sensors
ArticleArtificial Intelligence Distinguishes Pathological Gait:
The Analysis of Markerless Motion Capture Gait Data
Acquired by an iOS Application (TDPT-GT)
Chifumi Iseki 1,2, * , Tatsuya Hayasaka 3 , Hyota Yanagawa 4 , Yuta Komoriya 3 , Toshiyuki Kondo 1 ,Masayuki Hoshi 5 , Tadanori Fukami 6 , Yoshiyuki Kobayashi 7 , Shigeo Ueda 8 , Kaneyuki Kawamae 9 ,Masatsune Ishikawa 10,11 , Shigeki Yamada 11,12,13 , Yukihiko Aoyagi 14 and Yasuyuki Ohta 1
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Citation: Iseki, C.; Hayasaka, T.;

10
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Yanagawa, H.; Komoriya, Y.; Kondo,T.; Hoshi, M.; Fukami, T.; Kobayashi,

12Y.; Ueda, S.; Kawamae, K.; et al.
Artificial Intelligence Distinguishes

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Pathological Gait: The Analysis ofMarkerless Motion Capture Gait Data

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Acquired by an iOS Application*

Division of Neurology and Clinical Neuroscience, Department of Internal Medicine III, Yamagata University
School of Medicine, Yamagata 990-2331, Japan; toshikon@med.id.yamagata-u.ac.jp (T.K.);
yasuyuki@med.id.yamagata-u.ac.jp (Y.O.)
Department of Behavioral Neurology and Cognitive Neuroscience, Tohoku University Graduate School of
Medicine, Sendai 980-8575, Japan
Department of Anesthesiology, Yamagata University School of Medicine, Yamagata 990-2331, Japan;
hayasakatatsuya1101@gmail.com (T.H.); aequanimitas.3520@gmail.com (Y.K.)
Department of Medicine, Yamagata University School of Medicine, Yamagata 990-2331, Japan;
m181274@st.yamagata-u.ac.jp
Department of Physical Therapy, School of Health Sciences, Fukushima Medical University, 10-8 Sakaemachi,
Fukushima 960-8031, Japan; mhoshi@fmu.ac.jp
Department of Informatics, Faculty of Engineering, Yamagata University, Yonezawa 992-8510, Japan;
fukami@yz.yamagata-u.ac.jp
Human Augmentation Research Center, National Institute of Advanced Industrial Science andTechnology (AIST), Kashiwa II Campus, University of Tokyo, Kashiwa 277-0882, Japan;
kobayashi-yoshiyuki@aist.go.jp
Shin-Aikai Spine Center, Katano Hospital, Katano 576-0043, Japan; uedashigeo@yahoo.co.jp
Department of Anesthesia and Critical Care Medicine, Ohta-Nishinouti Hospital, Koriyama 963-8558, Japan;
kkawamae@ohta-hp.or.jp
Rakuwa Villa Ilios, Rakuwakai Healthcare System, Kyoto 607-8062, Japan; rakuwadr1001@rakuwadr.com
Normal Pressure Hydrocephalus Center, Rakuwakai Otowa Hospital, Kyoto 607-8062, Japan;
shigekiyamada393@gmail.com
Department of Neurosurgery, Nagoya City University Graduate School of Medical Science,
Nagoya 467-8601, Japan
Interfaculty Initiative in Information Studies, Institute of Industrial Science, The University of Tokyo,
Tokyo 113-8654, Japan
Digital Standard Co., Ltd., Osaka 536-0013, Japan; y.aoyagi@digital-standard.com
Correspondence: chi.iseki@gmail.com; Tel.: +81-22-717-7358(TDPT-GT). Sensors 2023, 23, 6217.
https://doi.org/10.3390/s23136217
Academic Editor: Giovanni Saggio
Received: 19 May 2023
Revised: 22 June 2023
Accepted: 4 July 2023
Published: 7 July 2023

Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andAbstract: Distinguishing pathological gait is challenging in neurology because of the difficulty ofcapturing total body movement and its analysis. We aimed to obtain a convenient recording withan iPhone and establish an algorithm based on deep learning. From May 2021 to November 2022
at Yamagata University Hospital, Shiga University, and Takahata Town, patients with idiopathic
normal pressure hydrocephalus (n = 48), Parkinson’s disease (n = 21), and other neuromusculardiseases (n = 45) comprised the pathological gait group (n = 114), and the control group consisted of
160 healthy volunteers. iPhone application TDPT-GT captured the subjects walking in a circular pathof about 1 meter in diameter, a markerless motion capture system, with an iPhone camera, which
generated the three-axis 30 frames per second (fps) relative coordinates of 27 body points. A light
gradient boosting machine (Light GBM) with stratified k-fold cross-validation (k = 5) was applied
for gait collection for about 1 min per person. The median ability model tested 200 frames of each
person’s data for its distinction capability, which resulted in the area under a curve of 0.719. The
pathological gait captured by the iPhone could be distinguished by artificial intelligence.

conditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Sensors 2023, 23, 6217. https://doi.org/10.3390/s23136217

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Keywords: artificial intelligence; motion tracking; markerless motion capture; quantitative gait
assessment; smartphone device; neuromuscular diseases

1. Introduction
In clinical settings, pathological gait must be distinguished for recognizing symptoms,
diagnosis/differential diagnosis, and monitoring disease progression and the effects oftreatment and rehabilitation. Gait evaluation is complicated because it includes manyfactors such as speed, power, position, angle, and cycle on each body part, expressing
posture, arm and leg swings, and steps. Furthermore, pathological gait is presented inmany combinations: asymmetry, unbalance, defragmentation, and reduced speed, length,
amplitude, and smoothness. Recognizing, diagnosing, and even describing all these
symptomatic disturbances are always challenging.
To evaluate gait, various quantified methods have been employed: pedobarography,
motion capture, floor sensors, and wearable sensors. Motion capture is useful for understanding whole-body movements, while others usually concentrate on parts of the body.
Optical motion capture systems require the attachment of several markers to the body,usually with multiple cameras [1–8]. Marker-based motion capture for gait requires theattachment of markers from head to toe during preparation and when recording for along time. Older people and patients, sometimes including individuals with cognitive
impairment, may not tolerate that. Therefore, most marker-based studies have focused onhealthy volunteers or athletes.
Active markerless motion capture systems emit light [9–12] or micro-doppler [13] tocollect the structures of objects, which requires a prepared place and settings like a laboratory. Passive systems rely on only the images captured by cameras and take a relatively
long time for analysis to generate the gait data after the recording [14,15]. By attaching
accelerometers and gyroscopes, called wearable devices, gait has been analyzed [7,16–18].
Some wearable devices are small and easy to wear; however, the information on gait islimited to speed and acceleration. Multimodal sensors and analysis systems with machinelearning have been developed [10]; however, to date, the systems are not made for theconvenience of participants but for obtaining precise information on gait.
Along with the history of gait analysis, especially for use in patients and older individuals, clinicians need non-invasive markerless motion capture systems that are possible
to use in the general room for outpatients and that take participants as short a time as
possible. In this study, we used a passive markerless motion picture system, TDPT-GT(Three-Dimensional Pose Tracker for Gait Test), which was novel in the circumstance inthat the gait recording was just like taking videos on an iPhone [19,20]. In addition to theconvenience and comfort for patients and older people, the system could immediately
generate and preserve three-dimensional coordinate data of 27 body points.
Gait recognition techniques based on machine learning have been evolving [7], as wellas our motion capture system [19]. Machine learning techniques have made it possible tocollect complicated movements of human gait quickly. Then, the next challenge was how
to analyze the large amount of gait data generated by these technologies. Machine learning
has also been applied to the analysis of the gait data of neurological disorders [21–23].
Here, in this study, having acquired gait data in 30 frames per second (fps) for 27 body
points with three axes produced by this iPhone application, we aimed to make artificial
intelligence distinguish the motions of a pathological gait from those of a healthy gait.
2. Materials and Methods
2.1. Subjects
We collected information on age, disease history, and gait data from May 2021 toNovember 2022 at Yamagata University Hospital, Shiga University, and Takahata Town.
The pathological gait group (n = 114) was composed of patients with idiopathic normal

2. Materials and Methods
2.1. Subjects

Sensors 2023, 23, 6217

We collected information on age, disease history, and gait data from May 2021
to November 2022 at Yamagata University Hospital, Shiga University, and Takahata
Town. The pathological gait group (n = 114) was composed of patients with idiopathic
3 of 13normal pressure hydrocephalus (iNPH, n = 48), Parkinson’s disease (PD, n = 21), andother neuro-muscular diseases (n = 55), which were mainly neurodegenerative diseases. The control group consisted of 160 individuals who were healthy volunteers atpressure hydrocephalus (iNPH, n = 48), Parkinson’s disease (PD, n = 21), and other neurolocal health check-ups or family members of patients who did not have a neurodegenmuscular diseases (n = 55), which were mainly neurodegenerative diseases. The control
erative disease. The inclusion criterion for both groups was the capability of walking
group consisted of 160 individuals who were healthy volunteers at local health check-ups orindependently
safely forseveralminutes;using a single-point
cane The
wasinclusion
the onlyfamily membersandof patients
who
did not
have a neurodegenerative
disease.
assistanceallowed.
criterion for both groups was the capability of walking independently and safely for several
minutes; using a single-point cane was the only assistance allowed.
2.2. Gait Data
2.2. Gait
TheDatagait of each participant was recorded as they walked in a circular path of around1 m indiameter.
The participant
recording wasfrom asa distanceof approximately
3 m awayfrom
The gait of each
wasdonerecorded
they walked
in a circular pathof aroundtrail,
ensuring
fit withinwas
the doneframefromof theapplication.
Each participant
completed1them indiameter.
Theit recording
a distanceof approximately
3 m awayfromtwotrail,laps,ensuringmoving bothclockwise
counterclockwise,
as shown
in the accompanying
the
it fit within
the andframeof the application.
Each participant
completedpicture(Figure1). both
Theyclockwise
were asked
to counterclockwise,
walk at their comfortablespeed
clockwise
for twotwolaps,
movingandas shown
in theaccompanying
laps andcounterclockwise
twotolaps.
Theapplication
TDPT-GTversions
picture(Figure1). They werefor
asked
walk
at their
comfortablespeed(Theclockwise
forweretwo
20210525,
20211111,
20220314,
20220701,
and
20220902)(Three-DimensionalPoselaps and counterclockwise for two laps. The application TDPT-GT (The versions were
Tracker for
Gait Test)
is a markerless
motioncapture system
based on machine
20210525,
20211111,
20220314,
20220701,
and 20220902)(Three-Dimensional
Poselearning.
Trackerfor
Gait
is a markerlessimages
motiontaken
capturesystembased
on machine
theFrom
theTest)two-dimensional
by an
iPhonecamera,3D heatlearning.
maps of Fromthe body
two-dimensional
images As
taken
by anpreviously
iPhone camera,
3Dthe
heatTDPT-GT
maps of the
body points
were[19,20],
generated
the threepoints were estimated.
shown
estimated.
As shown
previously[19,20], coordinates
the TDPT-GT
the three-axis
30 frames
axis 30 frames
per second(fps) relative
ofgenerated
24 body points:
the nose,navel,
per(fps)
relative
coordinates
of 24
body
points: thenose, navel,
andbilateralmiddle
pointsandsecondbilateralpointssuch
as the eyes,
ears,
shoulders,elbows,wrists,thumbs,such
as theeyes,
ears,heels,shoulders,wrists,thumbs,middle fingers,
hip,body
knees,points:
heels,
fingers,hip,knees,and elbows,
toes andcalculated
coordinatesof threeandtoes andcalculated
coordinatesof threeThese
bodycoordinates
points: the centerof the head,the centerof thehead, neck,and buttocks.
were processedby aneck,
lowandThesea low-passfilter,named the1 europassbuttocks.
filter, named
thecoordinates
1 euro filterwere[24], processed
setting the byminimum
cut-oﬀ
frequencyof 1.2,thefilter[24],
setting
the minimum
cut-offof
slope
of 0.001,
and thecut-oﬀ
slope
of 0.001,
and the cut-off
cut-oﬀ frequency
frequency ofof1.2,
thethederivate
1, andpreserved
ascut-off
frequencyofthederivateof1,andpreserved
asCSV
files.
We
defined
the
navel
as
CSV files. We defined the navel as the reference point of the depth to each point. The
thepoint of the were
depthused
to eachpoint. The
X and
Y coordinates
were usedtorawreference
X and Y coordinates
to represent
therawverticalandhorizontal points,
respecrepresent
theverticalandhorizontal
points,respectively.
tively.

Figure 1.1. The
Themarkerlessmarkerlessmotion
motioncapturewasrecorded
while
the participants
walkedin a aboutcircleFigurecapturewasrecorded
while
the participants
walkedin a circle
about
1
m
in
diameter
and
about
3
m
away
from
the
gait
trail
so
that
their
whole
body
fit
within1 m in diameter and about 3 m away from the gait trail so that their whole body fit within the frame.
the frame.

2.3. Deep Learning for the Distinction of Gait
About two hundred frames were extracted from the CSV files of each participant inan early and stable state of the record. All coordinates were labeled either disease gait orcontrol gait. The total data were divided into 60 (n = 71–73 for disease and n = 102–104 forcontrol) for training and 20 (n = 18–20 for pathological gaits and n = 24–26 for controls) for
validation without dividing the data of the same participant into both datasets. (Table 1).

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Table 1. The number of participants in the datasets divided by the training, validation, and test.
Training
Control Disease
Dataset 1
Dataset 2
Dataset 3
Dataset 4
Dataset 5

103
102
103
104
104

72
73
72
71
72

Validation
Control Disease
25
26
25
24
24

19
18
19
20
19

Test
Total
Control Disease Control Disease
32
32
32
32
32

23
23
23
23
23

160
160
160
160
160

114
114
114
114
114

For deep learning, a light gradient boosting machine (Light GBM) [25] was employed,
which was a technique involved combining a gradient boosting decision tree with gradientbased one-side sampling and exclusive feature bundling. The learning aimed to predictwhether the gait was pathological in each frame containing the parameters of 27 threedimensional body points. The hyperparameters were tuned by Optuna [26]; the method
automatically selected the features and parameters that were determined to perform best.
The feature importance scores of the coefficients from linear regression were calculated to
identify the key points for predicting pathological gait. Stratified k-fold cross-validation(k = 5) was used to evaluate the differentiation ability of the models of machine learning
by generating the classification accuracy, sensitivity, specificity, and area under the curve(AUC) calculated from the receiver operating characteristic (ROC) curve.
2.4. Test
To test the ability of machine learning, we used the newest (data acquisition order)
20 data out of the data of all patients and controls. Among the five models, the modelwith the median of the AUCs was employed for the final test to evaluate the model’s
generalization. The final test was evaluated as follows to evaluate the gait of a whole
person. The AUC was calculated from the ROC curve drawn based on the averaged
accuracy for 200 frames for each person, and the sensitivity and specificity were also
calculated from the curve. To optimize the hyperparameters, Optuna v.3.0 was used. The
feature importance scores of the coefficients from the linear regression were calculated to
identify the key points for predicting pathological gait.
2.5. Hardware, Software, and Statistics
Machine learning was carried out by Anaconda 3 on Ubuntu 20.04 LTS. The analysis
hardware comprised a Core i9 10940X 14core CPU (Santa Clara, CA, USA) and an NVIDIA
RTX A4000 16GB GPU (Santa Clara, CA, USA). EZR version 1.41 and Python 3.8 were used
for the statistical analysis. The results were expressed as the mean ± standard deviationand numbers (percentages). The constructed model was considered to have sufficient
diagnostic capability when AUC > 0.700, along with a 95% confidence interval (CI).
2.6. Ethical Considerations
The study was conducted according to the Declaration of Helsinki and approved by
the Ethics Committee for Human Research of Shiga University of Medical Science (protocol
code: R2019-337; date of approval: 17 April 2020) and the Ethical Committee of Yamagata
University School of Medicine (protocol code: 2020-10; date of approval: 12 April 2021).
3. Results
3.1. Clinical Characteristics
The average age and standard deviation were 74.5 ± 7.8 years in the pathologicalgait group and 72.9 ± 11.1 years for the controls, which were not significantly different
between the groups. Sex proportions were also not significantly different between thegroups (Table 2).

Sensors 2023, 23, 6217

5 of 133.2. Results of the Five Learning Models
Among the five models made by AI using stratified K-fold cross-validation (k = 5),
dataset 1 presented the median of the AUCs: an AUC of 0.882 (the 95% confidence interval
Sensors 2023, 23, x FOR PEER REVIEW
6 ofof14
ranged from 0.875 to 0.890), a sensitivity of 0.740, a specificity of 0.898, and an accuracy
0.8327 (Table 3, Figure 2).

Figure2.2.The
The
receiveroperatingcharacteristic(ROC)
curvesandareatheunderarea theunderthe(AUC)
curve
Figurereceiveroperatingcharacteristic(ROC)
curvesand thecurve(AUC)
obtained
by
the
five
machinelearning
models.
obtained by the five machine learning models.

Each learning model’s cut-oﬀ value (specificity and sensitivity) is shown near the
curve.
3.2.1. Discrimination of Pathological Gait
Machine learning model 1 made by dataset 1 was tested by the test data (Table 2).

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Table 2. The number, age, and sex proportion of the pathological gait group and the controls.
Pathological Gait n = 114

Controls n = 160

p74.5 ± 7.8
52/62

72.9 ± 11.1
91/69

0.141
0.076

Age (average ± SD *)Sex (male/female)* SD, standard deviation.

Table 3. Five datasets making up the results of the five models: the diagnosis of pathological gaitevaluated by the cut-off, the area under the curve (AUC), sensitivity, specificity, and accuracy.
Cut-off

AUC(95%CI)

Sensitivity

Specificity

Accuracy

Model 1

0.466

0.882
[0.875–0.890]

0.740

0.898

0.833

Model 2

0.415

0.932
[0.926–0.937]

0.852

0.878

0.868

Model 3

0.279

0.921
[0.915–0.926]

0.861

0.836

0.824

Model 4

0.273

0.820
[0.812–0.829]

0.798

0.723

0.716

Model 5

0.472

0.774
[0.764–0.784]

0.635

0.785

0.722

AUC: area under the receiver-operating characteristic curve; 95%CI: the parameter range within 95% confidence interval.

Each learning model’s cut-off value (specificity and sensitivity) is shown near the curve.
3.2.1. Discrimination of Pathological Gait
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Machine learning model 1 made by dataset 1 was tested by the test data
(Table 2). The
result of distinguishing pathological gait was a cut-off of 0.595, an AUC of 0.719 (the 95%
confidence interval was ranged from 0.576 to 0.862), a sensitivity of 0.652, a specificity of(the 95% confidence interval was ranged from 0.576 to 0.862), a sensitivity of 0.652, a
0.781,
and an accuracy of 0.709 (Figure 3).

specificity of 0.781, and an accuracy of 0.709 (Figure 3).

Figure 3.
model
1, theto distinguisheach individual’sgait, whether gait, whether
Figure3. Applying
Applying
model
1, testthewastestperformed
was performed
to distinguisheach individual’s
pathological or not, resulting in an AUC of 0.719. The cut-oﬀ value (specificity and sensitivity) is
pathologicalnot, resulting in an AUC of 0.719. The cut-off value (specificity and sensitivity) isshown near theorcurve.
shown near the curve.

3.2.2. Feature Importance
The feature importance score was highest in the y coordinate of the right hip
joint, followed by high scores in the depth of the buttock, the depth of the right knee,
and the x coordinate of the center of the head (Figure 4).

Sensors 2023, 23, 6217

Figure 3. Applying model 1, the test was performed to distinguish each individual’s gait, whether
7 of 13
pathological or not, resulting in an AUC of 0.719. The cut-oﬀ value (specificity and sensitivity) isshown near the curve.

3.2.2.
3.2.2. Feature
Feature ImportanceImportanceThe
featurewashighestin they coordinateof theofright
hip joint,
The featureimportanceimportancescore
score
was
highestin they coordinate
the right
hipfollowedby
highscores
in
the
depth
of
the
buttock,
the
depth
of
the
right
knee,
and
the x
joint, followed by high scores in the depth of the buttock, the depth of the right knee,
coordinateof the centertheheadof(Figure4). (Figure 4).
and the x coordinate
ofof
thecenterthe head

Figure4.
4. HighHigh featurefeatureimportanceimportancescoresscoresininthe
thediscrimination
pathological
gait.
Figurediscriminationof ofpathologicalgait.

4. Discussion
In this study, AI distinguished the pathological gait from the control gait with an AUC
of 0.719 for the ROC and an accuracy of 0.702. The markerless motion capture systemon the iPhone successfully recorded the gaits for analysis. The present analysis included
two valuable methods. First, the motion capture of gaits was obtained very easily withan iPhone camera and its application TDPT-GT. The participants did not need to wear
markers or devices when walking in a circle within the camera’s angle of view for justseveral minutes at a comfortable speed. Second, we succeeded in diagnosing pathologicalgait by constructing an AI analysis for the big and precise data from TDPT-GT, with 30 fps
and 27 body points in 3 dimensions.
4.1. Usage of the Present AI Model
The present AI model has been trained to effectively differentiate pathological gait,a task that was challenging even for experts. Clinicians do not solely rely on visualobservation of a patient’s gait to determine if it is pathological. In the early stages of
disease, patients typically do not exhibit apparent gait disturbances when they come fora visit. Therefore, we collect many clinical records, such as backgrounds, history, labodata, systematic neurological examinations, physiological examinations, and neuroimaging.
Subsequently, other diseases are also differentiated and/or judged as complications. Gait
provides just a piece of information. After this process, we diagnose the disease andcomprehensively ensure that the person’s gait is pathological. AI distinguished the gaitwithout these long, clinical, and complicated procedures. Consequently, AI’s incorporation
into gait screening during health check-ups in communities of older individuals can lead toearlier diagnoses.
Our ultimate objective in the future is not only to achieve the early diagnosis of pathological gait but to also promote overall well-being. The ability to ambulate independently
is a major contributor to overall well-being and autonomy in older individuals, and gait
and its decline are crucial for the health and function of older patients [27]. Elderly patientsregularly present with complex gait disorders, with concurrent contributions from multiple

Sensors 2023, 23, 6217

8 of 13causal factors [27], such as muscle weakness, cognitive impairment, alcohol consumption,
pain, and physical inactivity [28]. Among older individuals participating in a study, gait
abnormalities were observed in 48.4% [29]. The combination of multiple modes of gait
abnormalities including the self-reported gait difficulty predicted the risk of various geriatric outcomes, e.g., falls. [29]. Based on their study, we derived the notion that objectively
detecting gait abnormalities should be carried out when individuals subjectively perceivedifficulties in their gait. The ability of the present AI model to distinguish common pathological gait patterns is applicable for analyzing complex gait disorders arising from diverse
causes. Our system aims to aid in identifying subjective gait disorders in order to prevent
their progression.
Gait encompasses more than just the physical manifestation of movement. Jason
reviewed and noted that the control of gait occurs via multiple cognitive domains [27].
Executive functions are a general system working with attention, memory, reasoning, andcognitive integration, which are associated with gait parameters, such as velocity andstep length [30–32]. Gait velocity is related to cognitive processing speed [30], short-term
memory [30], and multiple cognitive domains [33]. By capturing gait disturbances, we
can also gain insights into recognizing cognitive decline and may contribute to overall
well-being.
The discrimination ability in the study was an accuracy of 0.709, with a sensitivity of
0.652 and a specificity of 0.781. di Biase introduced other studies that tried to discriminate
the gait of Parkinson’s disease by various measurements from control subjects with atleast 80% sensitivity and specificity [21]. Most previous studies have included clinicallyengaged, diagnosed patients and analyzed gait through complicated monitoring [34–38].
We supposed that the gait impairment of Parkinson’s disease was one of the most characteristic among other diseases and not difficult to diagnose with other symptoms without
gait information. Hence, it was possible to be promising to discriminate among their
participants. The discriminatory ability in our study was diminished due to the inclusionof various diseases. Recognizing the challenges that arose from the hardships involved indiscriminating among our study subjects, we undertook the challenge.
With advancements in AI and the motion capture technology of TDPT, there is potential for further development to differentiate or indicate various types of gait disturbances.
Gait movement disorders are expressed as wide-based, shuffling, dragging, small steps,
hesitating, frozen, propulsive, waddling, swaying, spastic, and ataxic. Even among specialists, certain gait symptoms exhibit limited inter-rater reliability [39]. In addition, patients’
symptoms fluctuate naturally, affected by circumstances, timing, and fatigue. These aspects
lie beyond our reach during the examinations conducted at the hospital. The system implemented in the study will assist us in perceiving and analyzing diverse gait disorders invarious settings.
4.2. Gait Analysis by TDPT-GT
TDPT-GT is an application of motion capture for various possible utilities, such as
making animations of humans and avatars and analyzing posture, dance, and sports. For
human health, the application may increase knowledge of human motion and may help
diagnose symptoms. It is significant that this application works on the iPhone with theprecise camera characteristics of the device and automatically generates the data of many
points of the entire body, including the head, trunk, arms, and legs. Furthermore, the data
incorporate the notion of time.
AI studies of movement in Parkinson’s disease (PD) were reviewed by Biase [21], who
focused on several features, for example, the maximum angles of a joint, stride length,
swings, and their statistical indexes. Each study tried to find characteristic disease features
among them. Hereditary spastic paraplegia and cerebral palsy were discriminated by
AI analysis of measures including strength and spasticity and kinematic gait metrics,
totaling 179 variables [22]; however, those measures were impracticable in clinical settings.
A smartphone was used to collect the tri-axial acceleration, and secondary associatedSensors 2023, 23, 6217

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statistical data were used to discriminate between PD and controls through AI analysis [23].
The advantage of our study was that the gait data were all primarily obtained automaticallyby a smartphone.
The present study’s algorithm of distinction of gait was learned frame by frame, moment by moment, and one by one. Additionally, the pathological gaits were distinguishedfrom those of the controls by the entire gait time of a person. Although the learning method
was simple, gait was evaluated with the concept of time because the whole gait of thesubjects was analyzed for around one minute. Compared with previous studies of gait
analysis with AI, we employed the most convenient and low-cost motion capture, using Al
analysis but systematically for the whole human body and the gait process.
Discoveries Made by AI
Feature importance, which indicates particular coordinates that strongly affect thedecision of AI analysis, is being used in many fields of medicine, such as genetics [40], thedrug design field [41], and survival prediction [42]. In the study, high feature importancescores were found in the coordinates of the hip joint, the buttocks, the knee, and the centerof the head. Despite the inherent lack of interpretability in AI analysis, it was observedthat the findings predominantly pertained to the body’s trunk. We usually describe thegait mainly as the movement of the legs; however, the model seemed to focus on postureduring walking. Therefore, these results of feature importance suggested that we should
reconsider the clinical importance of posture and the trunk while walking. Such findings
may be valuable in reconstructing medical assessments and rehabilitation knowledge. The
results of the present study implied that pathological gaits, mainly with hydrocephalus
and neurodegenerative diseases, had common mechanisms of posture impairment. Static
posture and posture during gait are also objected to analysis with sensors [10,43–47].
In neurodegenerative diseases, Parkinson’s disease is the most analyzed for its posture
impairment [10,48,49]. In patients with Huntington’s disease, the wearable device revealed
that the magnitude of thoracic and pelvic trunk movements was significantly higher during
static periods (such as sitting or standing) compared to walking [46]. The function of thetrunk is associated with balance and falls, expressing frailty, among older people withoutcausative diseases [50]. Trunk posture adaptation before the onset of decline in gait speed
was found in at-risk community-dwelling older adults [51]. Leveraging AI to detect trunk
function will contribute to the early prevention and diagnosis of frailty or diseases.
We suppose it is advisable not to separate the evaluation and discussion of postureand gait. For postural neural control, the mesencephalic area, the reticular formation,
the forebrain structure, and the spinal locomotor networks, cerebral, cerebellar, and basal
ganglia were affected [52]. The instability of the trunk can be attributed to various factors,
such as peripheral nervous system disorders and psychosomatic disorders [53]. Trunkmuscles are also crucial and responsive to training for the preventing falls [54]. Due to thecomplexity of the system, clinicians face challenges in recognizing disruptions in trunk
function through direct observation. Our findings could facilitate earlier diagnosis orintervention for persistent impairments in posture and gait.
4.3. Limitations
The methodological limitation was that we were within the capability of the present
smartphone (iPhone). The data we obtained from the system were 30 fps, while the VICON(Vicon Motion Systems, Oxford, United Kingdom), the standard for motion capture, could
generate data of 100 Hz. More frequent analysis on the iPhone may exceed its processing
ability and make the iPhone slower in its performance. In an effort to achieve convenienceand versatility, as alternative motion capture systems, Microsoft Kinect [10,55] operated
at a frame rate of 30 fps, while the Kinect of YOLOv4 [56] had a frame rate of 17.5 fps.
Clinically, 30 fps seemed enough for human gait, which did not have a high-frequency
motion, especially since patients needed to walk safely within the angle view of the iPhone

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camera. Even with the limitations of using an iPhone, we were able to swiftly obtain gait
data directly at the hospital or the common space.
The clinical limitation of the study was that the severity of the pathological gait wasdifficult to describe and control. Therefore, learning may be easy if very severe pathologicalgaits are included. However, the patients with severe gait disturbance were not included
naturally because the method of the gait test in the study needed the participants to walksafely and independently without assistance.
5. Conclusions
Gait data in the study were collected using a markerless motion capture system called
TDPT-GT, utilizing an iPhone. The study involved examining patients with iNPH, PD,
other neurodegenerative and neuromuscular diseases, and healthy volunteers. The deeplearning algorithm could distinguish a pathological gait from a control gait with an AUC
of 0.719 for the ROC and an accuracy of 0.702.
Author Contributions: Conceptualization, C.I., T.H. and S.Y.; data curation, T.K., M.H., S.U. and M.I.;
formal analysis, C.I., T.H., H.Y. and Y.K. (Yuta Komoriya); funding acquisition, S.Y. and Y.O.; investigation, C.I.; methodology, T.H., H.Y., T.F. and Y.K. (Yoshiyuki Kobayashi); project administration, S.Y.
and Y.O.; resources, S.Y. and Y.A.; software, Y.A.; supervision, K.K., M.I. and Y.O.; validation, T.H.
and H.Y.; visualization, T.H. and Y.A.; writing—original draft, C.I. and T.H.; writing—review editing,
C.I. and S.Y. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by research grants from the Japan Society for the Promotion of
Science, KAKENHI (grant number: 21K09098 and 21K07336); the G-7 Scholarship Foundation in
2020; the Taiju Life Social Welfare Foundation in 2020 and 2022; and the Osaka Gas Group Welfare
Foundation in 2022. The funders had no effect on or involvement in the writing of this article. The
funders played no role in the study design, data collection, and analysis, in the decision to publish
the results, or in the preparation of the manuscript.
Institutional Review Board Statement: The study was conducted according to the Declaration of
Helsinki and approved by the Ethics Committee for Human Research of Shiga University of Medical
Science (protocol code: R2019-337, date of approval: 17 April 2020) and the Ethical Committee of
Yamagata University School of Medicine (protocol code: 2020-10, date of approval: 12 April 2021).
Informed Consent Statement: Written informed consent was obtained from all subjects involved inthis study.
Data Availability Statement: Data generated or analyzed during the study are available from thecorresponding author upon reasonable request.
Acknowledgments: We appreciate the cooperation of the office staff of Takahata Town, especially
Masanori Togashi; the project collaboration of nurses and office staff at Takahata Public Hospital;
the cooperation in the gait examination by the rehabilitation staff of Shiga University Hospital and
Takahata Public Hospital, especially Masashi Shindo and Ikue Nagahashi; and the special assistanceprovided by Emi Murayama and Takee Kikuchi in contacting the volunteers and patients and the
instruction for the analysis provided by Hiroto Kimura from the Institute for the Promotion of Medical
Science at Yamagata University.
Conflicts of Interest: Yukihiko Aoyagi was employed by Digital Standard Co., Ltd. The remaining
authors declare that this research was conducted in the absence of any commercial or financial
relationships that could be interpreted as a potential conflict of interest.

Abbreviations
The following abbreviations are used in this manuscript:
AI
Artificial intelligence
AUC
Area under the curve
PD
Parkinson’s disease
ROC
Receiver opening characteristics
TDPT-GT Three-Dimensional Pose Tracker for Gait Test

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