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\begin{document}

\title{A Quantitative Motor Assessment Using Markerless Motion Capture
Technology}



\author[1]{Paula Johnson}%
\author[2]{skcharles}%
\affil[1]{Brigham Young University}%
\affil[2]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\section*{Abstract}

{\label{127670}}\par\null

\section*{Introduction}

{\label{832378}}

There is a recognized need for sensitive, quantitative and
cost-effective clinical exams that assess neuromuscular health.
Correctly identifying movement impairments, and the extent and source of
the impairment, is critical for diagnosing movement disorders and
prescribing an appropriate course of action. Movement deficits caused by
traumatic brain injury (TBI), for example, are difficult to diagnose
because they are both common and complex and involve diffuse neural
networks. Movement deficits that involve diffuse neural networks are not
yet understood as well as those caused by focal lesions that are seen in
stroke victims. Consequently, the clinician generally recommends a
standard rehabilitation protocol that is not tailored to the problem or
the individual.

A variety of batteries to evaluate motor deficits already exist but are
lacking in one or more of these criteria. For example the extent of
tremor is most often determined using a rating scale (Goetz, 2008)
(Trouillas, 1997). Though rating scales have been validated and are
performed by highly experienced and trained clinicians, they are
nonetheless subjective. The scales are also course, from 0-4, with each
rating defined differently depending on the test.~ Robotic systems
(Debert, 2012) are quite sensitive and provide quantitative information,
however, they are cost prohibitive for most clinics and rehabilitation
centers. Optoelectronic systems, electromagnetic systems, inertial
measurement units, and electrogoniometers identity and quantify
movements well, but require painstaking placement of markers,
electrodes, or sensors in precise locations on the patient's body by
trained personnel. The setup time for such systems are impractical in
the clinical setting, especially when it compounds over repeated
assessments.

Recent advances in gaming and animation provide an opportunity for
sensitive, quantitative, and low-cost movement assessments. Systems such
as the X-box Kinect, Organic Motion, and Leap Motion employ markerless
motion capture that capture body movements, thus providing a practical
solution for recording movement during a motor assessment. Such
recordings result in a large quantity of data for in-depth movement
analyses.~~ Also impressive is that this technology is inexpensive and
commercially available.

We leveraged markerless motion capture technology to fulfill two
research aims: 1) Develop a quantitative motor assessment (QMA) that is
clinically relevant, user-friendly, low-cost, and highly sensitive, and
2) establish a normative database for each of the test measures to allow
comparisons relative to a healthy norm. The QMA involves an integrated
system, including a motion capture device and custom software. The
particular motion capture device we used (Leap Motion, San Francisco,
CA) is capable of tracking the position of a fingertip, palm of the hand
and tools such as pens or dowels. As the person being tested performs
each movement in the assessment, the system records their hand and
finger position with a resolution of 0.01mm and a sampling frequency of
100Hz. Other motion capture devices with such capabilities can equally
serve as a solution in our system.

The QMA automates conventional tests, which have served the medical
community relatively well in the absence of current technology. Each
test is described in detail in Kincaid et al. We seeded a normative
database for the QMA by administering these tests to 101 healthy control
subjects, and compared QMA tests results to those of conventional tests.

\section*{Methods}

{\label{806906}}

\subsection*{}

{\label{953512}}

\subsection*{Participants}

{\label{953512}}

One hundred one healthy subjects (49 females and 52 males) between the
ages 18-50 years old (mean=31.5, SD=9.94 years) participated in this
study. Participants were right-handed, free of any movement disorder or
medications that interfere with movement or alertness, and were not
pregnant. Only thirty (15 male and 15 female, mean age = \#\#, SD =
\#\#) also participated in the online reaction time test and the NIH
balance test. Thirty-one (\# male and \# female, mean age = \# SD =\#)
complete the cognitive motor task. All participants signed the informed
consent approved by the institutional review board for human research.

\par\null

\subsection*{Experimental Set-up}

{\label{724889}}

Subjects participated in 6 QMA tests and 5 conventional tests. A
complete list of measures from each test are shown in Table 1.~ As
described in Kincaid et al., the QMA battery consists of a finger
oscillation test, a simple reaction time test, an assessment of postural
tremor, a visually guided movement activity, and standing balance tests.
Also included is a cognitive-motor task. For comparison we included a
corresponding conventional test, for three of the six QMA exams:~
Halstead-Reitan Finger Tapping Test (FTT), an online simple reaction
time test ({[}1{]}), and the NIH Toolbox balance tests. We excluded
conventional tests corresponding to postural tremor and visually guided
movement, because rating scales for such measures yield zero for an
unimpaired population. We also included a conventional grip strength
test and the Beery Visuomotor Integration (VMI), which have no QMA
counterparts, but are tested for correlations. The tests were performed
in random order, except the balance tests, which were administered last
to avoid upsetting the sensor-screen calibration for the pointing tasks.
~Prior to each subject's arrival, the motion capture controller was
calibrated for the ambient lighting and its relative position to the
computer screen. Upper limb movements were performed on each side. The
entire assessment lasted no more than 2 hours. Administration of the QMA
and conventional tests were conducted according to established protocols
{[}Kincaid, et al.{]}{[}2{]}, {[}3{]}{[}1{]}; however a brief
description of each test and its administration is included here.~

For all but the balance tests, participants sat square at a table in
front of a computer screen. The motion capture sensor sat on the table,
face up, in front of the computer screen. Participants were introduced
to the motion capture controller by observing how their hand was tracked
in a visualizer window on the computer screen (Figure 1). As they
watched, they were instructed to keep their hand approximately 25 cm
directly above the controller as much as possible for optimal tracking
during each of the QMA tests (Figure 2A). Position in three dimensions
and velocity of the finger tips and palm were recorded by the Leap
Motion sensor at approximately 100 samples per second. During the
conventional tests that required sitting, participants moved to an
adjacent table. In the last test the sensor was mounted on a tripod to
record the movement of two wooden dowels which were attached to a helmet
worn by the participant (Figure 2B). During that time, an iPod used for
the NIH balance tests was attached to belt around the participant's
waist, so data for both balance tests were collected simultaneously.~\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.28\columnwidth]{figures/Visualizer/Visualizer}
\caption{{Leap Motion Visualizer shows user how their hand is tracked.
{\label{924554}}%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.28\columnwidth]{figures/QMA/QMA}
\caption{{Test setup. for most tests (A), subjects pointed to objects on a screen
while a Leap Motion sensor (C) captured their movements. In the balance
test (B), the sway of crown of the subjects' head was calculated from
the motion of dowels attached to a helmet.
{\label{268452}}%
}}
\end{center}
\end{figure}

\subsection*{Experiment Protocol}

{\label{181943}}

\subsubsection*{QMA Finger Oscillation
Test}

{\label{154124}}

The graphic user interface (GUI) on the screen for the finger
oscillation (FO) task (Figure 3A) contained two parallel lines, spaced
15mm apart, a black ball representing the user's finger, and a set of
crosshairs marking the starting point. While pointing at the GUI with
their index finger over the sensor, participants were instructed to
``tap'' in the air as fast as possible so that the black ball on the
screen moved below and then above the two parallel lines. They were to
move only at the metacarpal-phalangeal joint, keeping their wrist and
shoulder stationary. Each trial lasted 10s, with a 10-90s rest between
trials. The assessment was complete when the subject performed 5 trials
within 5 oscillations of each other. In the event that this requirement
was not met, the number of taps in ten trials were averaged. The
participant was given as many practice trials as desired. The test was
performed with each hand.

\par\null\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.28\columnwidth]{figures/Screen-Shot-2016-04-20-at-2-07-58-PM/Screen-Shot-2016-04-20-at-2-07-58-PM}
\caption{{QMA GUI for A) Finger Oscillation test; B) Visually Guided Movement; C)
Tremor Test; D) Reaction Time Test
{\label{220303}}%
}}
\end{center}
\end{figure}

~

\subsubsection*{Halstead-Reitan Finger Tapping Test
(HRFTT)}

{\label{712269}}

The HRFTT is administered using an instrument consisting of a board with
a mechanical counter attached. Participants start with the heel of their
hand resting on the board, their index finger on the lever of the
counter, and the remaining fingers extended and resting on the board.
The participant is instructed to tap the lever as quickly as possible
for 10s. The test is complete when five trials within five taps of each
other are performed. In the event that this is not accomplished, the
average of ten trials is used. The participant is given as many practice
taps as necessary and 10-90s rests were given between trials.

\selectlanguage{english}
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