 lo-

cal measurements from the same person, each computed

from one of the 15 consecutively recorded depth images.

More details see text.

shirt size to be ok or partially ok which is three times

better than would be expected from above quantitative

estimate when using the standard size table and still

about 50% better than the machine learning model

from above.

Manufacturer bias may explain this discrepancy

as people may consider more than one size to ﬁt, de-

pending on manufacturer and model. To test this hy-

pothesis, a visualization of actual measurements ver-

sus user-reported T-shirt sizes was created. Fig. 2

shows a plot of SHOULDER versus WAIST with

the reported sizes as differently shaped points, us-

ing arithmetic mean to average measurements of the

15 recorded depth images from each person. Fig. 3

shows the same data but uses median instead of

mean for averaging. It can be seen at ﬁrst glance

that no clear deﬁnition of sizes depending on either

SHOULDER or WAIST – or both – can be obtained.

The most likely explanation is therefore that different

manufacturers deﬁne T-Shirt sizes differently (per-

haps even for different models), so these are not uni-

versally comparable. In fact one tester mentioned that

he often buys T-shirts in two different sizes from dif-

ferent manufacturers but all of them ﬁt.

5 CONCLUSION

We have introduced the Green eCommerce project,

which is concerned with reducing returns and en-

couraging customers to shop more environmental-

consciously.

In the preliminary part of this paper, we have char-

acterized reasons for returns using compact rule lists

as in earlier work. However, the obtained rules were

Climate-Friendly Online Shopping Within the Green eCommerce Project: A Fitting Tool to Determine T-Shirt Sizes Using Active Depth

Sensing

925

0.2

0.4

0.6

0.8

1.2

1.4

0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3

WAIST (in meters)

SHOULDER (in meters)

Figure 3: T-Shirt sizes reported by users versus SHOUL-

DER and WAIST measured sizes. Both SHOULDER and

WAIST measures are median values of at most 15 local

measurements from the same person, each computed from

one of the 15 consecutively recorded depth images. More

details see text.

not suitably precise which is most likely due to the

much smaller dataset available here, and could not be

translated into actionable items. We therefore sug-

gested actions known to reduce returns from earlier

work.

In the main part of this paper we have described

the Fitting Tool app, to exactly measure body param-

eters using an active depth camera. We found that

direct body measurements (in cm) were quite pre-

cise, however T-Shirt sizes could only be predicted

at a much lower accuracy from this data either by a

ﬁxed size table or a machine learning model. How-

ever, feedback by users indicated that the perceived

performance of the system is about 1.5-3 times higher.

This may be explained by manufacturer and/or model

bias, leading to non-comparable T-Shirt sizes be-

tween manufacturers or models and also making peo-

ple more likely to consider more than one predicted

size as ﬁtting. Manufacturer bias of this kind was also

found in earlier work by Seewald et al. (2019).

5.1 Future Work

The main issue with the proposed Fitting app is that T-

shirt sizes can only be predicted at low accuracy due

to manufacturer bias. We aim to resolve this by us-

ing manufacturer-dependent size tables – either em-

bedded into the app itself and additionally chosen by

the user, or embedded into the webshop while restrict-

ing the app to search for ﬁtting garments by using the

more precise body measurements instead of the es-

timated T-shirt size. The latter would have the ad-

ditional advantage to allow normal webshop users to

also search by exact measurements rather than by in-

accurate size brackets.

We also plan to re-evaluate the accuracy of the

proposed body size measurement algorithm on a

larger data set of test persons with known body sizes.

A sufﬁciently large data set may even make it possible

to apply deep-learning training methods to this task,

possibly even end-to-end learning body sizes from

depth images, while a more moderately sized dataset

could be used to automatically tune the parameters of

the tracking algorithm.

One limitation of the present work is the rather

small number of mobile phones with active depth

cameras for which raw depth data is available and suf-

ﬁciently accurate. We will continue to watch out for

suitable mobile phones and plan to port the app on

those mobile phones that seem suitable.

Another limitation is that currently two people are

needed to use the Fitting app. We plan on evaluat-

ing whether leaning the mobile phone against the wall

is a feasible option (similar to (Zalando Corporate,

2023)), making the app also usable for just one per-

son.

ACKNOWLEDGEMENTS

This project was funded by the Austrian Research

Promotion Agency (FFG) and by the Austrian Fed-

eral Ministry for Transport, Innovation and Technol-

ogy (BMVIT) within the Mobility for the Future (Mo-

bilit

at der Zukunft (MdZ)) research program as a

project for the 2021 call on M-Era as project Green

eCommerce. We would like to especially thank Sigi

and Simon G. for testing the Fitting Tool app, invalu-

able feedback and ensuring that we received useful

data to improve on this research prototype.

REFERENCES

BMK (2020). PoviMob. Potentiale virtueller Mobilit

– Rahmen und Maßnahmen f

ur eine bestm

ogliche

Verkn

upfung virtueller und physischer Mobilit

at. Bun-

desministerium f

ur Klimaschutz, Umwelt, Energie, Mo-

bilit

at, Innovation und Technologie (BMK), Vienna, Aus-

tria.

BMVIT (2015). eComTraf - Auswirkungen von E-

Commerce auf das Gesamtverkehrssystem. Bundesmin-

isterium f

ur Verkehr, Innovation und Technologie, Wien.

Cao, Z., Hidalgo Martinez, G., Simon, T., Wei, S., and

Sheikh, Y. A. (2019). Openpose: Realtime multi-person

2d pose estimation using part afﬁnity ﬁelds. IEEE Trans-

actions on Pattern Analysis and Machine Intelligence.

Chum, O., Pajdla, T., and Sturm, P. (2005). The geomet-

ric error for homographies. Computer Vision and Image

Understanding, 97(1):86–102.

ICAART 2024 - 16th International Conference on Agents and Artiﬁcial Intelligence

926

Cohen, W. (1995). Fast effective rule induction. In Pro-

ceedings of the Twelfth International Conference on Ma-

chine Learning, San Francisco, CA, 1995, pages 115–

123. Morgan Kaufmann.

DCTI (2015). Klimafreundlich einkaufen – eine ver-

gleichende Betrachtung von Onlinehandel und sta-

tion

arem Einzelhandel. Deutsches CleanTech Institut,

Bonn. https://www.onlinehaendler-news.de/online-

handel/haendler/19828-online-handel-klimafreundlich

(accessed 4th January 2024).

Frank, E., Hall, M. A., Holmes, G., Kirkby, R., Pfahringer,

B., and Witten, I. H. (2005). Weka: A machine learning

workbench for data mining., pages 1305–1314. Springer,

Berlin.

Holtmann, U. and Klitzsch, C. (2021). Talk about

it - Nachhaltigkeit als PR-Thema. EHI Retail In-

stitute, K

oln. https://www.ehi.org/produkt/whitepaper-

talk-about-it-nachhaltigkeit-als-pr-thema/.

Knabl, W., K

ob, M., Meszaros, G., Prenger, C.,

Rischanek, U., Segal, D., and Weigl, A. (2021).

Bundesweite eCommerce Studie

Osterreich. Handelsver-

band. https://www.handelsverband.at/publikationen/

studien/ecommerce-studie-oesterreich/ ecommerce-

studie-oesterreich-2021.

Kolf, F. (2021). Neue Studie: E-Commerce hat eine bessere

Klimabilanz als station

arer Handel. Handelsblatt,

13:2021. https://www.handelsblatt.com/unternehmen/

nachhaltigkeit/einzelhandel-neue-studie-e-commerce-

hat-eine-bessere-klimabilanz-als-stationaerer-

handel/27082114.html (accessed 4th January 2024).

Kristensen, K., Borum, N., Christensen, L., et al. (2013).

Towards a next generation universally accessible online

shopping-for-apparel system. In Human-Computer In-

teraction: Users and Contexts of Use, Volume 8006 of

the series Lecture Notes in Computer Science, pp. 418-

427. Springer.

Seewald, A. K. and Pfeiffer, A. (2022). Magic Mirror: I

and My Avatar - A Versatile Augmented Reality Installa-

tion Controlled by Hand Gestures. In Proceedings of the

14th International Conference on Agents and Artiﬁcial

Intelligence (ICAART 2022), volume 3, pages 300–307.

Seewald, A. K., Wernbacher, T., Pfeiffer, A., Denk, N.,

Platzer, M., Berger, M., and Winter, T. (2019). Towards

minimizing e-commerce returns for clothing. In Pro-

ceedings of the 11th International Conference on Agents

and Artiﬁcial Intelligence (ICAART 2019), volume 2,

pages 801–808.

Sievering, O. (2020). The environmental impact of shop-

ping via the internet. In Central and Eastern European

eDem and eGov Days, pages 33–42.

Singh, K. (2015). Reducing Customer Returns in an Online

Marketplace. Dept. of Fashion Technology, National In-

stitute of Fashion Technology, Mumbai, India.

Toktay, L. (2003). Forecasting product returns. In Guide Jr.,

D. and Van Wassenhove, L., editors, Business Apects of

Closed-Loop Supply Chains, International Management

Series, volume 2. Carnegie Bosch Institute.

Wernbacher, T., Platzer, M., Seewald, A. K., Winter, T.,

Wimmer, S., and Pfeiffer, A. (2023). Green eCommerce.

In IEEE 9th International Conference on Computing, En-

gineering und Design (ICCED 2023), pages 1–5. IEEE.

In print.

Wernbacher, T., Seewald, A., Denk, N., Pfeiffer, A., Platzer,

M., and Winter, T. (2019). Think! ﬁrst: Inducing be-

havioural change through gamiﬁcation, persuasive de-

sign principles and machine learning. In European Con-

ference on Games Based Learning, pages 793–XXIV.

Academic Conferences International Limited.

Zalando Corporate (2023). Zalando launches size recom-

mendations based on customers’ own body measure-

ments. https://corporate.zalando.com/en/technology/

zalando-launches-size-recommendations-based-

customers-own-body-measurements. (accessed 15th

November 2023).

Climate-Friendly Online Shopping Within the Green eCommerce Project: A Fitting Tool to Determine T-Shirt Sizes Using Active Depth

Sensing

927