3D hand pose estimation using a wrist-worn camera

Date:
October 21, 2020
Source:
Tokyo Institute of Technology
Summary:
Researchers have developed a wrist-worn device for 3D hand pose
estimation. The system consists of a camera that captures images of
the back of the hand, and is supported by a neural network called
DorsalNet which can accurately recognize dynamic gestures.



FULL STORY ========================================================================== Researchers at Tokyo Institute of Technology (Tokyo Tech) working
in collaboration with colleagues at Carnegie Mellon University, the
University of St Andrews and the University of New South Wales have
developed a wrist-worn device for 3D hand pose estimation. The system
consists of a camera that captures images of the back of the hand, and
is supported by a neural network called DorsalNet which can accurately recognize dynamic gestures.


========================================================================== Being able to track hand gestures is of crucial importance in advancing augmented reality (AR) and virtual reality (VR) devices that are already beginning to be much in demand in the medical, sports and entertainment sectors. To date, these devices have involved using bulky data gloves
which tend to hinder natural movement or controllers with a limited
range of sensing.

Now, a research team led by Hideki Koike at Tokyo Tech has devised a
camera- based wrist-worn 3D hand pose recognition system which could in
future be on par with a smartwatch. Their system can importantly allow
capture of hand motions in mobile settings.

"This work is the first vision-based real-time 3D hand pose estimator
using visual features from the dorsal hand region," the researchers
say. The system consists of a camera supported by a neural network
named DorsalNet which can accurately estimate 3D hand poses by detecting changes in the back of the hand.

The researchers confirmed that their system outperforms previous work
with an average of 20% higher accuracy in recognizing dynamic gestures,
and achieves a 75% accuracy of detecting eleven different grasp types.

The work could advance the development of controllers that support
bare-hand interaction. In preliminary tests, the researchers demonstrated
that it would be possible to use their system for smart devices control,
for example, changing the time on a smartwatch simply by changing
finger angle. They also showed it would be possible to use the system
as a virtual mouse or keyboard, for example by rotating the wrist to
control the position of the pointer and using a simple 8-key keyboard
for typing input.

They point out that further improvements to the system such as using a
camera with a higher frame rate to capture fast wrist movement and being
able to deal with more diverse lighting conditions will be needed for
real world use.


========================================================================== Story Source: Materials provided by Tokyo_Institute_of_Technology. Note: Content may be edited for style and length.


==========================================================================


Link to news story: https://www.sciencedaily.com/releases/2020/10/201021111546.htm

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