New software may help neurology patients capture clinical data with
their own smartphones
Study finds that software can accurately detect human movements performed during motor function assessments

Date:
January 24, 2022
Source:
Johns Hopkins Medicine
Summary:
New pose estimation software has the potential to help neurologists
and their patients capture important clinical data using simple
tools such as smartphones and tablets, according to a new study.



FULL STORY ==========================================================================
New pose estimation software has the potential to help neurologists and
their patients capture important clinical data using simple tools such as smartphones and tablets, according to a study by Johns Hopkins Medicine,
the Kennedy Krieger Institute and the University of Maryland. Human
pose estimation is a form of artificial intelligence that automatically
detects and labels specific landmarks on the human body, such as elbows
and fingers, from simple images or videos.


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To measure the speed, rhythm and range of a patient's motor function, neurologists will often have the patient perform certain repetitive
movements, such as tapping fingers or opening and closing hands. An
objective assessment of these tests provides the most accurate insight
into the severity of a patient's condition, thus better informing
treatment decisions. However, objective motion capture devices
are often expensive or only have the ability to measure one type of
movement. Therefore, most neurologists must make subjective assessments
of their patients' motor function, usually by simply watching patients
as they carry out different tasks.

The new Hopkins-led study sought to find whether pose estimation software developed by the research team could track human motion as accurately
as manual, frame-by-frame visual inspections of video recordings of
patients performing movements.

"Our goal was to develop a fast, inexpensive and easily accessible
method to objectively measure a patient's movements across multiple extremities," says study lead author Ryan Roemmich, Ph.D., an assistant professor in the Department of Physical Medicine and Rehabilitation at
the Johns Hopkins University School of Medicine and a human movement
scientist at the Kennedy Krieger Institute.

The research team had 10 healthy subjects between the ages of 24 and
33 record smartphone video of themselves performing five tasks often
assigned to neurology patients during motor function assessments:
finger taps, hand closures, toe taps, heel taps and hand rotations. The subjects performed each task at four different speeds. Their movements
were tracked using a freely available human pose estimation algorithm,
then fed into the team's software for evaluation.

The results showed that across all five tasks, the software accurately
detected more than 96% of the movements detected by the manual
inspection method. These results held up across several variables,
including location, type of smartphone used and method of recording: Some subjects placed their smartphone on a stable surface and hit "record,"
while others had a family member or friend hold the device.

With encouraging results from their sample of young, healthy people,
the research team's next step is to test the software on people who
require neurological care. Currently, the team is collecting a large
sample of videos of people with Parkinson's disease doing the same five
motor function tasks that the healthy subjects performed.

"We want anyone with a smartphone or tablet to be able to record
video that can be successfully analyzed by their physician,"
says Roemmich. "With further development of this pose estimation
software, motor assessments could eventually be performed
and analyzed without the patient having to leave their home." ========================================================================== Story Source: Materials provided by Johns_Hopkins_Medicine. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Hannah L. Cornman, Jan Stenum, Ryan T. Roemmich. Video-based
quantification of human movement frequency using pose estimation:
A pilot study. PLOS ONE, 2021; 16 (12): e0261450 DOI: 10.1371/
journal.pone.0261450 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/01/220124114939.htm

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