Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mapping Surgeon's Hand/Finger Motion During Conventional Microsurgery to
  Enhance Intuitive Surgical Robot Teleoperation   Purpose: Recent developments in robotics and artificial intelligence (AI)
have led to significant advances in healthcare technologies enhancing
robot-assisted minimally invasive surgery (RAMIS) in some surgical specialties.
However, current human-robot interfaces lack intuitive teleoperation and cannot
mimic surgeon's hand/finger sensing and fine motion. These limitations make
tele-operated robotic surgery not suitable for micro-surgery and difficult to
learn for established surgeons. We report a pilot study showing an intuitive
way of recording and mapping surgeon's gross hand motion and the fine synergic
motion during cardiac micro-surgery as a way to enhance future intuitive
teleoperation. Methods: We set to develop a prototype system able to train a
Deep Neural Net-work (DNN) by mapping wrist, hand and surgical tool real-time
data acquisition(RTDA) inputs during mock-up heart micro-surgery procedures.
The trained network was used to estimate the tools poses from refined hand
joint angles. Results: Based on surgeon's feedback during mock micro-surgery,
the developed wearable system with light-weight sensors for motion tracking did
not interfere with the surgery and instrument handling. The wearable motion
tracking system used 15 finger-thumb-wrist joint angle sensors to generate
meaningful data-sets representing inputs of the DNN network with new hand joint
angles added as necessary based on comparing the estimated tool poses against
measured tool pose. The DNN architecture was optimized for the highest
estimation accuracy and the ability to determine the tool pose with the least
mean squared error. This novel approach showed that the surgical instrument's
pose, an essential requirement for teleoperation, can be accurately estimated
from recorded surgeon's hand/finger movements with a mean squared error (MSE)
less than 0.3%

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The categories of this paper are: patt-sol
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The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: math-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: cond-mat
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The categories of this paper are: q-bio
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The categories of this paper are: econ
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: supr-con
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an