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Machine-vision approach for automated measurement with ProAnalyst
Machine-vision approach for automated measurement with ProAnalyst
April 16, 2020
Automated Von Frey Testing with ProAnalyst
Based off of:
A machine-vision approach for automated pain measurement at millisecond timescales
Jessica Jones, William Foster, Colin Twomey; University of Pennsylvania
Machine-vision approach for automated measurement with ProAnalyst
Animals generate rapid motor
responses to somatosensory stimuli at
millisecond speeds that cannot be readily
detected by eye. Therefore, significantly
increasing the recording rate of the motor
actions, coupled with sub-second mapping of
behavioral signatures, will sharpen the
resolution and confidence for assessing an
animal’s internal pain state.
"We used ProAnalyst software to
automatically track hind paw movements
following stimulus application. This software
allowed us to integrate automated and
manually scored data, possible through the
‘interpolation’ feature within ProAnalyst. We
were able to define specific regions of interest
(paw), track, and generate data containing ‘x’
and ‘y’ coordinates of the paw through time,
as well as velocity, speed, and acceleration
information."
Recently, computational neuroethology platforms have introduced a suite of machine learning and deep neural networks to automatically track animal body parts during behavior for postural estimation .
The markerless automated tracking software ProAnalyst, tracks moving objects across high frame rate videography data. This approach relies on built-in machine learning algorithms for automated tracking, which provides an easier point of entry for researchers with limited time for software development or computing power.
Here, we present an automated
mouse pain scale that combines videography
at 2,000 fps, automated paw tracking with
ProAnalyst.