Hello Everyone, My name is TeYen Wu. I am a third year phd student From XDiscovery lab
at Dartmouth College.
Today, I am going to talk about Capacitivo, Contact-Based Object Recognition on Interactive Fabrics
using Capacitive Sensing
This work was done in collaboration with two undergraduate research interns, Lu and Yuji
as well as my advisors teddy and xing-dong
Before going into the detail of this work, I want to briefly show you what Capacitivo is
and what it can be used for
Capacitivo is a capacitive sensing technique that makes fabric aware of the objects it’s in contact with.
This includes things like food items, and dinnerware.
It can also detect the change of what is inside objects, like containers or cups.
For example, it can differentiate between milk, coke, apple cider, cold and hot water
in a regular glass cup
And as many everyday objects around us are made or covered by fabric, there are a numbers
of applications that are enabled by our sensing technique
Such as turning a coaster into a diet tracker with our smart textile.
or a smart tablecloth for recipe helper
Next step, add a slice of cheese
Although there are a number of techniques to enable interactive fabrics,
the one most related to Capacativo, is Tessuitivo by Jun Gong et al.
Tessutivo demonstrates an interactive fabric to recognize metallic objects based on inductance.
In contrast, our work focuses on nonmetallic objects that are also very common in daily life.
Our work complements existing research with a new sensing technique that is based on capacitance.
Next, I will explain how the sensing works for Capacitivo
When a object is placed on the textile sensor that is made of conductive electrodes,
the capacitance among the electrodes will change due to the permittivity of the object.
And different materials of the object will bring up different permittivity, resulting
in different capacitance.
Thus, Capacitivo recognize objects by measuring different changes in capacitance.
Next, I want to talk about our DIY approach for fabrication
First, we used an iron-on adhesive to bond a conductive fabric to a textile substrate.
Then, we used a desktop cutting machine to cut out the electrodes and row connections.
After cutting, we reheat it again and then peel the unnecessary pieces off.
so the pattern is clear.
Next, we need to connect the column electrodes
To connect the column electrodes, we use a sewing machine or hand stitching to connect
the conductive thread for each column.
Then, we attached a conductive fabric to the bottom as a ground plane
the sensor is completed
To validate the sensor’s accuracy and robustness, we ran a ten-participant user study.
We tested twenty everyday objects, including kiwi, avocados, grapefruit, credit card and so on
Each object was repeated for ten times in a randomized order.
We pre-trained a model three days before the study and we ran the real time recognition
in the study
In turn, our system can achieve an overall accuracy of 94.5%
This is an encouraging result, especially for the items which have a similar shape but
have different content or materials, such as kiwi and avocado
In addition, we also evaluated how well the system can recognize different types of liquids.
For this evaluation, we recruited a volunteer
and asked her to test 6 types of liquids,
including cold water, hot water, coke, apple cider, milk and beer
Each drink had 20 samples for training and 20 samples for testing
The result showed the overall accuracy of 90%.
The worst case, which was beer, if removed, results in a higher accuracy, of up to 96%
With that, I would like to conclude our work with three take-home messages.
We present a non-metallic object recognition technique on interactive fabrics using capacitive sensing
We measure the accuracy of our technique with several experiments
We demonstrate unique applications enabled by sensing technique
thank you for taking the time to listen. I am happy to take any questions now