We present eGlove, a wearable and low-cost fabric sensor for recognizing a rich context
of objects by touching them, including daily necessities, fruits, plants, as well as different
body parts.
Compared with previous research, we focused on contextual interactions on wearable interactive
fabrics. Our work complements existing research by recognizing different objects and body
parts even when the fabric is under varying degrees of deformation and stretching.
Alternating current can pass through dry human skin or some high resistance objects such
as fruits and plants. When the frequency of the applied AC signal changes, the electrical
current flows through different paths.
This is because the electrical impedance of tissues or substances is frequency-dependent.
and the AC signal will always flow through the path of least impedance inside the organic
objects.
Once we touch the apple, the receiving signal changes, therefore, we can distinguish between
different objects and human body parts by comparing AC signal changes over a wide range
of frequencies.
Here we can see how SFCS works in our system. we used Teensy to generate evenly stepped
square wave sweeps and the signal transformation module to transform the square wave signal
into a sine wave signal.
Once we hold the apple, the returned signal will undergo a characteristic change, and
we can distinguish the signal through SVM, thus knowing that an apple is touched.
We tested the conductive fabric's properties through four aspects: size, shape, deformability,
and stretchability. We expect to maximize the sensitivity of the signal through fabric
sensor designing.
The experimental results show that the other three have little effect on the test results
in a certain range except for the area, which means measurement results would not change
even if the electrode has been deformed or stretched.
And the size test demonstrates that the smaller electrode, which has a lower impedance, allows
the impedance to be measured in a wider range. Therefore we should make the electrode area
as small as possible while ensuring the target is in full contact.
Based on the four properties test of the conductive fabric. We designed eGolve into a seven-layer
structure, including two parts: 1) an outer side of the glove for gathering the signal,
and 2) the inner side acting as the shielding layer for shielding the fabric sensor from
the noise of the human body.
First, we adhered to the conductive fabric at the glove's middle finger. Then, we manually
sewed the conductive thread to the conductive fabric for connecting with the wires and sensing
board. The whole fabrication processes were low-cost and easily manipulated within a lab
environment.
The fabric electrode can fit the finger well, and it has little effect on hand movement.
Our technique can make daily necessities touch or grasp sensitive without instrumenting them
Since the AC signal could pass through clothes, our approach could work even
when the participant is wearing clothes and be used to identify the different body parts
that are in contact with the electrode with an accuracy of 93.2%
Aside from recognizing inanimate objects, our approach can also classify fruits and plants