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New Tech Uses Hardware, Software
to Train Dogs More Efficiently

North Carolina State University researchers have developed and used a
customized suite of technologies that allows a computer to train a dog
autonomously, with the computer effectively responding to the dog based
on the dog’s body language.

“Our approach can be
used to train dogs
efficiently and effectively,”
says David Roberts, an
assistant professor of
computer science at NC
State and co-author of a
paper on the work. “We
use sensors in custom
dog harnesses to monitor
a dog’s posture, and the
computer reinforces the
correct behavior quickly
and with near-perfect
consistency.”

“Because the technology integrates fundamental
principles of animal learning into a computational
system, we are confident it can be applied to a wide
range of canine behaviors,” says Alper Bozkurt, an
assistant professor of electrical and computer
engineering and co-author of the paper. “For example,
it could be used to more quickly train service dogs.
Ultimately, we think the technology will be used in
conjunction with human-directed training.”

The dog harness fits comfortably onto the dog and is
equipped with a variety of technologies that can
monitor the dog’s posture and body language. Each
harness also incorporates a computer the size of a
deck of cards that transmits the sensor data wirelessly.

The researchers published a paper about the
harness’s potential applications in late 2014.

For the current
study, the
researchers
wrote an
algorithm that
triggered a
beeping sound
and the release
of dog treats
from a nearby
dispenser
whenever the
dog’s harness
sensors detected
that the dog went
from standing to sitting.

The researchers had to ensure that the reinforcement
was given shortly after the desired posture was
exhibited, and also ensure that rewards were only
given for the correct posture. This required a trade-off.
If the algorithm ran long enough to ensure the correct
posture with 100 percent certainty, the reinforcement
was given too late to be effective for training purposes.
But if the reinforcement was given immediately, there
was a high rate of rewarding the wrong posture.

To address this, the researchers worked with 16
volunteers and their dogs to optimize the algorithm,
finding the best possible combination of speed and
accuracy. The researchers then compared the
algorithm’s timing and accuracy to that of an expert
human trainer.

The algorithm was highly accurate, rewarding the
appropriate behavior 96 percent of the time. But the
human trainer was better – with a 100 percent
accuracy rate.

However, while the average response time was about
the same for both algorithm and trainer, there was a
lot of variation in the time of response from the trainer.
The algorithm was incredibly consistent.

“That variation matters, because consistency is
fundamentally important for all animal training,” Roberts
says.

“This study was a proof of concept, and demonstrates
that this approach works,” Bozkurt says. “Next steps
include teaching dogs to perform specific behaviors
on cue, and integrating computer-assisted training and
human-directed training for use in various service dog
applications.”

“In the long term, we’re interested in using this
approach to animal-computer interaction to allow dogs
to ‘use’ computers,” Roberts says. “For example,
allowing an explosive detection dog to safely and
clearly mark when it detects components of a bomb,
or allowing diabetic alert dogs to use their physical
posture and behaviors to call for help.”
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