Enabling Multi-Material 3D Printing for Designing and Rapid
Prototyping of Deformable and Interactive Wearables
University of Oxford
Oxford, United Kingdom
Alexander Keith Eady
Ottawa, Ontario, Canada
Ottawa, Ontario, Canada
Figure 1: The workow pipeline for our design and fabrication approach follows 5 steps. In step 1, users rst use software
to design the deformable surface. For step 2, they then 3D print the surface with an o-the-shelf multi-material 3D printer.
During step 3, they then assemble the electronics using conductive thread and in step 4 they program the functionality. Step 5
demonstrates our deformable surface attached to the body, integrated onto a garment, and secured to another mobile object.
Deformable surfaces with interactive capabilities provide oppor-
tunities for new mobile interfaces such as wearables. Yet current
fabrication and prototyping techniques for deformable surfaces,
that are both exible and stretchable, are still limited by complex
structural design and mechanical surface rigidity. We propose a sim-
plied rapid fabrication technique that utilizes multi-material 3D
printing for developing customizable and stretchable surfaces for
mobile wearables with interactive capabilities embedded during the
3D printing process. Our prototype, FlexiWear, is a dynamic surface
with embedded electronic components that can adapt to mobile
body shape/movement and applied to contexts such as healthcare
and sports wearables. We describe our design and fabrication ap-
proach using a commercial desktop 3D printer, the interaction tech-
niques supported, and possible application scenarios for wearables
and deformable mobile interfaces. Our approach aims to support
rapid development and exploration of deformable surfaces that can
adapt to body shape/movement.
•Human-centered computing →User interface toolkits.
Permission to make digital or hard copies of all or part of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for prot or commercial advantage and that copies bear this notice and the full citation
on the rst page. Copyrights for components of this work owned by others than the
author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or
republish, to post on servers or to redistribute to lists, requires prior specic permission
and/or a fee. Request permissions from email@example.com.
MUM 2021, December 5-8, 2021, Leuven, Belgium
©2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ACM ISBN 978-1-4503-8643-2/21/05. . . $15.00
Fabrication, Prototyping, 3D Printing, Wearables, Mobile Interface
ACM Reference Format:
Aluna Everitt, Alexander Keith Eady, and Audrey Girouard. 2021. Enabling
Multi-Material 3D Printing for Designing and Rapid Prototyping of De-
formable and Interactive Wearables. In 20th International Conference on
Mobile and Ubiquitous Multimedia (MUM 2021), December 5-8, 2021, Leu-
ven, Belgium. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/
There is growing interest for fabricating deformable devices with
stretchable surfaces that can adapt to the body and movement
]. Research on these devices is diverse and explores form,
interaction, sensing capabilities, visualization [
], as well as
covering a broad set of applications [
]. Applications range across
generalized topics such as health monitoring to more specic ar-
eas such as reghting [
]. With a wide range of applications
come design challenges for conceptualizing, developing, and test-
ing devices that can be easily worn on the body, follow movements,
and/or attach to existing garments. As the domain of wearables
matures and expands, we see the need to further support the design
and development of wearable technologies, particularly with rapid
prototyping approaches .
Current fabrication techniques for developing deformable wear-
ables are often limited by thickness of electronics and mechanical
surface rigidity [
]. Ultra-thin on-body devices provide more ex-
ibility but often contain chemical compounds for printing with
conducting polymer that may cause irritation on direct contact
with sensitive skin (e.g. PEDOT:PSS/Gwent C2100629D1) [
textiles are commonly used for interactive deformable wearables
MUM 2021, December 5-8, 2021, Leuven, Belgium Everi et al.
and mobile devices [
], though they are often limited in sup-
porting rapid fabrication and customization of material properties,
e.g. stretchability/elasticity and bendability on demand and/or at
specic locations [
]. Much like e-textiles require specialized ma-
] (e.g. knitting machines), FDM 3D printers can also
be used to prototype interactive wearables with more complex
geometries and congurations [
]. Alternative methods of proto-
typing wearables require more advanced materials such as silicone
] and Polydimethylsiloxane [
] that come at high cost and
complexity (e.g. multi-layered approaches require more assembly).
The need for rapid prototyping mobile interfaces is acknowl-
edged within the eld of Human-Computer Interaction (HCI). For
example, Leber and Madrid [
] present a platform for rapidly pro-
totyping wearables that can be used to augment the body with an
array of sensors. However, it is limited to placing sensors only on
an existing garment (e.g. a vest). When designing wearables, a mul-
titude of forms and applications that can be explored and proposed,
particularly because these devices can be situated on both on the
body and on a garment. We see the need for a prototyping approach
that is adaptable and can support the design of wearables both di-
rectly on-body or within a garment as well as support embedded
sensing and interaction capabilities. We also emphasis the need
for context dependent wearables which provide device functional-
ity based on the context of use [
]. To achieve this, we highlight
the need to support rapid and accessible fabrication approaches.
Additionally, research [
] indicates a need for rapid prototyping
methods to expand the design space for fabricating deformable and
interactive mobile devices.
Our core motivation is to provide the mobile research and de-
sign communities with an alternate method to rapidly fabricating
deformable wearable and mobile prototypes that can be worn on
skin (Figure 2A), integrated in garments (Figure 2C), or attached
to portable objects (Figure 1). We demonstrate this by presenting
a rapid and customizable fabrication approach that can support
a range of forms, is easily augmented with o-the shelf compo-
nents, and is mobile. By utilising Computer Aided Design (CAD),
we support fast iterations (print times and modications) and in-
expensive and easily sourced materials, which speed up assembly
with commonly available tools.
We demonstrate how multi-material 3D printing can be utilised
as a rapid prototyping method (Figure 1) for developing wearables
with our proof of concept prototype (Figure 2). We employ multi-
material Fused Deposition Modeling (FDM) 3D printing where:
(1) Flexible lament provides deformable and stretchable surfaces
and, simultaneously. (2) Conductive lament is embedded to cre-
ate interactive areas, through capacitive touch circuits, within the
stretchable surfaces all in a single print. The surface design includes
gaps and channels that, post-printing, accommodate Light-Emitting
Diodes (LEDs) to support output visualization (e.g. for at a glance
feedback) together with capacitive sensing capabilities. See Fig-
ure 2D for details. The structural design of our device prototype,
FlexiWear, allows it to deform and follow the form of the wearer’s
body during movement as it can be worn on-body or attached to a
garment (Figure 2A-C). Please see Supplementary Material for the
STL 3D models of our prototype for 3D printing.
Our main contribution is a
design and fabrication approach
(Figure 1) for developing deformable wearables with integrated
interaction and visualisation capabilities realised through multi-
material 3D printing. By integrating exible and conductive materi-
als simultaneously during printing, we rapidly create customizable
interactive wearable surfaces, at low-cost, where the tile design
enables the surface to t around body parts. Our
proof of concept
(Figure 2), a thin and stretchable wearable surface that
easily adapts to on-body or in-garment movement and deforma-
tions. Our approach and resulting prototype demonstrates how
multi-material 3D printing together with the specied structural
surface design oers rapid fabrication of deformable and interactive
2 RELATED WORK
We explore current work related to fabrication approaches for wear-
ables, deformable user interfaces, and how they support the devel-
opment of our design and fabrication approach as well as nal
2.1 Fabrication Approaches for Wearables and
Fabrication approaches for rigid wearables (e.g. commercial smart-
watches) follow well established electronic and fabrication proto-
typing methods [
]. Below, we detail the fabrication ap-
proaches for wearables commonly adopted for deformable surfaces.
We also discuss general fabrication methods that include electronic
prototyping within 3D printing.
2.1.1 Fabricating Stretchable Surfaces . With the increase in per-
sonal fabrication [
] 3D printing oers a more widely accessible
approach for developing wearables as they are commonly found
within maker-spaces and even some households. Rivera et al. 
illustrate how 3D printing and textiles complement, with textile-
embedded 3D printed objects that are exible and stretchable. Wong
and Hernandez [
] review current additive manufacturing pro-
cesses for 3D printing. Schumacher et al. [
] present a method to
characterize the mechanical behavior of structured sheet materials
allowing users to capture deformational behavior under stretch-
ing and bending. With their approach users can explore various
structures and design/create materials with specic desired mi-
cro mechanical properties. Ultimately, their work allows people to
dene and fabricate structures with a variety of desired material
properties, particularly for bending and stretching. Silicon based
fabrication techniques can also be used to develop deformable wear-
Laser cut solid segments can also be attached to stretchable
Spandex to fabricate exible surfaces, specically for producing
shape-changing applications [
]. Similarly, LASEC [
] is another
example of a fabrication technique that utilizes the rapid nature
of laser cutters for developing deformable circuits with custom
stretch-ability. Their prototypes enable both stretchable and trans-
parent wearable devices to be rapidly fabricated using a single
layer of conductive material. By utilizing multi-material 3D print-
ing our approach provides design freedom in 3D space and more
customization options for post-print components that can speed
Enabling Multi-Material 3D Printing for Designing and Rapid Prototyping of Deformable and Interactive Wearables MUM 2021, December 5-8, 2021, Leuven, Belgium
2.1.2 E-Textiles for Wearables. E-textiles comprise electronic com-
ponents directly incorporated with fabrics to create interactive
]. This includes recent work that explores touch-
less gestural input with interactive fabrics [
]. To build interactive
surfaces for wearables with the uidity of textiles and interactive
features, e-textiles employ conductive yarns, optical bers, printing
with conductive ink, and connecting electronic components with
]. While these approaches can be
used for wearables and mobile devices [
], they pose several
fabrication limitations. Fabrication requires extensive manual as-
sembly and electronic components can be exposed to the wearer or
others nearby . Woven optical bers  can create cloth-like
materials that act as exible displays without exposing wiring and
electronics to users, however, this technique requires an external
infrared signal input device (e.g. laser pen). FunCushion [
duces a method for discretely integrating electronics within textiles.
Sugiura’s et al. [
] pressure sensing method uses infrared sensors
hidden within household soft objects for interaction and integrated
lights for visualization to create 3D cloth displays.
Digital fabrication methods for deformable textile surfaces also
enable end-users and designers to create interactive fabric-like ap-
]. However, these techniques do not yet have es-
tablished rapid fabrication processes that are reproducible without
a high level of technical skill. Sketch&Stitch [
] begins to establish
a DIY interactive design process for rapidly fabricating e-textiles by
mapping user’s drawings directly to computer-controlled embroi-
dery machines. Though this system removes barriers to users with
lower technical skill, this approach still requires specialized ma-
chines (e.g. computer-controlled embroidery machines) unlike 3D
printers that provide more generalized functionality in comparison
]. Similar to computer-controlled embroidery machine use for
e-textiles, McCann et al. [
] demonstrate how computer-controlled
knitting machines utilize automatic assemblies of high-level shape
primitives (tubes, sheets) into low-level machine instructions to
construct knitted objects. Though this approach is promising for
minimizing assembly, the user is limited to yarn materials and are
limited to specic automated knitting machines. More conventional
fabrication technologies, such as 3D printers, are more commonly
available in maker spaces and libraries.
2.2 3D Printing with Embedded Electronics
Printed exible circuits are increasingly used for a range of applica-
tions as they create bend or touch sensors [
], typically made using
inkjet printing. They often require additional assembly, e.g. attached
to 3D objects or within enclosures [
]. Lee et al. [
] provide a
review on 3D printed smart devices for 4D printing. Particularly,
they detail recent work on multi-material 3D printing with electri-
cally functional materials including conducting, sensing, insulating
and semiconducting materials that has led to the development of
smart devices such as 3D structural electronics, sensors, batteries,
etc. In the eld of soft robotics 3D printing has also been utilized
to create pneumatic actuators with embedded sensing [
use of multi-material 3D printing reduces assembly time, with the
core device fabricated in one print. Schmitz et al. [
] present a
fabrication pipeline to design and 3D print capacitive touch sensors
for interactive objects. EVA Moccasin [
] also uses a 3D printing
approach in creating wearables, specically for prototyping smart
shoe soles. We follow similar approaches, creating capacitive touch
sensors with multi-material 3D printing.
Savage et al. [
] describe the design and development of inter-
active devices by embedding optical light tubes within 3D printed
objects. A subtractive algorithmic approach generates space within
3D models to accommodate post-print manual insertion of compo-
nents like sensors or actuators for interaction. Our focus is similar,
preparing the surfaces during the design phase to include sensors
(i.e. using conductive lament) or to enable electronics to be em-
bedded after with minimal assembly [
] (e.g. at most three steps).
2.3 Interaction with Context Dependent
Context dependent wearables provide device functionality based on
the context of use [
]. Work in this are illustrates several important
design requirements for closely worn devices. Wearables must (1)
adapt to and with the body’s movement, and (2) support the organic
and dynamic shape of the human body alongside our changing
needs in dierent settings. Snaplet [
] provides context dependent
functionality with sensors embedded in a exible display to detect
deformation. Similarly, Facet [
], a multifaceted digital bracelet,
recongures to match user needs.
Wearables can also adapt to and support interactions that occur
o the device and even o the body. Doppio [
], a recongurable
smartwatch, allows one of its two touch sensitive displays to be de-
tached and shared with others. SleeD [
] is a sleeve-like wearable
that supports interaction with touch-sensitive walls, other devices,
and people. Context aware wearables can also adapt to dynamic so-
cial factors. AugmentedForearm [
], a sleeve worn wearable with
multiple displays, transitions between public and private states
based on arm orientation and direct user interaction. This work
highlights the importance of designing wearables that acknowl-
edge how the public and personal aspects of clothing can overlap.
Our approach supports the design of context aware and adaptable
wearables. The conductive lament used during printing, creates
integrated capacitive sensors within the deformable surface with
no additional assembly required. By monitoring capacitance and
establishing thresholds we can detect whether it is worn on or o
the body, along with changes in its shape, or additional touching.
2.4 Flexible and Stretchable Interfaces and
Deformable User Interfaces (DUIs) can be deformed as a means
of interaction through physical actions like squeezing, bending,
and stretching [
]. They support a range of tangible interactions
and are diverse in their degree of exibility and malleability [
]. Jensen et al. [
] present TransPrint, a method which
oers a way of creating bendable interactive displays using as
electrochromic manufacturing technique. Prototypes developed
from these methods can adapt to t dynamic organic shapes like
the human body - making them ideal for wearable devices [
Current wearables commonly employ exible touch-screens [
or LEDs embedded in a rigid enclosure (e.g. FitBit). Olberding et al.
] highlight that wearable displays must be stretchable to ensure
the device does not impede human motor systems when the user
MUM 2021, December 5-8, 2021, Leuven, Belgium Everi et al.
is turning and bending for instance. Non-stretchable wearables
limit the rotation and twisting capabilities of the user’s body parts.
Transparent and exible woven electrodes [
] and smart fabric
] are a promising direction, though commercially
they are yet to be adopted for stretchable interfaces.
Work on thin skin overlays supports thin and stretchable wear-
able input devices [
] and tattoo-based approaches [
though these approaches have yet to go beyond prototyping wear-
ables. Thin skin overlay wearables, such as SkinMarks [
supports display output, deformation sensors, and context/body-
pose dependent input controls though often require chemical com-
pounds for printing and masking with conducting polymers. These
processes are often time consuming and the chemical compounds
involved (e.g. PEDOT:PSS conducting polymer) may not be suitable
for prolonged direct contact on skin. To support a more accessible
and rapid fabrication approach we focus on more commercially
available techniques for fabrication (e.g. 3D printing) without the
need for harsh chemical use directly on skin.
Some wearables use tiled displays loosely connected and directly
attached to the skin, to ensure the human motor system and move-
ment remain unaected [
], though this limits the resolution and
scale of visualizations. Wearable displays also use exible elec-
trophoretic displays [
], arrays of large tiles [
] where multiple
screens are attached in a row [
], or augmented e-textiles [
Functional nano-materials for wearables are a promising direc-
tion for slim devices that seamlessly adapt to human body and skin.
However, recent works highlight that high cost and lack of accessi-
bility limit these fabrication processes [
]. FlexiWear utilizes
3D printing and CAD for developing exible and stretchable wear-
ables that meet key interaction principles for DUIs. Our fabrication
approach reects the vision of Olberding et al. [
] for exible
devices, demonstrated by FlexiWear, whose stretchable properties
support use on body parts, such as the knee, which undergo a wide
range of motion.
3 DESIGN AND FABRICATION APPROACH
The main premise of our fabrication approach is to use multi-
material 3D printing to rapidly create exible surfaces that have
embedded interactivity, are deformable, and can be worn by a user.
Figure 1 shows the end-to-end workow of our design and fabri-
cation approach which consists of 5 steps, where steps 1 and 2 are
iterative. To demonstrate the versatility of multi-material 3D print-
ing for prototyping we developed FlexiWear, a prototype device
that can be worn on-body or attached to a garments (Figure 2A-C).
The design comprises of tiles (sizes 10–15 mm) that are linked by
thin s-shaped sections, which allow the device to bend and stretch
easily. Below we provide an overview of our workow pipeline
together with details for 3D modeling design rational and materials
required to replicate the wearable prototype.
Figure 1 shows the workow of our design and fabrication ap-
proach where for
, we use Computer Aided Design (CAD)
software for 3D modeling a custom deformable surfaces with in-
teractive sections and areas for embedding electronics. In
multi-material FDM 3D printing with exible lament is used to
produce the deformable surfaces that also support capacitive touch
sensing capabilities using conductive lament. Steps 1 and 2 are part
Figure 2: Our 3D printed prototype with capacitive touch
sensing and integrated LED electronics. Demonstrating in-
teraction techniques for pressing (A), bending (B), and
stretching (C) on the hand and integrated into a pair of jeans.
Breakdown of prototype elements (D).
of an iterative process where the design 3D model can be readjusted
after printing test prototypes. For
, we assemble the device
by placing electronic components into designated cavities in the 3D
printed surface and then ’sewing/threading’ together the circuit us-
ing conductive thread. During
, a small micro-controller such
as an Arduino Nano or the wearable Adafruit Gemma is used with
Arduino IDE to program the electronics. Finally for
, once the
surface is fully assembled and functioning it can be attached either
to the body using double sided body tape [
], attached to clothing
using pins, or other objects using conventional double sided tape
or super glue.
3.1 Step 1: Design of Deformable Surface
We use Fusion360 to model tile and link designs. With each tile
linked, the print behaves as a continuous exible surface. Figure 3
shows our 3D model design example. The tile dimensions (15x15x2
mm) can accommodate miniature electronics (e.g. surface mount
LEDs) and are linked together during the printing process where, in
aggregate, they form a exible and lightweight interactive prototype
device. A gap of 5 mm between tiles, where a curved link connects
each tile, produces a stretchable surface. For interaction, a layer of
conductive lament (0.5 mm depth) is embedded during printing
on each tile and is connected through the links (Figure 3A).
We use NeoPixel LEDs [
] for their miniature size and com-
patibility with wearable applications. A space ranging between
5x10x1.5mm (min) to 12.75x12.75x2.80 mm (max) in each tile can be
used to set an LED. We used white exible 3D printing material [
which also served to diuse light from the LEDs throughout each
tile, making them more like large pixels in a display. The number
of tiles and LEDs reects the resolution of the wearable. With more
tiles, more complex visualizations can be created. In our design of
the FlexiWear prototype, we use between two to three gaps (1x1
mm each) in every tile that allow conductive thread or insulated
electromagnetic wire (0.5 mm) to be threaded through the proto-
type and provide current and data for the LED chain (Figure 3B).
We daisy chain the electronics in sequence (Figure 7) to address
individual components. Below, we describe the design iterations
of the tiles and links and how their geometry aects bendability,
stretching, and conductivity.
Enabling Multi-Material 3D Printing for Designing and Rapid Prototyping of Deformable and Interactive Wearables MUM 2021, December 5-8, 2021, Leuven, Belgium
Figure 3: Prototype tile and link 3D model design close-up
that is merged in aggregate to create a FlexiWear device.
3.1.1 Tile Design. First, we explored the impact of varying tile and
link dimensions and shapes to support surface deformation (Figure
4), which is aected by the arrangement of the tiles and the links
between them. Tiles in a triangular conguration (Figure 4B) allow
60-degree bends on an angle parallel to the triangle sides. This limits
deformation to provide a exible, but not cloth-like, surface. For
our nal prototype, we use square tiles as they enable a 90-degree
bend in four directions without any obstructions. We limit the tile
depth to 2mm, leaving the overall prototype lightweight. This also
reduces printing time and material required for fabrication.
Figure 4: Initial tile design explorations with square (A), tri-
angular (B), and hexagonal (C) polygons.
3.1.2 Link Design. Secondly, we explore links that allow stretching
of 3D printed surfaces. Inspired by 3D printed spring mechanisms
], our stretchable links use exible lament which has elastic
properties. After deformation, tiles regain their original shape like
most auxetic style materials [
]. The stretch factor between tiles
is facilitated by the “S” shape and link length. This link connection
design creates an anisotropic exibility with limited stretching.
This means that the stretching ability is limited along the direction
of the power lines (Figure 5D). To overcome this limitation more
power lines can be added each link, much like a spring has more
coils to increase the length of stretch.
Deformation of the 3D printed surface is aected by width and
thickness of the link. In our initial link design, we used straight
lines to connect each tile to understand how width and depth of
the exible material aects bending in aggregate. We printed link
widths of 0.5mm, 1mm, and 2mm. We found that 1mm link was
more durable than the 0.5mm link and also more stretchable than
the 2mm link. Our original link designs were 2 mm wide (Figure
4A/C), from our design iterations we recommend link width of 1
mm and depth of 1 mm for maximum exibility and durability. A
wider link (Figure 5A) reduces bending, resulting in a more rigid
form. Thinner links (Figure 5B) bend easily under less force. Our
nal link design (Figure 5C), uses a “S” shape that allows horizontal
stretch (Figure 5D).
Figure 5: Link design explorations; wider vertical link de-
sign (A), thinner vertical link (B), nal horizontal curved
link (C), curved link stretched (D).
Table 1: List of printing parameters with their respective val-
ues used to fabricate our prototype on the Ultimaker 3.
3.2 Step 2: 3D Printing, Materials, and
We utilize multi-material 3D printing using the Ultimaker 3 dual
extrusion FDM 3D printer. Table 1 provides a breakdown of pa-
rameter values for each material. For the exible material we use
commercially available Cheetah Snow White 95A TPU [
diameter, 1kg at $85.00 per spool) for fabricating a 2D array of tiles
that are linked together to create a stretchable surface (Figure 2D).
Each spool of exible lament makes 15 surfaces (length = 185mm,
width = 95mm, depth = 3.9mm) where cost per unit is $5.67. For
the conductive material we use Protopasta Electrically Conductive
Composite PLA [
] (2.85mm diameter, 500g at $49.99 per spool)
to print capacitive touch sensors within the exible surfaces [
Approximately half a spool is used to print 24 surfaces. Though
conductive PLA is not a exible lament, when applied as a single
layer (0.2 mm) and encased in exible material, it can behave as
such. Gaps in the tiles allow for LEDs to be inserted within the
surface and channels for wiring and conductive thread (Figure 3B)
allow us to embed electronics.
In terms of electronic components, for the micro-controller we
use either an Arduino Nano [
] or a wearable Adafruit Gemma
]. For visualisation, we use either single colour Adafruit LED
] or more advanced colour programmable Adafruit Flora
RGB Smart NeoPixel v2 [
]. We calculate the full cost of fabricating
one prototype can ranges between $20-$34, particularly depending
on the choice of micro-controller used.
MUM 2021, December 5-8, 2021, Leuven, Belgium Everi et al.
Figure 6: Step by step work-through for assembling a prototype using conductive thread and RGB LEDs . Thus includes;
placing LEDs into designated cavities (A); sewing conductive thread through LED chain (B-C); insulating thread with clear nail
polish (D); and connecting the thread using crocodile clips (E-G) to secure it to a micro-controller for testing (H).
3.3 Step 3: Assembly and Embedding
To assemble the prototype we use conductive thread to connect
the electronic components together. Figure 6 provides a step by
step guide for assembly for a chain of four RGB LEDs. We begin by
placing LEDs in the designated cavities making sure the text is the
right way up (Figure 6A). We then secure conductive thread through
all GND holes in the chain of LEDS (Figure 6B). We recommend
threading the conductive thread through the holes at least 6 times
before moving onto the next LED and repeat this process for V+,
Data IN, and Data OUT holes (Figure 6C). To insulate the conductive
thread, rst place white card dividers between each LED square and
coat all conductive thread and LEDs with clear nail polish, spreading
it generously (Figure 6D). Remove the white card dividers and wait
for polish to dry for at least 2 hours. Once the polish is dry cut
the thread between Data IN and Data OUT (Figure 6F) and wrap
remainder of conductive thread around alligator clip (Figure 6E-G).
Finally connect the alligator clips to a micro-controller board and
test the LEDs work (Figure 6H).
We also used insulated electromagnetic wire (0.5 mm) on a
smaller-scale prototype (tile dimensions 10x10x1.8 mm) where the
wire allows the device to hold its shape, unlike with thread. After
repeated twisting and bending the wire does become fragile. To
mitigate the risk of breaking we recommend burning o the insu-
lating coating on the ends to expose the conductive copper beneath
and applying solder directly on the wire to reinforce connections.
3.4 Step 4: Circuity and Programming
Figure 7 shows the circuit diagram details for setting up RGB LEDs
for visualisation and touch-sensing using conductive lament for
interaction. For touch sensing we use the Arduino Capacitive Sens-
ing Library [
] in conjunction with a 60 OHM resistor connected to
the conductive lament. We use three pieces of conducive thread
per row of LEDs, threaded into the channels in the tiles (Figure 3B).
Capacitive readings from the conductive lament can detect when
the device is worn by a user and when it is removed—e.g. contact-
ing skin or lying on a table. The capacitive sensor library enables
two or more Arduino or Adafruit Gemma pins to be a capacitive
sensor that can detect the electrical capacitance of the human body.
The sensor setup requires a medium to high value resistor (e.g. 100
kilohm - 50 megohm) and a piece of wire that is connected to the
conductive lament on the end using copper tape or a crocodile
clip (Figure 6G).
Figure 7: Circuit diagram for electronics, showing printed
conductive tracks (yellow) and LEDs connected with conduc-
tive thread (blue).
3.5 Step 5: Attachment
Once the surface is fully assembled and functioning it can be at-
tached to the body using double sided body tape [
], attached to
clothing using pins, or other objects using conventional double
sided tape or super glue. The device can also be easily secured
onto the hand like a bracelet using clips or secured to other cloth-
ing/garments by directly sewing it on with thread.
4 PROTOTYPE OVERVIEW
Our prototype (Figure 8) is a exible 2D array of 8x5 tiles (15x15x2
mm and 5 mm apart), each connected with a curved link (13x1x1
mm). To support rapid and iterative prototyping the design of the
surface requires minimal print material, as the 3D models are thin (2
mm depth) and do not need support structures. The lack of support
structures reduces printing time for our initial prototype (195x95x2
mm) to 3h 45 min. A smaller-scale version (145x70x1.8 mm) takes
1h 45 min to print.
Enabling Multi-Material 3D Printing for Designing and Rapid Prototyping of Deformable and Interactive Wearables MUM 2021, December 5-8, 2021, Leuven, Belgium
Figure 8: FlexiWear (195x95x2 mm) conforms to body shape
and is easily worn on hand (A) with light (B) and hard (C)
pressing, integrated with a pair of jeans (D), pressing on bent
knee (E) and stretched on bend knee (F).
4.1 Interaction Capabilities
We introduce interaction techniques supported by FlexiWear (Fig-
ure 2 and Figure 8), this includes; (1) capacitive touch sensing and
(2) deformation detection on skin and stretching with movement.
The interaction techniques below use the capacitive sensing library
for Arduino .
4.1.1 Device Body Placement. We demonstrate interactions using
two body placement examples. Figure 8A-C shows our prototype
as a hand worn device that wraps, glove-like, around the wearer’s
hand. The hands and wrists are common locations for wearables
]. Figure 8D-F shows our prototype integrated into a pair of
jeans to detect knee bends. We use pins to attach the FlexiWear to
the jeans. 3D printed devices like FlexiWear are robust and adapt
to the body during movement. We describe these use cases below.
4.1.2 Touch Interactions. FlexiWear detects proximity of touch on
tiles using capacitive thresholds set with the Arduino Uno. The
conductive lament tracks (Figure 8) spans each row of tiles. The
change in the capacitive sensing library’s raw value output is a
result from the deformation of the exible lament and bringing
the user’s nger closer to the conductive lament (i.g., shorten-
ing the discharge cycle by sensing proximity of a conductor). We
measure this change in output value for capacitive touch sensing
to determine how close is a user’s nger on the tile when they
touch it. Figure 8A shows green LEDs activated when a user lightly
touches a tile. Figure 8B shows red LEDs light up when a user
touches the tile harder, as the capacitance reading increases above
a set threshold. For interactive functionality, this capability to dis-
tinguish between light touches, where the nger is not so close to
the conductive lament, and harder touches, where the nger is
closer to the conductive lament, can be used as input control for
common and uncommon tasks. For example, as a music control to
pause a song a user can lightly touch a tile (e.g. common task). To
skip a track (e.g. uncommon task), they press harder on the tile.
This eliminates the need for both single and double touches.
4.1.3 Deformation Detection. Figure 8D-F shows FlexiWear adapt-
ing to bend and stretch with electronic circuits (i.e. a chain of LEDs)
embedded within the prototype. Using capacitive input from the
conductive material, we can detect human limb exing and bending
based on proximity of the skin to the conductive tiles when the
sirface is stretched over a joint like the knee or elbow. For example,
When the knee is extended, FlexiWear’s surface is more loose tted
and contracted (Figure 8D), this is shown with the LEDs deacti-
vated. When the knee is at 90 degrees, the surface is expanded and
stretched across the knee, making the conductive lament have
closer contact with the bare skin of the knee. Blue LEDs on the
surface are activated (Figure 8F) to indicate that bending is detected.
The system can simultaneously detect two separate inputs, one
from the user’s touch and a second from deformation with their
body. By assigning one row of capacitive sensors a lower threshold
for knee bending detection and another row a higher activation
threshold for touch, the system can accommodate both. Figure 8E
shows simultaneous knee bend and nger touch detection with a
change in LED color (Figure 8D).
4.1.4 On-body Interaction. Using capacitive sensing we can detect
when FlexiWear is not worn on the body. When the prototype is
placed on the non-conductive table (e.g. wood), the capacitance
reading is at its lowest. Once FlexiWear is placed on skin, there is
an increase in capacitance from the conductive lament making
close contact to the skin. This enables the device to distinguish
between worn and not worn and could be used to control device
modes, e.g. enter power saving mode when it is not being actively
worn. Further explorations are needed for conductive surfaces (e.g.
metal tables). These same capacitive sensors can be re-purposed
for dierent uses through both software or hardware modications
(either by re-printing or with tools post-print). For example, we can
detect knee bending based on the amount of contact the surface
makes with the skin once it is stretched. The more the knee is bent,
the greater the contact of the conductive material with the skin,
resulting in a higher capacitance input reading. 3D printed exible
surfaces can also stretch based on the interconnected link designs
as demonstrated by Schumacher et al. [
]. Generic stretch sensors
can also be incorporated to measure stretch when no skin contact
is made with the surface.
Here we discuss the practical advantages of our design and fabri-
cation approach, elaborating on design considerations, expand on
future work, as well as highlight current limitations and how we
aim to address them.
5.1 Advantages of Approach
5.1.1 Tile Design. To create the FlexiWear prototype, a user only
needs to create one tile and one link in a CAD environment (e.g. one
square with a link). The set is then replicated (using the copy and
move function available in most CAD environments) to form an
array of tiles that are linked together. After, tiles and linkages can
be modied and connected in dierent congurations to change
the overall shape and properties of the device. We believe that this
replication focused CAD design approach reduces the complexity
of the design process during prototyping. Additionally, once the
user is satised with their digital design, they 3D print the surface,
insert post-print components, and make a connection between the
conductive lament to an Arduino using an Alligator clip or silver
MUM 2021, December 5-8, 2021, Leuven, Belgium Everi et al.
5.1.2 Reduced Complex Assembly Requirements. Recent work in
e-textiles and wearables consist of multilayer fabrication processes
that require extensive manual assembly and advanced laser micro-
machining that is capable of processing thin metal lms [
Similarly, stretchable thin lm wearables consist of multi-layer
] that require handling chemicals such as phosphor
and silicon-based organic polymers. These approaches oer in-
creased technical complexity due to their multi-layer structures. To
reduce technical complexity and support a more accessible design
and development method, our fabrication approach consists of two
primary elements that are integrated into a single-layer wearable
surface: (1) The multi-material surface layer that integrates exible
and conductive lament during the 3D printing process to support
input capabilities without the need for additional electronic sensors.
(2) The additional layer of electronic components, like LEDs, that
are inserted within the 3D printed stretchable surface for output
visualizations. These components can be embedded in the exible
surface. Though our simple device prototype consisting of two
elements, conductive track for capacitive sensing and an array of
LEDs, there are still manual assembly requirements. Specically, as
each LED has to be manually inserted into a tile and soldered there
is a scalability trade-o, where more LEDs increase the manual
5.1.3 Rapid Fabrication and Replicability. By utilizing multi-material
3D printing, with integrated conductive and exible laments, we
can reduce the cost of time and provide fabricators with broad con-
trol of the prototyping process. With this method is it possible to
mimic the deformable properties of e-textiles, dictate the rigidity
and stretch of all or some parts of the prototype, add and alter
capacitive circuits, and modify the printed substrate to support dif-
ferent components for embedded interactive features. We highlight
the rapid nature of fabricating FlexiWear style prototypes: where a
195x95x2 mm surface takes 3h 45min to 3D print. A smaller-scale
version (145x70x1.8 mm) takes 1h 45 min to print. Inserting LEDs
in the surface and wiring took an additional 30 min. Fabricating a
fully functioning wearable prototype using our approach took, in
total, 4 hours and 30 min. To demonstrate the potential for repli-
cability of our fabrication process we built 25 prototypes in total,
21 of which have been used as toolkits for a Masters’ remote work-
shop on prototyping wearable technologies. Each prototype packed
and sent to students homes. Each student was able to be set-up
and assembled the prototype during a Zoom workshop without
any technical failure. We believe this demonstrate the robust and
replicable nature of our prototypes.
5.2 Design Considerations
5.2.1 Stretch Factor and Durability. When 3D modeling exible
links and tiles, designers must take scale into consideration. A
thicker link ensures a more robust connection and less chance
of breakage when stretched with greater force but limits overall
malleability. The stretching capabilities of FlexiWear also depend
on where conductive lament is located. Conductive PLA can bend
when encased in exible material and printed in a single layer (e.g.
0.2 mm). The long-term robustness of this adaptation is unexplored
but sucient for iterative prototyping and testing.
To increase stretch-ability, the link length can be increased, or
its shape changed. The stretching factor works like a 2D spring coil,
where the number of “coils” and their length aect the stretching.
For example, if the link has one coil with length of 10 mm between
two tiles, then the stretched gap between those two tiles will be
10 mm. If the link has two coils with length of 10 mm then the
gap will be 20 mm. Using 10 mm links, a 5-tile array with 4 links
can extend up to 40 mm from its non-deformed size. At rest, a
maximum gap of 2 mm between each tile should be maintained to
ensure the tile array behaves as a singular surface. Based on our link
design explorations, we recommend curved links over right-angles,
particularly for durability. Figure 5C shows the nal link design to
be curved where it attaches to the tiles. This curvature reduces the
risk of tearing the link. The design consideration for link design
promotes durability whilst the surface is stretching.
5.2.2 Sensing Capabilities. Additional design questions on sensing
capabilities that support future work have also emerged. Precari-
ously, how long can the conductive tracks can be and still be able
to detect touch at the end? The conductive lament resistance can
be decreased by making the printed lines thicker or wider, however
this will be more dicult to bend or deform the surface. There are
additional design considerations on the geometry of the conductive
lines for optimal sensing and detection. The choice of the resistance
between the pulse pin and reading pin can also aect the interactive
capabilities of the surface. The physical setup includes a medium to
high value (100 kilohm - 50 megohm) resistor between the send pin
and the receive (sensor) pin. One trade-o with larger resistors is
that the sensor’s increased sensitivity means that it is slower. Also,
if the surface is placed on exposed metal, it is possible that the send
pin will never be able to force a change in the receive sensor pin,
and the sensor will timeout.
5.3 Future Work
In future work we aim integrate a wider range of sensors and
components to support input and output capabilities, such as Light
Dependent Resistors (LDRs) and actuators like muscle wires/shape-
memory alloys (SMAs). We also aim to explore a range of body
placements and experiment with tile designs and links. Currently,
the 3D printed FlexiWear devices are limited by the size of the print
bed. To create larger surfaces, we will explore patchwork designs
with conductive interlinks. Designs that ll gaps created during
deformation are important, currently gaps can catch on clothing or
In terms of design accessibility and democratizing technology
], we hope our approach will be adopted by Maker culture. By
publishing our 3D model online in open source modeling libraries,
we aim to expand the reach of 3D printed wearables, beyond e-
textiles, to hobbyists and designers. Through making our models
open-source (e.g. Thingiverse.com) we hope to facilitate designing
prototypes and bespoke wearables.
In future work, we also aim to integrate self-actuation capa-
bilities as detailed by Roudaut et al. [
] or incorporate modular
origami robots [
] for actuation. Finally, we envision a range of
quantitative and qualitative user studies to evaluate how partici-
pants prefer interacting with our prototype and use these ndings
to inform usability paradigms for deformable devices and support
the exploration for potential adoption of these devices. We aim to
develop a range of application examples that utilize the deformable
material properties and interactions supported by FlexiWear. In the
area of robotics we envision the surfaces as robotic skins that can
stretch and ex with the joint movement of a robotic arm. Int he
area of digital health, we also aim to use FlexiWear as devices used
for working-out to potentially improve the people’s awareness of
their bodies while exercising. Dierent exercises require dierent
on-body sensing, such as push-up (device on the elbows), or squats
and dead lifts (on the knees).
In terms of input and output electronics, we acknowledge that ca-
pacitive sensing and LEDs have been extensively used in HCI. For
this work, these components were used to demonstrate the gen-
eralizability and accessibility of this prototyping method. Though
exible printed electronics oer better conductors (silver nanopar-
ticles) over 3D printed conductors (graphene-based lament), those
techniques are not as aordable or accessible. Additionally, we have
limited validation for the usability of our approach as there is no
user study or initial user feedback provided which would allow
to gauge whether the approach is easy-to-use. For our next step,
we aim to demonstrate the utility of our approach with a qualita-
tive user study, following a similarly methodology for toolkit user
validation as pre-existing work .
5.4.1 Post-Printing Assembly Requirements. The post processing
does require some assembly to be done by hand (e.g. securing
the conductive thread to the LEDs and placing LEDs within the 3D
printed enclosure). This does partly lose some of the advantages that
automated 3D printing brings, nevertheless connecting the LEDs
and placing them into an enclosure is a menial task in comparison
to the multi-layer assembly processes fabrication techniques such
as ElectroDermis [
] or Stretchis [
] oer. Additionally, there is
potential for a user to develop favourable attraction towards a pro-
totype that they assemble manually for themselves. Nevertheless,
for future work, our method can be further automated by adopting
techniques from latest work in acoustic fabrication with automated
contactless pick and place of electronic components [
] to further
reduce assembly requirements.
5.4.2 Circuit Limitations. Currently realized structure only allows
for row-based sensing and activation of the LEDs. This is because
our demo has no electronic connections between rows and all ca-
pacitors are in a series in one row. This design decision was pre-
dominantly intended to reduce the complexity of the 3D printed
structure. A full matrix like conguration would be a more desir-
able structure design for individual tile sensing capabilities. For
more ecient capacitive touch sensing a structured matrices [
design would be more desirable in future iterations of the prototype.
The LED control for our demo prototype (Figure 8) is also row by
row control which is a limitation. Using PCB RBG LEDs with an
additional input data pin can provide individual control to each
LED as illustrated in Figure 7.
5.4.3 Technical Clarifications and Scalability. The current sens-
ing capabilities of our prototype requires further testing. A large
body of work within Human Computer Interaction explores these
capacitive sensing techniques and highlights current challenges
and limitations with simplistic circuitry and limited location based
] that capacitive sensing oers. Currently, we promote
this method for early prototyping and our initial bend interaction
tests with capacitive sensing provide enough accuracy for rough
input sensing. Capacitive sensing can be prone to noise and can
potentially show dierent characteristics when the prototype is on
the skin or on cloth. We manually congured the input thresholds
for each prototype when placed on the skin or on cloth. In future
work we also aim to integrate other forms of sensing for input into
our prototypes. User-aware sensing could include an accelerometer
to provide a more accurate input when the device is placed on a con-
ductive surface such as a metal table. The system can potentially be
improved as currently each cell requires a separate control system
if not daisy chained in sequence. Multiplexing can support multiple
analog and digital signals over a shared medium. Alternatively, as
mentioned above, a full matrix structure design with both column
and row connections can also provide a more scalable design.
5.4.4 Flexibility and Stretching. The electrical wires, connection,
and architecture of the tiles create an anisotropic exibility with
limited stretching. This means that the stretching ability is limited
along the length of the lines (Figure 2C). This limited stretching
can be overcome by adding more lines to each link, much like a
spring has more coils to increase the length of stretch.
This work explores how multi-material 3D printing can support
the fabrication of deformable wearables with interaction and vi-
sualization capabilities. Our multi-material 3D printing approach
focuses on an aordable and accessible fabrication process that
does not require highly technical or expensive fabrication tools for
prototyping deformable wearables. Through our FlexiWear proto-
types, we demonstrated that this approach is capable of fabricating
thin deformable surfaces that are exible, foldable, and stretchable.
We believe that the wearable electronics community can benet
from utilizing multi-material 3D printing for fabricating wearables.
Additionally, we also aim to introduce even novice makers and hob-
byists to developing their own stretchable and exible wearables
using 3D printers.
Our core motivation is to provide the research and design com-
munities with an alternate rapid and accessible means of fabricating
deformable wearable prototypes that can be worn on skin (Figure
2A) or integrated in garments (Figure 2C). We demonstrate this
by presenting a rapid and customizable fabrication approach that
can support a range of wearable forms and functions, is easily
augmented with o-the shelf components, and modiable with
standard tools. By designing in digital environments, we support
fast iterations (print times and modications), inexpensive and eas-
ily sourced materials, which speed up assembly with commonly
To summarize, the key contribution of this work is our unique
tile and surface design that supports low-cost fabrication approach
for developing interactive deformable wearable prototypes using
multi-material 3D printing. Our deformable wearables (FlexiWear)
adapt to various body shapes, joints, and can be integrated in gar-
ments. Specically, with minimal time and assembly requirements.
MUM 2021, December 5-8, 2021, Leuven, Belgium Everi et al.
We highlight how FlexiWear demonstrates that multi-material 3D
printing oers rapid fabrication of prototypes that can be used to
test a wide range of wearable and mobile concepts.
Big thank you to Alfrancis Guerrero for helping with the 3D print-
ing. This work was supported and funded by the National Sciences
and Engineering Research Council of Canada (NSERC) through a
Discovery grant (2017-06300). The research was also supported by
Amazon Web Services in the Oxford-Singapore Human-Machine
Adafruit. 2021. Adafruit Gemma v2. Retrieved Feb 6, 2021 from https://www.
Adafruit. 2021. Adafruit LED Sequins. Retrieved Feb 6, 2021 from https:
Adafruit. 2021. Adafruit NeoPixel LED. Retrieved Feb 6, 2021 from https:
Humza Akhtar and Ramakrishna Kakarala. 2015. Ecient capacitive touch
sensing using structured matrices. In Computational Imaging XIII, Vol. 9401.
International Society for Optics and Photonics, 94010O.
Marco AB Andrade, Tiago S Ramos, Julio C Adamowski, and Asier Marzo. 2020.
Contactless pick-and-place of millimetric objects using inverted near-eld acous-
tic levitation. Applied Physics Letters 116, 5 (2020), 054104.
Arduino. 2021. Arduino Nano. Retrieved Feb 6, 2021 from https://store.arduino.
Paul Badger. 2018. Capacitive Sensing Library. Retrieved Sept 10, 2020 from
Patrick Baudisch, Stefanie Mueller, et al
2017. Personal fabrication. Foundations
and Trends®in Human–Computer Interaction 10, 3–4 (2017), 165–293.
Christoph H Belke and Jamie Paik. 2017. Mori: a modular origami robot.
IEEE/ASME Transactions on Mechatronics 22, 5 (2017), 2153–2164.
Leah Buechley, Mike Eisenberg, Jaime Catchen, and Ali Crockett. 2008. The
LilyPad Arduino: using computational textiles to investigate engagement, aes-
thetics, and diversity in computer science education. In Proceedings of the SIGCHI
conference on Human factors in computing systems. 423–432.
Jesse Burstyn, Paul Strohmeier, and Roel Vertegaal. 2015. DisplaySkin: Exploring
pose-aware displays on a exible electrophoretic wristband. In Proceedings of the
Ninth International Conference on Tangible, Embedded, and Embodied Interaction.
Lina M Castano and Alison B Flatau. 2014. Smart fabric sensors and e-textile
technologies: a review. Smart Materials and structures 23, 5 (2014), 053001.
Victor Cheung, Alex Keith Eady, and Audrey Girouard. 2017. Exploring eyes-free
interaction with wrist-worn deformable materials. In Proceedings of the Eleventh
International Conference on Tangible, Embedded, and Embodied Interaction. 521–
William Cheung and Sudip Vhaduri. 2020. Context-Dependent Implicit Authen-
tication for Wearable Device User. arXiv preprint arXiv:2008.12145 (2020).
Kenneth E Evans and Andrew Alderson. 2000. Auxetic materials: functional
materials and structures from lateral thinking! Advanced materials 12, 9 (2000),
Aluna Everitt and Jason Alexander. 2017. PolySurface: a design approach for rapid
prototyping of shape-changing displays using semi-solid surfaces. In Proceedings
of the 2017 Conference on Designing Interactive Systems. 1283–1294.
Aluna Everitt and Jason Alexander. 2019. 3D Printed Deformable Surfaces for
Shape-Changing Displays. Frontiers in Robotics and AI 6 (2019), 80.
Fennerdrives.com. 2021. Cheetah
Flexible 3D Printing Filament 95A
TPU - Industrial Strength Material. Retrieved Feb 6, 2021 from
GHEORGHE Florea, RADU Dobrescu, D Popescu, and MATEI Dobrescu. 2013.
Wearable system for heat stress monitoring in reghting applications. In Recent
Advances in Computer Science and Networking: Proceedings of the 2nd International
Conference on Information Technology and Computer Networks. 129–134.
Minoru Fujimoto, Fujita Naotaka, Tsutomu Terada, and Masahiko Tsukamoto.
2011. Lighting choreographer: an LED control system for dance performances. In
Proceedings of the 13th international conference on Ubiquitous computing. 613–614.
Francine Gemperle, Chris Kasabach, John Stivoric, Malcolm Bauer, and Richard
Martin. 1998. Design for wearability. In digest of papers. Second international
symposium on wearable computers (cat. No. 98EX215). IEEE, 116–122.
Nan-Wei Gong, Jürgen Steimle, Simon Olberding, Steve Hodges, Nicholas Ed-
ward Gillian, Yoshihiro Kawahara, and Joseph A Paradiso. 2014. PrintSense: a
versatile sensing technique to support multimodal exible surface interaction. In
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.
Daniel Groeger and Jürgen Steimle. 2019. LASEC: Instant Fabrication of Stretch-
able Circuits Using a Laser Cutter. In Proceedings of the 2019 CHI Conference on
Human Factors in Computing Systems. 1–14.
Tobias Grosse-Puppendahl, Christian Holz, Gabe Cohn, Raphael Wimmer, Oskar
Bechtold, Steve Hodges, Matthew S Reynolds, and Joshua R Smith. 2017. Finding
common ground: A survey of capacitive sensing in human-computer interaction.
In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems.
Nur Al-huda Hamdan, Simon Voelker, and Jan Borchers. 2018. Sketch&stitch:
Interactive embroidery for e-textiles. In Proceedings of the 2018 CHI Conference
on Human Factors in Computing Systems. 1–13.
Sunao Hashimoto, Ryohei Suzuki, YouichiKamiyama, Masahiko Inami, and Takeo
Igarashi. 2013. LightCloth: senseable illuminating optical ber cloth for creating
interactive surfaces. In Proceedings of the SIGCHI Conference on Human Factors in
Computing Systems. 603–606.
Liang He, Huaishu Peng, Joshua Land, Mark D Fuge, and Jon E Froehlich. 2017.
Designing 3D-printed deformation behaviors using spring-based structures: an
initial investigation. In Adjunct Publication of the 30th Annual ACM Symposium
on User Interface Software and Technology. 151–153.
Paul Holleis, Albrecht Schmidt, Susanna Paasovaara, Arto Puikkonen, and Jonna
Häkkilä. 2008. Evaluating capacitive touch input on clothes. In Proceedings of the
10th international conference on Human computer interaction with mobile devices
and services. 81–90.
Scott E Hudson. 2014. Printing teddy bears: a technique for 3D printing of soft
interactive objects. In Proceedings of the SIGCHI Conference on Human Factors in
Computing Systems. 459–468.
Kohei Ikeda, Naoya Koizumi, and Takeshi Naemura. 2017. FunCushion: fabricat-
ing functional cushion interfaces with uorescent-pattern displays. In Interna-
tional Conference on Advances in Computer Entertainment. Springer, 470–487.
Margot Jacobs and Linda Worbin. 2005. Reach: dynamic textile patterns for
communication and social expression. In CHI’05 Extended Abstracts on Human
Factors in Computing Systems. 1493–1496.
Walther Jensen, Ashley Colley, Jonna Häkkilä, Carlos Pinheiro, and Markus
Löchtefeld. 2019. TransPrint: A method for fabricating exible transparent free-
form displays. Advances in Human-Computer Interaction 2019 (2019).
Lee Jones, Sara Nabil, Amanda McLeod, and Audrey Girouard. 2020. Wearable
Bits: scaolding creativity with a prototyping toolkit for wearable e-textiles. In
Proceedings of the Fourteenth International Conference on Tangible, Embedded, and
Embodied Interaction. 165–177.
Viirj Kan, Katsuya Fujii, Judith Amores, Chang Long Zhu Jin, Pattie Maes, and
Hiroshi Ishii. 2015. Social textiles: Social aordances and icebreaking interactions
through wearable social messaging. In Proceedings of the Ninth International
Conference on Tangible, Embedded, and Embodied Interaction. 619–624.
Hsin-Liu Kao, Christian Holz, Asta Roseway, Andres Calvo, and Chris Schmandt.
2016. DuoSkin: rapidly prototyping on-skin user interfaces using skin-friendly
materials. In Proceedings of the 2016 ACM International Symposium on Wearable
Johan Kildal. 2012. Interacting with deformable user interfaces: eect of material
stiness and type of deformation gesture. In International Conference on Haptic
and Audio Interaction Design. Springer, 71–80.
Hyunyoung Kim, Aluna Everitt, Carlos Tejada, Mengyu Zhong, and Daniel Ash-
brook. 2021. MorpheesPlug: A Toolkit for Prototyping Shape-Changing Interfaces.
In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems.
Jaemin Kim, Jongsu Lee, Donghee Son, Moon Kee Choi, and Dae-Hyeong Kim.
2016. Deformable devices with integrated functional nanomaterials for wearable
electronics. Nano Convergence 3, 1 (2016), 1–13.
William Kylberg, Fernando Araujo De Castro, Peter Chabrecek, Uriel Sonderegger,
Bryan Tsu-Te Chu, Frank Nüesch, and Roland Hany. 2011. Woven electrodes for
exible organic photovoltaic cells. Advanced Materials 23, 8 (2011), 1015–1019.
Isabel Leber and Natividad Martínez Madrid. 2018. WearIT-a rapid prototyp-
ing platform for wearables. In International Conference on Bioinformatics and
Biomedical Engineering. Springer, 335–346.
Jeongwoo Lee, Ho-Chan Kim, Jae-Won Choi, and In Hwan Lee. 2017. A review
on 3D printed smart devices for 4D printing. International Journal of Precision
Engineering and Manufacturing-Green Technology 4, 3 (2017), 373–383.
Seulki Lee, Binhee Kim, and Hoi-Jun Yoo. 2009. Planar fashionable circuit board
technology and its applications. Journal of Semiconductor Technology and Science
9, 3 (2009), 174–180.
Jessica Lo and Audrey Girouard. 2014. Fabricating bendy: Design and develop-
ment of deformable prototypes. IEEE Pervasive Computing 13, 3 (2014), 40–46.
Joanne Lo, Doris Jung Lin Lee, Nathan Wong, David Bui, and Eric Paulos. 2016.
Skintillates: Designing and creating epidermal interactions. In Proceedings of the
2016 ACM Conference on Designing Interactive Systems. 853–864.
Kent Lyons, David Nguyen, Daniel Ashbrook, and Sean White. 2012. Facet:
a multi-segment wrist worn system. In Proceedings of the 25th annual ACM
symposium on User interface software and technology. 123–130.
Diana Marculescu, Radu Marculescu, Nicholas H Zamora, Phillip Stanley-Marbell,
Pradeep K Khosla, Sungmee Park, Sundaresan Jayaraman, Stefan Jung, Christl
Lauterbach, Werner Weber, et al
2003. Electronic textiles: A platform for perva-
sive computing. Proc. IEEE 91, 12 (2003), 1995–2018.
Eric Markvicka, Guanyun Wang, Yi-Chin Lee, Gierad Laput, Carmel Majidi, and
Lining Yao. 2019. ElectroDermis: Fully Untethered, Stretchable, and Highly-
Customizable Electronic Bandages. In Proceedings of the 2019 CHI Conference on
Human Factors in Computing Systems. 1–10.
James McCann, Lea Albaugh, Vidya Narayanan, April Grow, Wojciech Matusik,
Jennifer Manko, and Jessica Hodgins. 2016. A compiler for 3D machine knitting.
ACM Transactions on Graphics (TOG) 35, 4 (2016), 1–11.
David A Mellis, Leah Buechley, Mitchel Resnick, and Björn Hartmann. 2016.
Engaging amateurs in the design, fabrication, and assembly of electronic devices.
In Proceedings of the 2016 ACM Conference on Designing Interactive Systems. 1270–
C Mota. 2011. The rise of personal fabrication In: Proceedings of the 8th ACM
conference on Creativity and cognition.
Troy Nachtigall. 2017. EVA Moccasin: creating a research archetype to explore
shoe use. In Proceedings of the 2017 ACM International Symposium on Wearable
Troy Nachtigall, Daniel Tetteroo, and Panos Markopoulos. 2018. A ve-year
review of methods, purposes and domains of the international symposium on
wearable computing. In Proceedings of the 2018 ACM International Symposium on
Wearable Computers. 48–55.
Steven Nagels, Raf Ramakers, Kris Luyten, and Wim Deferme. 2018. Silicone
devices: A scalable DIY approach for fabricating self-contained multi-layered soft
circuits using microuidics. In Proceedings of the 2018 CHI Conference on Human
Factors in Computing Systems. 1–13.
Grace Ngai, Stephen CF Chan, Joey CY Cheung, and Winnie WY Lau. 2009.
The TeeBoard: an education-friendly construction platform for e-textiles and
wearable computing. In Proceedings of the SIGCHI Conference on Human Factors
in Computing Systems. 249–258.
Aditya Shekhar Nittala, Anusha Withana, Narjes Pourjafarian, and Jürgen Steimle.
2018. Multi-touch skin: A thin and exible multi-touch sensor for on-skin input.
In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems.
Simon Olberding, Kian Peen Yeo, Suranga Nanayakkara, and Jurgen Steimle. 2013.
AugmentedForearm: exploring the design space of a display-enhanced forearm.
In Proceedings of the 4th Augmented Human International Conference. 9–12.
Jifei Ou, Daniel Oran, Don Derek Haddad, Joseph Paradiso, and Hiroshi Ishii.
2019. SensorKnit: architecting textile sensors with machine knitting. 3D Printing
and Additive Manufacturing 6, 1 (2019), 1–11.
Antti Oulasvirta and Gregory D Abowd. 2016. User interface design in the 21st
century. Computer 49, 7 (2016), 11–13.
Roshan Lalintha Peiris, Mili John Tharakan, Adrian David Cheok, and Owen Noel
Newton. 2011. AmbiKraf: a ubiquitous non-emissive color changing fabric display.
In Proceedings of the 15th International Academic MindTrek Conference: Envisioning
Future Media Environments. 320–322.
Huaishu Peng, Jennifer Manko, Scott E Hudson, and James McCann. 2015. A
layered fabric 3D printer for soft interactive objects. In Proceedings of the 33rd
Annual ACM Conference on Human Factors in Computing Systems. 1789–1798.
Ivan Poupyrev, Nan-Wei Gong, Shiho Fukuhara, Mustafa Emre Karagozler,
Carsten Schwesig, and Karen E Robinson. 2016. Project Jacquard: interactive
digital textiles at scale. In Proceedings of the 2016 CHI Conference on Human Factors
in Computing Systems. 4216–4227.
 Proto-pasta.com. 2021. Electrically Conductive Composite PLA. Retrieved Feb
6, 2021 from https://www.proto-pasta.com/products/conductive-pla?variant=
Isabel PS Qamar, Rainer Groh, David Holman, and Anne Roudaut. 2018. HCI
meets material science: A literature review of morphing materials for the design
of shape-changing interfaces. In Proceedings of the 2018 CHI Conference on Human
Factors in Computing Systems. 1–23.
Raf Ramakers, Johannes Schöning, and Kris Luyten. 2014. Paddle: highly de-
formable mobile devices with physical controls. In Proceedings of the SIGCHI
Conference on Human Factors in Computing Systems. 2569–2578.
Gregory B Raupp, Shawn M O’Rourke, Curt Moyer, Barry P O’Brien, Scott K
Ageno, Douglas E Loy, Edward J Bawolek, David R Allee, Sameer M Venugopal,
Jann Kaminski, et al
2007. Low-temperature amorphous-silicon backplane tech-
nology development for exible displays in a manufacturing pilot-line environ-
ment. Journal of the Society for Information Display 15, 7 (2007), 445–454.
Michael L Rivera, Melissa Moukperian, Daniel Ashbrook, Jennifer Manko, and
Scott E Hudson. 2017. Stretching the bounds of 3D printing with embedded
textiles. In Proceedings of the 2017 CHI Conference on Human Factors in Computing
Anne Roudaut, Abhijit Karnik, Markus Löchtefeld, and Sriram Subramanian.
2013. Morphees: toward high" shape resolution" in self-actuated exible mobile
devices. In Proceedings of the SIGCHI Conference on Human Factors in Computing
Valkyrie Savage, Ryan Schmidt, Tovi Grossman, George Fitzmaurice, and Björn
Hartmann. 2014. A series of tubes: adding interactivity to 3D prints using internal
pipes. In Proceedings of the 27th annual ACM symposium on User interface software
and technology. 3–12.
Martin Schmitz, Mohammadreza Khalilbeigi, Matthias Balwierz, Roman Lisser-
mann, Max Mühlhäuser, and Jürgen Steimle. 2015. Capricate: A fabrication
pipeline to design and 3D print capacitive touch sensors for interactive objects.
In Proceedings of the 28th Annual ACM Symposium on User Interface Software &
Martin Schmitz, Jürgen Steimle, Jochen Huber, Niloofar Dezfuli, and Max
Mühlhäuser. 2017. Flexibles: deformation-aware 3D-printed tangibles for capaci-
tive touchscreens. In Proceedings of the 2017 CHI Conference on Human Factors in
Computing Systems. 1001–1014.
Christian Schumacher, Steve Marschner, Markus Gross, and Bernhard
Thomaszewski. 2018. Mechanical characterization of structured sheet mate-
rials. ACM Transactions on Graphics (TOG) 37, 4 (2018), 1–15.
Carsten Schwesig, Ivan Poupyrev, and Eijiro Mori. 2004. Gummi: a bendable
computer. In Proceedings of the SIGCHI conference on Human factors in computing
Teddy Seyed, Xing-Dong Yang, and Daniel Vogel.2016. Doppio: A Recongurable
Dual-Face Smartwatch for Tangible Interaction. In Proceedings of the 2016 CHI
Conference on Human Factors in Computing Systems. 4675–4686.
Yuta Sugiura, Gota Kakehi, Anusha Withana, Calista Lee, Daisuke Sakamoto,
Maki Sugimoto, Masahiko Inami, and Takeo Igarashi. 2011. Detecting shape
deformation of soft objects using directional photoreectivity measurement. In
Proceedings of the 24th annual ACM symposium on User interface software and
Ruojia Sun, Ryosuke Onose, Margaret Dunne, Andrea Ling, Amanda Denham,
and Hsin-Liu Kao. 2020. Weaving a Second Skin: Exploring Opportunities for
Crafting On-Skin Interfaces Through Weaving. In Proceedings of the 2020 ACM
Designing Interactive Systems Conference. 365–377.
Joshua G Tanenbaum, Amanda M Williams, Audrey Desjardins,and Karen Tanen-
baum. 2013. Democratizing technology: pleasure, utility and expressiveness in
DIY and maker practice. In Proceedings of the SIGCHI Conference on Human Factors
in Computing Systems. 2603–2612.
LocoTime Body Tape. 2021. LocoTime - Double Sided Fashion Body Tape for
Clothing. Retrieved Feb 6, 2021 from https://www.amazon.co.uk/Fashion-
381e1433-bdb9- 4468-b5a7- c4405490f0e8&pd_rd_w=XY5Ox&pd_rd_
wg=bSBa4&pf_rd_p=da0677f5-a47b- 4543-8b54- 10be576b8f26&pf_rd_r=
Aneesh P Tarun, Byron Lahey, Audrey Girouard, Winslow Burleson, and Roel
Vertegaal. 2011. Snaplet: using body shape to inform function in mobile exible
display devices. In CHI’11 Extended Abstracts on Human Factors in Computing
Ryan L Truby, Michael Wehner, Abigail K Grosskopf, Daniel M Vogt, Se-
bastien GM Uzel, Robert J Wood, and Jennifer A Lewis. 2018. Soft somatosensitive
actuators via embedded 3D printing. Advanced Materials 30, 15 (2018), 1706383.
Kevin Vlack, Terukazu Mizota, Naoki Kawakami, Kazuto Kamiyama, Hiroyuki Ka-
jimoto, and Susumu Tachi. 2005. GelForce: a vision-based traction eld computer
interface. In CHI’05 extended abstracts on Human factors in computing systems.
Ulrich Von Zadow, Wolfgang Büschel, Ricardo Langner, and Raimund Dachselt.
2014. Sleed: Using a sleeve display to interact with touch-sensitive display walls.
In Proceedings of the Ninth ACM International Conference on Interactive Tabletops
and Surfaces. 129–138.
Martin Weigel, Tong Lu, Gilles Bailly, Antti Oulasvirta, Carmel Majidi, and Jürgen
Steimle. 2015. Iskin: exible, stretchable and visually customizable on-body touch
sensors for mobile computing. In Proceedings of the 33rd Annual ACM Conference
on Human Factors in Computing Systems. 2991–3000.
Martin Weigel, Aditya Shekhar Nittala, Alex Olwal, and Jürgen Steimle. 2017.
Skinmarks: Enabling interactions on body landmarks using conformal skin elec-
tronics. In Proceedings of the 2017 CHI Conference on Human Factors in Computing
Michael Wessely, Theophanis Tsandilas, and Wendy E Mackay. 2016. Stretchis:
Fabricating highly stretchable user interfaces. In Proceedings of the 29th Annual
Symposium on User Interface Software and Technology. 697–704.
Kaufui V Wong and Aldo Hernandez. 2012. A review of additive manufacturing.
International scholarly research notices 2012 (2012).
Te-Yen Wu, Shutong Qi, Junchi Chen, MuJie Shang, Jun Gong, Teddy Seyed, and
Xing-Dong Yang. 2020. Fabriccio: Touchless Gestural Input on Interactive Fabrics.
In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems.