Hello everyone.
My name is Denisa McDonald and I am presenting the collaborative work between Drexel University
and Worcester Institute of Technology:
Interaction with Touch-Sensitive Knitted Fabrics: User Perceptions and Everyday Use Experiments
Textile touch sensors can be constructed through digital weft-knitting using a single conductive
yarn and only two external connections.
These sensors are based on capacitive touch.
A capacitor is created between the carbon-coated yarn and human skin, since they are both conductors.
Different design forms can be created as illustrated in the three sensors in this figure.
This demo shows how approximate location of touch can be detected by these touch-sensitive
fabrics.
They are created by interlocking conductive yarn (carbon-coated nylon) with non-conductive
yarn (polyester, in this case).
The interactive areas are determined by the design, and other designs are also possible.
Illustrations include detection of both tap and swipe gestures with some accuracy.
The contributions of this work are:
Results from a qualitative study investigating user perceptions of this technology
Design guidelines for potential applications Investigating the feasibility of simple gesture
recognition Results from everyday use experiments, such
as sensor stretching and laundering
We conducted a formative study with the following objectives:
•To understand users’ views and experiences regarding interactions with this technology.
•To explore application areas into which users would want this technology integrated
•To identify potential concerns when engaging with it, for further study
•To generate design guidelines for future interactive applications based on touch-sensitive
fabrics
The study was structured as 8 focus groups with 2-5 participants each, for a total of
32 participants.
There are 3 design phases, which approximately match the objectives of the study.
In the first phase, users are introduced to the technology, its basic working principles
and only the touchpad designs in the bottom left figure are shown.
Participants are asked to discuss their thoughts.
In the next phase, users interact with several design prototypes and their usability is discussed,
especially compared to any existing technology with a similar functionality – these samples
are illustrated in the rest of the figures.
We can see several examples of familiar interfaces such as an alphanumeric keyboard, a volume
controller, a piano keyboard etc.
Finally, in the last phase, users are encouraged to share more ideas, concerns and suggestions.
The data is then analyzed using thematic analysis, as a result of which, the following themes
emerged.
The themes resulting from this study can be broadly categorized into:
General user perceptions, which were related to the soft fabric texture, the feelings of
comfort it elicited, its robustness, affordability and portability
Relevant application areas, including wearables, smart environments, education, art and entertainment,
medical and assistive technologies, as well as monitoring and activity recognition.
- Implicit in many of these mentioned applications was the need for this technology to support
accurate gesture recognition, which we investigate subsequently.
The third category is user hesitations and concerns, which focused on damage and safety
questions, washing and drying of the sensor, stretching, and its exposure to outside conditions.
We start to explore some of these everyday use concerns through experiments in the last
section of this work.
Gesture input is investigated on a volume controller-shaped knitted touch-sensitive
fabric, to see if the signals are different from each other for some simple gestures such
as:
Half swipe, full swipe, double swipe, and in addition, random contact samples, emulating
accidental touch events – which are common in everyday life.
The swipe gestures are illustrated in the figures above, and the accidental touch events
in the short clip.
The plots in the top figure illustrate gesture signals over time.
The data was collected from one participant performing the four different gesture types
20 times each, and is meant to provide an intuitive understanding of the trajectory
of the signal, expressed as two frequency gain values over time.
We can see that samples of the same gesture type follow a very similar path, more clearly
shown in the top plots.
These plots also illustrate how different gestures follow distinct trajectories from
each-other.
Even random contact samples, emulating accidental touch events, seem to be more similar to each-other
than to any of the swipe gestures.
The generated heatmap is a more comprehensive analysis of all gesture samples from 12 users.
It indicates gesture distances, computed using the sum of distances of all gesture sample
pairwise comparisons.
We can see that gesture samples of the same type are more similar to each-other than samples
from different gesture types, and that is true also for random gestures, which seem
to be more similar to each other than to any other category, showing promise for real-world
use of this technology.
Many applications of these sensors are wearable, which means that the fabric component of each
sensor would need to be washed, for appropriate use in real-world settings.
This experiment demonstrates the change in the resistance of the fabric after 5 cycles
of washing and drying with three different sensors.
These results show that the percent change in resistance compared to baseline in all
the trials, does not exceed 7%.
Stretchability is another inherent property of the knitted fabric.
In this experiment, the change in resistance due to stretching the sensor is considered,
for two cases: vertical and horizontal stretching of the sensor.
Then, those results are compared with a baseline state.
Three different sensors were tested for this experiment and results vary with the structure
of the sensor, starting form a 2% change with bigger sensors built more sturdily, with more
knitted layers, to 27% for stretching thinner and smaller sensors, which is to be expected
since their structure changes more.
This variability, however, needs to be considered when designing applications for each specific
knitted pattern, and should be included in the computational models for accurate, real-world
functionality.
Through our user studies and experiments, we have moved closer to having knitted smart
sensors ubiquitously integrated into everyday environments, similarly to regular fabric.
Thank you for listening, and please refer to our paper for more in-depth explorations
of these topics.