sensors
ArticleFluctuations in Upper and Lower Body Movement during
Walking in Normal Pressure Hydrocephalus and Parkinson’s
Disease Assessed by Motion Capture with a Smartphone
Application, TDPT-GT
Chifumi Iseki 1,2, * , Shou Suzuki 3 , Tadanori Fukami 3 , Shigeki Yamada 4,5,6 , Tatsuya Hayasaka 7 ,Toshiyuki Kondo 2 , Masayuki Hoshi 8 , Shigeo Ueda 9 , Yoshiyuki Kobayashi 10 , Masatsune Ishikawa 6,11 ,Shigenori Kanno 1 , Kyoko Suzuki 1 , Yukihiko Aoyagi 12 and Yasuyuki Ohta 2
1

2

3

4

5

6

7

8

Citation: Iseki, C.; Suzuki, S.; Fukami,

9T.; Yamada, S.; Hayasaka, T.; Kondo,

10T.; Hoshi, M.; Ueda, S.; Kobayashi, Y.;
Ishikawa, M.; et al. Fluctuations inUpper and Lower Body Movement
during Walking in Normal Pressure
Hydrocephalus and Parkinson’s

11
12

*

Department of Behavioral Neurology and Cognitive Neuroscience, Tohoku University Graduate School of
Medicine, Sendai 980-8575, Japan; s-kanno@med.tohoku.ac.jp (S.K.); kyon@med.tohoku.ac.jp (K.S.)
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 Informatics, Faculty of Engineering, Yamagata University, Yonezawa 992-8510, Japan;
t222164m@st.yamagata-u.ac.jp (S.S.); fukami@yz.yamagata-u.ac.jp (T.F.)
Department of Neurosurgery, Nagoya City University Graduate School of Medical Sciences,
Nagoya 467-8601, Japan; shigekiyamada393@gmail.com
Interfaculty Initiative in Information Studies, Institute of Industrial Science, The University of Tokyo,
Tokyo 113-8654, Japan
Normal Pressure Hydrocephalus Center, Rakuwakai Otowa Hospital, Kyoto 607-8062, Japan;
rakuwadr1001@rakuwadr.com
Department of Anesthesiology, Yamagata University School of Medicine, Yamagata 990-2331, Japan;
hayasakatatsuya1101@gmail.com
Department of Physical Therapy, Fukushima Medical University School of Health Sciences,
10-6 Sakaemachi, Fukushima 960-8516, Japan; mhoshi@fmu.ac.jp
Shin-Aikai Spine Center, Katano Hospital, Katano 576-0043, Japan; uedashigeo@yahoo.co.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
Rakuwa Villa Ilios, Rakuwakai Healthcare System, Kyoto 607-8062, Japan
Digital Standard Co., Ltd., Osaka 536-0013, Japan; y.aoyagi@digital-standard.com
Correspondence: chi.iseki@gmail.com; Tel.: +81-22-717-7358

Disease Assessed by Motion Capture
with a Smartphone Application,
TDPT-GT. Sensors 2023, 23, 9263.
https://doi.org/10.3390/s23229263
Academic Editor: Cecilia Garcia
Received: 26 October 2023
Revised: 17 November 2023
Accepted: 17 November 2023
Published: 18 November 2023

Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.

Abstract: We aimed to capture the fluctuations in the dynamics of body positions and find thecharacteristics of them in patients with idiopathic normal pressure hydrocephalus (iNPH) and Parkinson’s disease (PD). With the motion-capture application (TDPT-GT) generating 30 Hz coordinates at
27 points on the body, walking in a circle 1 m in diameter was recorded for 23 of iNPH, 23 of PD, and92 controls. For 128 frames of calculated distances from the navel to the other points, after the Fouriertransforms, the slopes (the representatives of fractality) were obtained from the graph plotting thepower spectral density against the frequency in log–log coordinates. Differences in the average slopes
were tested by one-way ANOVA and multiple comparisons between every two groups. A decreasein the absolute slope value indicates a departure from the 1/f noise characteristic observed in healthyvariations. Significant differences in the patient groups and controls were found in all body positions,where patients always showed smaller absolute values. Our system could measure the whole body’s
movement and temporal variations during walking. The impaired fluctuations of body movement in
the upper and lower body may contribute to gait and balance disorders in patients.

This article is an open access article
distributed under the terms andKeywords: fractality; 1/f noise; smartphone device; gait analysis; neurodegenerative diseases

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

Sensors 2023, 23, 9263. https://doi.org/10.3390/s23229263

https://www.mdpi.com/journal/sensors

Sensors 2023, 23, 9263

2 of 15

1. Introduction
Describing and measuring the gaits of patients have long been clinical challenges.
In previous studies, terms describing pathological gait, such as short stepped, shuffling,
freezing, wide based, festination, hemiplegic, spastic, ataxic, and instability, have been
useful and identifiable characteristics for diagnosing diseases, such as Parkinson’s disease(PD) [1–4], cerebrospinal degeneration [4–6], and idiopathic normal pressure hydrocephalus(iNPH) [7–10]. These characteristics are observed in aspects such as the length, speed, timing, and laterality of movement in the feet and legs within the range of human visual
perception. Clinicians learn from textbooks and examine patients for many years; however,
we are often unable to reach an agreement on descriptions of gait [8]. The quantitative dynamic evaluation of gait has also been difficult because of the uncertainty of human vision.
Therefore, studies have tried to assess the cycle and variability of gait with various sensors,
such as floor sheets [5,6,11], wearable devices [12,13], and motion-capture systems [14,15].
To introduce our method for measuring gait dynamics, let us mention the fluctuations
found in nature. Nature hosts some intriguing examples of self-similar structures, such as
the Roman cauliflower, in which almost exact copies of the entire flower may be recognizedon multiple smaller scale examples, and the correlations between the frequency and sizeof the copy structure express the 1/f pattern [16]. We can also observe the same pattern
in many biological phenomena and complex systems in nature [17–19]. Human motion isdiverse and contains inherent variability, yet it is not entirely random. For instance, when
standing still in a relaxed state, the swaying motion exhibits fractal, 1/f properties [20],exhibiting a roughly proportional relationship between log power and log frequency ina spectral power analysis. In other words, 1/f relations refer to self-similar fluctuations
observed across multiple time scales, indicating fractal patterns. By applying fractality,
healthy and pathological gaits, as well as their differences, have been explored in the field of
bioengineering [21–29]. These studies assessed the dynamic aspect of gait, with a focus oninconsistent fluctuation, which was different from classical quantitative analysis centeredon calculating averages of key parameters, such as stride length, swing, and speed, toestablish stable and consistent patterns in gait. Hausdorff et al. proposed watching thestride-to-stride fluctuations while considering that the fluctuations are not just noise but
convey important information [21,30]. In healthy adults, the fluctuations from stride to
stride are not completely random; instead, each stride time is related to adjacent stride
times and to stride times hundreds of strides later [22]. The distribution of strides in ahealthy walk has a 1/f-like structure [31]. This pattern of fluctuation (noise) has also beenreferred to as pink noise. In contrast, noise that is unhealthy, unstable, and lacks a fractal
pattern is referred to as white noise.
The advancements in these sensors and methods for capturing the dynamics of humanbody movements have significantly contributed to the current wealth of knowledge. As
a result, our goal is to make these beneficial sensors as easily accessible as possible forpatients in need. The system we employed was a smartphone-based markerless motioncapture system using artificial intelligence (AI), named TDPT-GT (Three-Dimensional Pose
Tracker for Gait Test), which essentially consisted of taking videos of the patients on an
iPhone [32–34]. A noninvasive motion-capture application allowed us to use this system in
the general room for outpatients. The patient setup required only several minutes [32], and,for the analysis, a minimal recording time of a few seconds was sufficient, as seen in thecurrent study. Among recent gait-analysis methods, our system stands out for its superior
availability and convenience in clinical applications [34]. Hence, we aimed to establish
a connection between the application of this measurement tool [32] and its meaningful
clinical applications. The initial trial involved extracting two-dimensional characteristics ofpathological gait [33], while the second trial delved into the use of AI for distinguishingvarious gait patterns [34]. This manuscript represents our third publication, focusing on
sensing the pathology of the temporospatial aspect of patients’ gait.
The role of the trunk is recognized as one of the key components of the gait system,with many studies having been conducted on this aspect [35–41]. Trunk lean [35], pos-

Sensors 2023, 23, 9263

3 of 15

ture [36,37], balance [38–40], and muscle power [41] are all associated with maintaininga healthy walking mechanism. Disturbed trunk function results in slowness of gait [37]and falls [38,41]. Additionally, the movement of the upper limbs in patients with various
diseases has been suggested to be linked to gait disturbance [42–46]. Usually, independent
analyses—for example, examinations of writing or hand-to-mouth movements—were conducted, and the connection between the upper and lower limbs was deliberated [42–44].
Some studies using motion capture evaluated the ranges of motion of hands, elbows, orarms during walking; however, these could not include the temporospatial concepts ofthe movements of each part [45,46]. Hence, for gait analysis, we aim for an inclusive
examination of body parts, including the trunk and upper limbs, while also being able toobserve the time course during walking.
The gaits of patients with PD have been the most common ones subjected to study, mainly
using the methods mentioned above to evaluate the fluctuation in motion [22,24,26,28,31]. In
addition to reduced stride length and gait variability, PD patients showed impaired fractal
scaling of the gait [24,26,28], which means that the normal healthy noise (fluctuation) captured during gait cycles was decreased in PD patients. The same tendency was also seenin aged people [29], patients with multiple sclerosis [23], and those with multiple-system
atrophy [28]. Although the mechanism for changing this fluctuation during gait was notunclear, this approach has an impact on the research on gait dynamics, considering the underlying neural systems [19,22,25,31] and the development of rehabilitation strategies [26].
We used this method because it has the potential to assess gait dynamics using data fromour system, including up to 27 body positions. This approach enabled us to evaluate theupper body’s movements, a previously unexplored area during walking.
On the other hand, the gaits of iNPH patients have not been assessed based on theirfluctuation in dynamics. The pathological gait specific to iNPH is characterized by freezing
gait, wide-based gait, short steps (or senile gait with reduced stride length), shuffling gait(diminished step height), instability (unsteady gait), gait festination, difficulty changing
direction, and difficulties in standing up [1,8,47,48]. Video-recorded gait performance
before and after the spinal tap test and shunt surgery has been recommended for gait
assessments in patients with iNPH [8,47]. We have prioritized the examination of the iNPH
gait [33], primarily due to the fact that the features and diagnosis of gait in iNPH remain
unclear, often being referred to as just “gait disturbance”. This time, we could focus on
the gaits of iNPH patients to find their characteristics and the difference compared to PD
patients, and on utilizing a convenient system that requires only a brief recording of afew seconds.
The study aimed to identify temporospatial abnormalities during walking-in patientswith iNPH and PD, as well as to discern the differences between the two diseases. Additionally, the study sought to explore the measurement and analysis of whole-body movement
using a noninvasive iPhone application, making it an easily accessible tool for all.
2. Materials and Methods
2.1. Ethical Approvals
The study design and protocol of this study were approved by the ethics committee forhuman research at Nagoya City University Graduate School of Medical Science (IRB number: 60-22-0111), Shiga University of Medical Science (R2019-337), and Yamagata University
School of Medicine (protocol code: 2021-10). All volunteers and patients participated inthis study after providing written informed consent. The study design was prospective
and observational. This study was conducted according to the approved guidelines of the
Declaration of Helsinki.
2.2. Study Population
We collected information on age, disease history, and gait from May 2021 to November
2022 at Yamagata University Hospital and Takahata Town Hospital. The subjects with PD
consisted of 23 patients with clinically established PD diagnosed according to the MDS

2.2. Study Population
Sensors 2023, 23, 9263

We collected information on age, disease history, and gait from May 2021 to Novemof 15
ber 2022 at Yamagata University Hospital and Takahata Town Hospital. The subjects4 with
PD consisted of 23 patients with clinically established PD diagnosed according to the MDS
clinical criteria for Parkinson’s disease [49]. Patients with iNPH consisted of 23 probableclinicalcriteria
fordiagnosed
Parkinson’s
disease to
[49].
with
iNPH consistedof 23 probableor definite
iNPH
accordingthePatientsJapaneseguidelines
and the managementofordefinitediagnosedto theguidelineswho
and participatedthe management
of
iNPH(3rdiNPH
edition)[9]. The according
controls were
92 Japanese
healthy volunteers
in localiNPH
edition)[9].
Thedidcontrols
were
92 healthy volunteers
who Gait
participatedin localhealth(3rdcheckups
andwho
not havea neurodegenerative
disease.
trials of patients
healthcheckups
and
who
did
not
have
a
neurodegenerative
disease.
Gait
trialsofpatients
were examined several times on diﬀerent days. The gait of a volunteer was recorded at awereexaminedtimes ondifferent
days.
gait of awas
volunteer
was
at of
a
specific
time. Toseveral
be includedin eithergroup,
anTheindividual
required
torecorded
be capable
specific
time.
To
be
includedineithergroup,
anindividual
was
requiredtobecapableofwalking independently and safely for several minutes; using a single-point cane was thewalking
independently
andsafelyfor several
using a ofsingle-point
canerelatively
was the
only assistanceallowed.
This
criterion
leads minutes;
to the selection
patients with
only
allowed. This criterion leads to the selection of patients with relatively mild
mildassistancemotor symptoms.
motorOnesymptoms.
iNPH and two PD patients had mild resting and postural hand tremors at a rateOne
iNPH
and two
PD patients
had mild
resting
posturalNo
hand
tremors
at a rateof 1 on the
UnifiedParkinson’s
DiseaseRatingScaleand(UPDRS).
patients
expressing
of
1
on
the
UnifiedParkinson’s
DiseaseRatingScale(UPDRS).
No
patients
expressing
dyskinesia during walking were included. Since the study focused solely on the gait cadyskinesia
duringthe
walking
weretime,
included.
Since
the studyfactors
focusedon thegaitpabilities during
recordingpotential
influencing
onsolelymobility,suchascapabilities
duringtherecordingtime,
potential
influencing
factorsonmobility,suchasmedications, the timing of medication intake, shunt treatment, or other examinations,medications,the timing
were not controlled
for ofinmedication
this study. intake, shunt treatment, or other examinations, were
not controlled for in this study.
2.3. Data Acquisition of Estimated Three-Dimensional Relative Coordinates during 1 m Circle
2.3. Data Acquisition of Estimated Three-Dimensional Relative Coordinates during 1 m
Walking
CircleWalkingTofitthetheframeframeofofthe
theapplication,
application,which
whichwaswasplacedplacedabout
about33m
maway
awayfrom
fromthethegaitgait
To fittrail,
the
gait
of
each
participant
wasrecorded.
Theywalkedina1mcircle
for
1–3
laps,
trail, the gait of each participant was recorded. They walked in a 1 m circle for 1–3 laps,
clockwiseand
andcounterclockwise
counterclockwise(Figure(Figure11shows
showsaasample
sampleof
ofthe
thegait
gaitcircle;
circle;patientspatientswere
wereclockwise
recordedin
inthe
thedailydailyconsultation
consultationroom).
room).
recorded

Figure 1.
1. Sample
of ofthethegaitgaitrecordingon theapplication,
TDPT-GT(Three-DimenFigure
Samplepicture
picturerecording
oniPhonethe iPhone
application,
TDPT-GT(Threesional Pose Tracker for Gait Test). Participants were recorded with a smartphone while walking in
Dimensional Pose Tracker for Gait Test). Participants were recorded with a smartphone while
walking in a 1 m circle (line of red dots) at a slow and comfortable speed. The stick figure is constructed with the 24 body points calculated by the deep leaning system of this application. The
non-English word at the right side is the option button for changing the skeleton figure to a doll-likeanimation figure.

a 1 m circle (line of red dots) at a slow and comfortable speed. The stick figure is constructed withthe 24 body points calculated by the deep leaning system of this application. The non-English word
at the right side is the option button for changing the skeleton figure to a doll-like animation figure.
Sensors 2023, 23, 9263

5 of 15

The TDPT-GT application could measure the 3D relative coordinates of the humanbody at 30 frames per second (fps) with 448 × 448 pixels and RGB color using an iPhone
cameraThe
without
any markers
forcould
motioncapture.
of thecoordinates
technology
thisapplicaTDPT-GT
application
measuretheDetails
3D relative
ofof
the
human
body
30 frames
per second
with 448
448prior
pixelsand RGB coloran iPhone aption
andatdataacquisition
were(fps)
describedin ×
our
publication[32].using
The TDPT-GT
camera estimates
without any
for motion
capture.
of the technologyof this theappliplication
themarkers
3D relative
coordinatesof Details
the following
24 body points:
nose,
cationanddataacquisitionwere
described
in
our
prior
publication
[32].
The
TDPT-GTnavel, and bilateral points, such as the eyes, ears, shoulders, elbows, wrists, thumbs, midestimates
3D relative
coordinates of thefollowing 24
points:the nose,cendleapplication
fingers, hips,
knees,theheels,and toes—calculated
coordinatesof body
3 bodypoints—thenavel,
andbilateralpoints,
such
as
the
eyes,
ears,
shoulders,elbows,wrists,thumbs,ter of the head, neck, and buttocks. The middle fingers and the thumbs weremiddle
capturedfingers, hips, knees, heels, and toes—calculated coordinates of 3 body points—the center ofaround the metacarpophalangeal joint. We extracted the sequential 128 frames (approxithe head, neck, and buttocks. The middle fingers and the thumbs were captured aroundmately 4 s) at every point during walking in a 1 m circle, when the data is as stable as
the metacarpophalangeal joint. We extracted the sequential 128 frames (approximately
possible,
exceptat thebeginningof whenwalking.
In thestudy,
positions
4 s) at every
point
duringwalkingand
in a the1 mendcircle,the data
is asstablebody
as possible,
wereclassified
into
the
trunk
positions(Figure
2):
the
head,
neck,
nose,
navel,
eyes,
except at the beginning and the end of walking. In the study, body positions were classifiedears,
shoulders,
hips,positions
buttocks,(Figureand knees,
theneck,
limbnose,positions:wrists,thumbs,midinto the trunk2): theandhead,navel,elbows,eyes, ears,
shoulders,hips,
dlebuttocks,
fingers, andheels,
andandtoesthe(Figure2). The positionsof the upper
body
were
markedabove
knees,
limb positions:elbows, wrists,
thumbs,middlefingers,
heels,
toes while(Figurethe2).positions
The positionsof lower
the upper
body
weremarkedabove(Figure
the navel,
theandnavel,of the
body
were
below
the navel2).while
the positions of the lower body were below the navel (Figure 2).

Figure2. 2.
Twenty-six
in thethestudy.
study.WhiteWhitedots
dots
arepositionsin thestudy.
FigureTwenty-sixbody
bodypositions
positions analyzed
analyzed inarethethepositionsin thestudy.
In In
thethedefinitionof
thestudy,
thetrunk
positionsareincluded
in
the
parts
of
gray
color,
anddefinition of the study, the trunk positions are included in the parts of gray color, and the thelimbpositions
Thedistancedistanceofof
eachpositionwascalculatedby thelimb positionsare
arethetheothers
othersonon thethe extremities.
extremities. The
eachpositionwascalculatedby thecoordinates
from
the
navel
(star)
toeachposition(dots).
The
positions
of
the
upper
body
indicated
coordinates from the navel (star) to each position (dots). The positions of the upper body indicatedthe dots above the navel (star) and those of the lower body were below the navel.
the dots above the navel (star) and those of the lower body were below the navel.

2.4.
FluctuationininBody
BodyPositions
Positions during
during Walking
2.4.
Fluctuation
WalkingFrom
128
framesofof
gait
cycles,body-positioncoordinates
coordinateswere
werecalculated
calculatedas
asthe
thedisFrom
128
framesgait
cycles,body-position
distances
26 body
positionsthe navel,
where
the Fouriertransformtancesof 26 ofbody
positionsfromfrom
the navel,
where
the Fouriertransform
donedonewithwith
PythonPython3.10.0producedbody-position
data
regardingthelength
from
the
navel
the 263.10.0 produced body-position data regarding the length from the navel to thetoother
other 26 points. A slope ( α) was obtained from the approximate line of the graph plotted
points. A slope (𝛼) was obtained from the approximate line of the graph plotted by coorby coordinates with the log of the power spectral density against the log of frequency
dinateswith the log of the power spectral density against the log of frequency (Figure 3).
(Figure 3).
P( f ) = k f α
P : power; f : f requency
logP( f ) = αlog f + logk
α = fluctuation index, in this study

𝑙𝑜𝑔𝑃(𝑓) = 𝛼𝑙𝑜𝑔𝑓 + 𝑙𝑜𝑔𝑘

Sensors 2023, 23, 9263

𝛼 = fluctuation index, in this study
In this study, the slope (α) of each set of body-position data was defined as a “fluctuation index” based on the previous reports where they mentioned gait fractality [13–15].
6 of 15
We employed the value of the slope (α) to represent the fluctuation. This process was
performed by clinically blinded engineers (S.S. and T.F.).

− 0.5 − 0.25

0

0.25

0.5

0.75

1

FigureFor eacheachsetsetofofbody-position
body-positiondata,
data,
Fouriertransform
Figure3.3.The
Theslope
slopeasasaafluctuation
fluctuation index.
index. For
thetheFouriertransform
wascompleted,
and
the
log
of
power
and
the
log
of
frequency
correlation
were
drawn
(the
black
was completed, and the log of power and the log of frequency correlation were drawn (the blackline).
line).
The
slope
(the
red
line)
of
the
approximate
line
was
a
fluctuation
index
in
the
study.
The slope (the red line) of the approximate line was a fluctuation index in the study.

In this study,
the slope (α) of each set of body-position data was defined as a “fluctu2.5. Statistical
Analysis
ation index” based on the previous reports where they mentioned gait fractality [13–15].
For the averages of the slopes in every body position, the diﬀerences between PD,
We employed the value of the slope (α) to represent the fluctuation. This process was
iNPH, and controls were tested by one-way ANOVA, of which the Tukey test was used toperformed by clinically blinded engineers (S.S. and T.F.).

compare each of the two groups. Statistical analysis was performed by Rcmr version 2.80 2.5.
on RStatisticalversion Analysis
4.2.2, and the significant level was defined as 5%.
For the averages of the slopes in every body position, the differences between PD,

3.iNPH,
Results
and controls were tested by one-way ANOVA, of which the Tukey test was used tocompareof the two groups. Statistical analysis was performed by Rcmr version 2.8-0
3.1.
ClinicaleachCharacteristics
on R
versionand thesignificant
was fordefinedas and5%. 92 patients with iNPH, PD,
The
total 4.2.2,
gait trials
were117, 56,leveland 184
23, 23,andthe controls, respectively (Table 1). Their average ages and standard deviations were
3. Results
77.0
6.4, 70.1± 6.0, and 72.3 ± 6.3, respectively, and they were not age-matched against
3.1.±Clinical
Characteristics
both patient groups. Symptoms of iNPH were clinically known to fluctuate with the time
The total gait trials were 117, 56, and 184 for 23, 23, and 92 patients with iNPH, PD,
and the controls, respectively (Table 1). Their average ages and standard deviations were77.0 ± 6.4, 70.1 ± 6.0, and 72.3 ± 6.3, respectively, and they were not age-matched against
both patient groups. Symptoms of iNPH were clinically known to fluctuate with the timeand day; therefore, we collected several trials from an individual, each with a different date.

Sensors 2023, 23, 9263

7 of 15

Table 1. Clinical characteristics of three datasets.

NumberSex (male/female)
Number of gait trials
Age (average ± SD)

iNPH

PD

Control

23
16/7
117
77.0 ± 6.4

23
13/10
56
70.1 ± 6.0

92
36/56
184
72.3 ± 6.3

iNPH, idiopathic normal pressure hydrocephalus; PD, Parkinson’s disease; SD, standard deviation.

3.2. Fluctuation Index for Each Body Position
In the average of the slope (α), as the fluctuation index for each body position, significant differences were found between iNPH and the controls (p < 1 × 10−7 ) in all positions
and those between PD and the controls (at most p < 1 × 10−3 ) in all positions. All absoluteindices in the patient groups were smaller than those of the controls (Table 2). In thecomparison between iNPH and PD, significant differences were found in seven upper
body positions and eight lower body positions (p < 0.05) (Table 2 and Figure 4), and thedifferences were spreading to the right-hand area (the wrist, the middle finger, and thethumb) (Figure 4).
Table 2. The averages of fluctuation indices in the three groups of controls, PD, and iNPH, and the pvalue from the comparison of PD and iNPH.

Upper body

Trunk

Limbs

Lower body

Trunk

Limbs

Controls

PD

iNPH

p

R. earHead
L. earR. eyeL. eyeR. shoulder
Neck
L. shoulder
Nose
R. elbow
L. elbow
R. middle finger
R. wrist
R. thumb
L. middle finger
L. thumb
L. wrist

−2.15
−2.15
−2.15
−2.15
−2.15
−2.15
−2.16
−2.16
−2.16
−2.25
−2.26
−2.31
−2.31
−2.33
−2.33
−2.35
−2.35

−2.07
−1.99
−1.98
−1.98
−1.98
−1.98
−2.00
−2.00
−1.99
−2.07
−2.06
−2.15
−2.14
−2.16
−2.10
−2.12
−2.11

−1.99
−1.89
−1.89
−1.90
−1.90
−1.90
−1.91
−1.90
−1.91
−1.99
−1.97
−2.02
−2.02
−2.04
−2.04
−2.06
−2.04

0.101
0.036 *
0.044 *
0.094
0.089
0.044 *
0.059
0.024 *
0.118
0.196
0.102
0.035 *
0.049 *
0.045 *
0.408
0.494
0.272

L. kneeR. hip joint
L. hip joint
R. knee
Buttocks
L. toeR. toeL. ankle
R. ankle

−2.20
−2.20
−2.21
−2.21
−2.21
−2.28
−2.29
−2.32
−2.35

−2.04
−2.05
−2.04
−2.03
−2.06
−2.05
−2.06
−2.09
−2.10

−1.92
−1.94
−1.95
−1.91
−1.95
−1.92
−1.92
−1.95
−1.96

0.023 *
0.019 *
0.093
0.015 *
0.019 *
0.038 *
0.012 *
0.018 *
0.015 *The order of lows was the ascending averages of absolute fluctuation indices in the controls. The significant
differences between iNPH and the controls were with p < 1 × 10−7 in all positions, and those between PD and thecontrols were at least with p < 1 × 10−3 in all positions. In the comparison between iNPH and PD, significant
differences were found in 7 upper body positions and 8 lower body positions (* p < 0.05). iNPH, idiopathic normalpressure hydrocephalus; PD, Parkinson’s disease.

The absolute values in both the upper and lower limbs were smaller than those in
the trunk (Figure 5). The absolute values were consistently smaller during circle walking
for individuals with iNPH, PD, and the control group in all body positions. Significantly

R. toeL. ankle
R. ankle

−2.29
−2.32
−2.35

−2.06
−2.09
−2.10

−1.92
−1.95
−1.96

0.012 *
0.018 *
0.015 *The order of lows was the ascending averages of absolute fluctuation indices in the controls. The
andsignificant diﬀerences between iNPH and the controls were with p < 1 × 10−7 in all positions,8 of
15those between PD and the controls were at least with p < 1 × 10−3 in all positions. In the comparison
between iNPH and PD, significant diﬀerences were found in 7 upper body positions and 8 lower
body positions (* p < 0.05). iNPH, idiopathic normal pressure hydrocephalus; PD, Parkinson’s dissmaller absolute values for iNPH than those for PD were found in seven positions in theease.

Sensors 2023, 23, 9263

upper body and eight positions in the lower body (Figure 5).

FigureThe
bodypositions
impairedfluctuation
fluctuationininpatientspatientswith
withiNPH
iNPHcompared
compared with
with PD.
PD. TheThe
Figure
4. 4.
The
body
positionsofof
impairedblack
circlessignificantlysmaller
smallerabsoluteabsolutefluctuation
fluctuation
indicesblack
circles(●)(•represent) representthe
thebody
body positions
positions with significantly
indicesin
in
iNPH
compared
with
PD.
The
fluctuation
These
body
positionswith
iNPH
compared
with
PD.
The
fluctuation
values
are
shown
in
Table
2.
Sensors 2023, 23, x FOR PEER REVIEW
9 of 16
diﬀerent
indices
betweendifferent
indices
betweeniNPH
iNPHandandPD
PDwerewerespreading
spreadingtotothe
thelower
lowerand
andupperupperbody,body, including
including thetheright-handarea.
right-handarea.

Randomness
Fluctuation index
iNPH*− 2.0-2

*

PD

iNPH
*

*

*

Control

-2.1
− 2.1

*

*

*

*

PD

*

*

*

L.

-1.9
− 1.9

tto
ck
s

-1.8
− 1.8

*

*

*

Control

-2.2
− 2.2

Trunk

Limbs
Upper body

le
an
c

an
kle
R.

e

to
e

L.

R.

to

kn
ee

Bu

R.

L.

hi
p

jo
int

ee
kn
L.

hi
p
R.

R.

ea
r
He
ad
L.
ea
r
R.
ey
e
L.
ey
R.
e
sh
ou
ld
er
Ne
L.
ck
sh
ou
ld
er
No
se
R.
el
bo
w
L.
R.
el
m
bo
id
w
dl
ef
in
ge
r
R.
w
ris
t
R.
L.
th
m
id umbdlefing
L. e r
th
um
b
L.
w
ris
t

-2.4
− 2.4

jo
int

-2.3
− 2.3

Trunk

Limbs
Lower body

Figure 5. The graph of fluctuation indices. The black line indicates iNPH, the gray line indicates
FigureThe
graphlineof fluctuation
indices.
The blackline indicatesiNPH,gray line indicatesPDPD
and5.thedottedindicates thecontrols.
The absolutevaluesweretheconsistently
smaller forand the dottedline
indicates
the controls.
Theinabsolutevalueswere
smaller
forabsoluteindividindividuals
with
iNPH,
PD, andthe control
this order,
acrossallconsistently
body positions.
Theuals with
iNPH,werePD, andthe smaller
control inthisthoseorder,
all body
positions. The
valuesofvaluesof iNPH
alwaysthanof acrossPD, withthe significancesof 7absolute
positionsin theiNPH were always smaller than those of PD, with the significances of 7 positions in the upper body
upper body and 8 positions in the lower body (*).
and 8 positions in the lower body (*).

4. Discussion
In this study, significant random fluctuations during walking were found in all body
positions of iNPH and PD patients compared to the controls. Pathological fluctuation patterns, in other words, random fluctuation patterns, were observed even in the limb positions of the upper body. We could measure the pathology of the temporospatial aspect ofSensors 2023, 23, 9263

9 of 15

4. Discussion
In this study, significant random fluctuations during walking were found in all body
positions of iNPH and PD patients compared to the controls. Pathological fluctuation
patterns, in other words, random fluctuation patterns, were observed even in the limb
positions of the upper body. We could measure the pathology of the temporospatial aspectof patients’ gaits with a handy motion-capture application on a single iPhone.
4.1. Analysis of Gait by the System of TDPT-GT
For gait analysis, various sensors, such as pedobarography, motion capture, floor
sensors, and wearable sensors, have been utilized. Optical motion-capture systems typicallyrequire the attachment of several markers to the body and the use of multiple cameras
for data collection [1–8]. Time-consuming preparation, recording, and the need for large
laboratory space limit their use in clinical settings and hospitals. Although wearable
devices [7,16–18] are commonly used, they are often incapable of capturing informationfrom multiple body parts simultaneously. Clinical demands have highlighted the necessityof a sensor that is noninvasive, requires short recording times, occupies minimal space for
examination, and provides comprehensive systemic information about the body. TDPT-GT,
a markerless motion-capture system, meets these requirements by enabling gait recordingby capturing videos on an iPhone and instantly generates coordinates for 27 body points.
Utilizing TDPT-GT, this study aimed to identify temporospatial abnormalities in thegaits of patients with neurodegenerative diseases. In the realm of time-series analysis,
detrended fluctuation analysis (DFA) is a statistical method that evaluates the self-affinityof a signal and was originally rooted in physics. Its application extends across diverse
research domains, including gene analysis [50]. To quantify how the dynamics fluctuate
over time while walking, DFA was used for gait analysis in the 1990s [51] and also heartrate analysis [52]. These fluctuations are described as being (1) uncorrelated white noise,(2) long-range correlations with a power-law scaling called pink noise [51] (or 1/f noise),or (3) Brownian noise [52], corresponding to an intentional random walk (generated in ahealthy person or composed of artificially shuffled data). In a normal gait pattern, complex
fluctuations of an unknown origin appear as (2), which corresponds to a 1/f-like noise [51].
Older people or individuals with disease showed (1) white noise; in other words, theirvariation of cycles was too random [51,52]. In the present study, we did not apply theexact DFA, which needed an integrated time series split into equal boxes, where a leastsquares line was used to fit the data [51,52]. The gait analysis in the present study was also
referenced from the DFA. Because our gait data initially exhibited significant variability
due to the circular walking of many body positions, we processed a whole of 128 frames(less than 5 s) and gathered information from multiple body positions without overlays ineach position. Our approach to data capture and analysis was efficient for comparing the
gaits of patients and controls.
Our recordings were characterized by individuals walking in circles. This walk wasnecessary within the hospital’s consultation room to facilitate a seamless examination
for patients with limited mobility. It was also essential to ensure stable recording within
one camera frame. This was atypical in earlier gait research, which primarily reliedon an analysis conducted in large laboratory rooms. That made it difficult to compare
results with those of the previous gait analysis, which typically involved walking instraight lanes. However, circle walking posed a considerable challenge for patients froma medical perspective. This type of walking includes turning movements that require
additional balance tasks beyond simple walking. As a result, circle walking likely induced
gait disorders more sensitively than straight walking, making it a potentially valuableassessment tool. Until now, circular walking in conjunction with TDPT-GT has beeneffective in recording pathological gait, as demonstrated in previous studies [32–34]. The
present study further supports this.
We were able to detect the dysfunction in the movement of the trunk and upper
body including the hands, during walking with this system of TDPT-GT. The mechanism

Sensors 2023, 23, 9263

10 of 15of trunk control and movement of the upper limbs in older individuals or those withvarious diseases has been suggested to be linked to gait disturbance [37,38,41–46]. The
present markerless motion-capture technology enabled the simultaneous measurement oftrunk, upper limb, and lower limb movements and allowed for a comprehensive analysis
over time.
4.2. Fluctuation during Walking in Patients with iNPH and PD
Our study found that the fluctuation system during walking was more impaired in
iNPH patients than in PD patients. The difference in gait between iNPH and PD patientshas been discussed in some prior research [1,2]; however, no previous reports assessed
fluctuation. Both diseases shared reduced gait velocity, due to a diminished and highlyvariable stride length [1]. Specific features of iNPH were broad-based with outwardly
rotated feet, diminished stride length [1,2], gait velocity, and disturbed equilibrium [2]. A
study assessed a series of iNPH patients and found that about 30% of complications ofPD (synucleinopathy) [42]. Some patients possibly have comorbid iNPH and PD, which
are impossible to differentiate; our study did not include comorbid cases. Regarding gaitdynamics, we found that the fluctuated body movement of iNPH patients tended to be
more impaired than that in the PD patients, meaning that this iPhone application could
possibly be used as a tool for differential diagnosis.
What made the difference in fluctuation during walking between iNPH and PD
patients? The external cues only mildly improved the gait disturbance in iNPH patients,
whereas they were highly effective in raising the stride length and cadence in PD patients [2].
In PD patients, walking with fixed-tempo rhythmic auditory stimulation can improve
many aspects of gait timing; however, it lowers fractal scaling (away from a healthy 1/f
structure) [53], which is an unhealthy pattern. The new, interactive rhythmic auditory
stimulation could re-establish healthy fluctuational gait dynamics in PD patients [22]. The
prevalence of dementia in iNPH patients is higher than that in PD patients; therefore, iNPH
patients may be distracted from following the instructions of auditory stimulation. Brain
networks are supposed to generate, control, and adjust the fractal function [54] captured bysignals such as imaging [55], electroencephalography [56], or connectivity [57] as well as
the function expressed in gait [21,30,51]. In other words, the healthy brain has fluctuationsof 1/f. In contrast to these various biological functions of the brain [54–57], dementia
diminishes the brain construction associated with the fractal formation of neurons andnetworks [21,30,54,58]. From the present study, it can be seen that a greater impairment of
the entire brain may lead to more pronounced impaired fluctuations in gait, highlighting
distinctions between iNPH and PD.
We were surprised to observe random fluctuations in the positions of the upper
body, including the hands, among the patient groups during walking. The significanceof the right hand in the difference between iNPH and PD patients was probably due tothe recording condition during the circular walking without controlling the direction of
the rounds; therefore, if we controlled that, the difference might disappear between theleft and right hands. Although it was possible that the potential differences in laterality
within each disease group could have affected the results, we were unable to conduct
a subanalysis due to the lack of precise data on laterality. When evaluating the gait ofpatients, the upper body or the arms are not often the subject of extensive focus. In
particular, the characteristics of gait disturbance in iNPH are known to affect their lower
body movements. The characteristics of gait disturbance in iNPH are especially known
to affect their lower body movements [8]. A few studies have evaluated the upper limb
function with a peg board in iNPH patients [59,60], where it was improved along with gait
after shunt surgery [60]. However, the association between dexterity in the upper limbs andthe fluctuation of motor control during walking is still unclear. Our previous study, using
the same motion-capture system, used AI to differentiate between pathological gait andnormal gait [34], and AI emphasized utilizing the trunk positions of the body [34]. Thus, we
speculated that the trunk-posture adaptation [61] and/or the subjective vertical position [62]Sensors 2023, 23, 9263

11 of 15

play a role in the movement of total or other body positions and contribute to gait orbalance (Figure 6). Kinetic problems, including problems with muscle tone, induce posture
impairment [63]; this may be associated with the muscle tonus symptoms of PD and iNPH,
such as akinesia, dyskinesia, or paratonia (Figure 6). Although the fractality or fluctuation
Sensors 2023, 23, x FOR PEER REVIEW
12 of 16
pattern of the upper limb has not been explored previously, using this system, we wereable to detect it among patients with PD and iNPH from temporospatial perspectives.

Figure6. 6.
Schemaof ofthethesuspected
development
FigureSchemasuspected
developmentofofgait
gaitdisturbance
disturbanceand
andimbalance
imbalance aﬀectedaffected byby therandomfluctuationof systemic
bodybody
partsparts
duringgait gaitin patients
withwith
iNPH
andandPD.PD.
Other
sympthe random
fluctuationof systemic
duringin patients
iNPH
Other
toms
of the diseases
aﬀectedeach other.
symptomsof the diseases
affectedeach other.

Prospectsfor
forUsing
UsingthetheTechnology
Technology
4.3.4.3.
Prospects
The
futuredevelopment
development ofof TDPT-GT
TDPT-GT may
automatic
functionThe
futuremayinclude
includethe
theadditional
additional
automatic
functionassociated with this fluctuation index. Moreover, incorporating the ability to sense correlaassociated with this fluctuation index. Moreover, incorporating the ability to sense corretions between upper and lower limb indices will contribute to advancements. Optionallations between upper and lower limb indices will contribute to advancements. Optional
applications working on TDPT-GT, designed to produce seamless recording and display of
applications
working
TDPT-GT,seamless
recordingand display
indices, empower
the onusers
to providedesigned
feedback to
onproduce
gait information
anytimeand anywhere.
of Itindices,
empower
the
users
to
providefeedbackon
gait
information
anytimeandallanyis likely to be beneficial for determining overall body balance during walking forwhere.
It isandlikelyto people.
be beneficial for determining overall body balance during walking forpatientsolder
all patientsand olderpeople.has the potential to be employed in diagnosing or assessing the
The current
application
outcomes
after therapy
or rehabilitation.
This is to
crucialfor preventing
falls in patientswith
The current
application
has the potential
be employed
in diagnosing
or assessing[7,8,10],PD therapy[3,4,64,65],
diseases, orThis
old isage[66]. Fallers
tended to falls
presenthightheiNPHoutcomesafter
or otherrehabilitation.
crucialfor preventing
in patients
consistency
with power
spectral density
in the mediolateral
Thetendedunpredictable
with
iNPH [7,8,10],
PD [3,4,64,65],
other diseases,
or old ageaxis[66].[67].
Fallers
to present
body
movements
experienced
duringwalkingby patients
can lead
imbalanceandhighconsistency
withpower spectral
densityin themediolateral
axisto[67].
The unpredictpotential
falls.
However,patientsmay
notalwaysexpressorreportthese
symptoms
able body movements experienced during walking by patients can lead to imbalance andconsistently. Hence, integrating the TDPT-GT application to identify bodily fluctuations
potential falls. However, patients may not always express or report these symptoms concould serve as a helpful tool for detecting challenging symptoms. Moreover, these lesssistently. Hence, integrating the TDPT-GT application to identify bodily fluctuations
apparent symptoms require thorough assessment before and after rehabilitation. The
could
serve
as a canhelpfultool fordetectingchallenging
Moreover, these lessexisting
systembe applied
effectively
in these
specific symptoms.
areas.
apparent symptoms require thorough assessment before and after rehabilitation. The existing system can be applied eﬀectively in these specific areas.

4.4. Limitations
The limitations of the study stem from the use of the motion-capture system, which
generated estimated 3D-relative coordinates. Previously, we demonstrated the correla-Sensors 2023, 23, 9263

12 of 15

4.4. Limitations
The limitations of the study stem from the use of the motion-capture system, which
generated estimated 3D-relative coordinates. Previously, we demonstrated the correlationsof each coordinate against VICON (Oxford, UK) as part of establishing full reliability andvalidity [32]. However, for the current study, it was necessary to analyze each coordinate ofeach body position independently, specifically focusing on temporospatial changes. Thisapproach was efficient in assessing the observed differences between the disease groups
and the control group. The consistent distinct trends between the disease groups andcontrols might suggest that the coordinates were suitable for this type of analysis.
5. Conclusions
By employing TDPT-GT, a user-friendly motion-capture system application on the
iPhone, we succeeded in sensing the disrupted fluctuations in the movement of the entire
upper and lower body, the trunk, and limbs, during walking in patients with iNPH and
PD. The present study may provide new insights into gait analysis, including whole-bodymovements and their dynamics for patients.
Author Contributions: Conceptualization, C.I., S.S. and T.F.; data curation, C.I., T.K. and M.H.; formal
analysis, C.I. and S.S. funding acquisition, S.Y. and Y.O.; investigation, S.S. and C.I.; methodology, S.S.
and T.F.; project administration, S.Y. and Y.O.; resources, S.U., S.Y., M.I. and Y.A.; software, Y.A. andT.H.; supervision, Y.K., S.Y., K.S. and Y.O.; validation, S.K., S.S. and T.F.; visualization, C.I. and T.F.;
writing—original draft, C.I.; writing—review editing, C.I., T.F. and S.Y. All authors have read andagreed 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 design and protocol of this study were approvedby the ethics committee for human research at Nagoya City University Graduate School of Medical
Science (IRB number: 60-22-0111), Shiga University of Medical Science (R2019-337), and Yamagata
University School of Medicine (protocol code: 2021-10).
Informed Consent Statement: Written informed consent was obtained from all subjects involved inthis study.
Data Availability Statement: Data are contained within the article.
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; thecooperation in the gait examination by the rehabilitation staff of Takahata Public Hospital, especially
Masashi Shindo and Ikue Nagahashi; and the special assistance provided by Emi Murayama andTakee Kikuchi in contacting the volunteers and patients.
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.

References
1.
2.
3.

Stolze, H.; Kuhtz-Buschbeck, J.P.; Drücke, H.; Jöhnk, K.; Illert, M.; Deuschl, G. Comparative analysis of the gait disorder of normalpressure hydrocephalus and Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 2001, 70, 289–297. [CrossRef] [PubMed]
Bugalho, P.; Alves, L.; Miguel, R. Gait dysfunction in Parkinson’s disease and normal pressure hydrocephalus: A comparative
study. J. Neural Transm. 2013, 120, 1201–1207. [CrossRef]
Silva de Lima, A.L.; Evers, L.J.W.; Hahn, T.; Bataille, L.; Hamilton, J.L.; Little, M.A.; Okuma, Y.; Bloem, B.R.; Faber, M.J. Freezingof gait and fall detection in Parkinson’s disease using wearable sensors: A systematic review. J. Neurol. 2017, 264, 1642–1654.
[CrossRef]

Sensors 2023, 23, 9263

4.
5.
6.
7.
8.
9.

10.
11.
12.
13.

14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.

26.
27.
28.

29.

30.
31.

13 of 15

Raccagni, C.; Nonnekes, J.; Bloem, B.R.; Peball, M.; Boehme, C.; Seppi, K.; Wenning, G.K. Gait and postural disorders inparkinsonism: A clinical approach. J. Neurol. 2020, 267, 3169–3176. [CrossRef] [PubMed]
Gouelle, A.; Megrot, F.; Presedo, A.; Husson, I.; Yelnik, A.; Pennecot, G.F. The gait variability index: A new way to quantify
fluctuation magnitude of spatiotemporal parameters during gait. Gait Posture 2013, 38, 461–465. [CrossRef] [PubMed]
Buckley, E.; Mazza, C.; McNeill, A. A systematic review of the gait characteristics associated with Cerebellar Ataxia. Gait Posture
2018, 60, 154–163. [CrossRef]
Debu, B.; De Oliveira Godeiro, C.; Lino, J.C.; Moro, E. Managing Gait, Balance, and Posture in Parkinson’s Disease. Curr. Neurol.
Neurosci. Rep. 2018, 18, 23. [CrossRef]
Ishikawa, M.; Yamada, S.; Yamamoto, K. Agreement study on gait assessment using a video-assisted rating method in patientswith idiopathic normal-pressure hydrocephalus. PLoS ONE 2019, 14, e0224202. [CrossRef]
Nakajima, M.; Yamada, S.; Miyajima, M.; Ishii, K.; Kuriyama, N.; Kazui, H.; Kanemoto, H.; Suehiro, T.; Yoshiyama, K.; Kameda,M.; et al. Guidelines for Management of Idiopathic Normal Pressure Hydrocephalus (Third Edition): Endorsed by the JapaneseSociety of Normal Pressure Hydrocephalus. Neurol. Med. Chir. 2021, 61, 63–97. [CrossRef]
Milletti, D.; Randi, F.T.; Lanzino, G.; Hakim, F.; Palandri, G. Gait Apraxia and Hakim’s Disease: A Historical Review. Biomedicines
2023, 11, 1086. [CrossRef]
Milne, S.C.; Hocking, D.R.; Georgiou-Karistianis, N.; Murphy, A.; Delatycki, M.B.; Corben, L.A. Sensitivity of spatiotemporal gaitparameters in measuring disease severity in Friedreich ataxia. Cerebellum 2014, 13, 677–688. [CrossRef] [PubMed]
Yamada, S.; Aoyagi, Y.; Yamamoto, K.; Ishikawa, M. Quantitative Evaluation of Gait Disturbance on an Instrumented Timed
Up-and-go Test. Aging Dis. 2019, 10, 23–36. [CrossRef] [PubMed]
Yamada, S.; Aoyagi, Y.; Ishikawa, M.; Yamaguchi, M.; Yamamoto, K.; Nozaki, K. Gait Assessment Using Three-Dimensional
Acceleration of the Trunk in Idiopathic Normal Pressure Hydrocephalus. Front. Aging Neurosci. 2021, 13, 653964. [CrossRef][PubMed]Kobayashi, Y.; Hobara, H.; Matsushita, S.; Mochimaru, M. Key joint kinematic characteristics of the gait of fallers identified byprincipal component analysis. J. Biomech. 2014, 47, 2424–2429. [CrossRef] [PubMed]
Kobayashi, Y.; Hobara, H.; Heldoorn, T.A.; Kouchi, M.; Mochimaru, M. Age-independent and age-dependent sex differences ingait pattern determined by principal component analysis. Gait Posture 2016, 46, 11–17. [CrossRef] [PubMed]
Hardstone, R.; Poil, S.S.; Schiavone, G.; Jansen, R.; Nikulin, V.V.; Mansvelder, H.D.; Linkenkaer-Hansen, K. Detrended fluctuation
analysis: A scale-free view on neuronal oscillations. Front. Physiol. 2012, 3, 450. [CrossRef]
Neumcke, B. 1/f noise in membranes. Eur. Biophys. J. 1978, 4, 179–199. [CrossRef]
Gilden, D.L.; Thornton, T.; Mallon, M.W. 1/f noise in human cognition. Science 1995, 267, 1837–1839. [CrossRef]
Diniz, A.; Wijnants, M.L.; Torre, K.; Barreiros, J.; Crato, N.; Bosman, A.M.; Hasselman, F.; Cox, R.F.; Van Orden, G.C.; Delignieres,
D. Contemporary theories of 1/f noise in motor control. Hum. Mov. Sci. 2011, 30, 889–905. [CrossRef]
Yamada, N. Chaotic swaying of the upright posture. Hum. Mov. Sci. 1995, 14, 711–726. [CrossRef]
Hausdorff, J.M. Gait variability: Methods, modeling and meaning. J. Neuroeng. Rehabil. 2005, 2, 19. [CrossRef] [PubMed]
Hove, M.J.; Suzuki, K.; Uchitomi, H.; Orimo, S.; Miyake, Y. Interactive rhythmic auditory stimulation reinstates natural 1/f timing
in gait of Parkinson’s patients. PLoS ONE 2012, 7, e32600. [CrossRef]
Huisinga, J.M.; Mancini, M.; St George, R.J.; Horak, F.B. Accelerometry reveals differences in gait variability between patientswith multiple sclerosis and healthy controls. Ann. Biomed. Eng. 2013, 41, 1670–1679. [CrossRef] [PubMed]
Herman, T.; Weiss, A.; Brozgol, M.; Giladi, N.; Hausdorff, J.M. Gait and balance in Parkinson’s disease subtypes: Objective
measures and classification considerations. J. Neurol. 2014, 261, 2401–2410. [CrossRef] [PubMed]
Schaefer, A.; Brach, J.S.; Perera, S.; Sejdic, E. A comparative analysis of spectral exponent estimation techniques for 1/f(beta)
processes with applications to the analysis of stride interval time series. J. Neurosci. Methods 2014, 222, 118–130. [CrossRef][PubMed]Uchitomi, H.; Ogawa, K.-i.; Orimo, S.; Wada, Y.; Miyake, Y. Effect of Interpersonal Interaction on Festinating Gait Rehabilitationin Patients with Parkinson’s Disease. PLoS ONE 2016, 11, e0155540. [CrossRef]
Fu, C.; Suzuki, Y.; Morasso, P.; Nomura, T. Phase resetting and intermittent control at the edge of stability in a simple biped model
generates 1/f-like gait cycle variability. Biol. Cybern. 2020, 114, 95–111. [CrossRef]
Sidoroff, V.; Raccagni, C.; Kaindlstorfer, C.; Eschlboeck, S.; Fanciulli, A.; Granata, R.; Eskofier, B.; Seppi, K.; Poewe, W.; Willeit, J.;et al. Characterization of gait variability in multiple system atrophy and Parkinson’s disease. J. Neurol. 2021, 268, 1770–1779.
[CrossRef]
Dingwell, J.B.; Salinas, M.M.; Cusumano, J.P. Increased gait variability may not imply impaired stride-to-stride control of walking
in healthy older adults: Winner: 2013 Gait and Clinical Movement Analysis Society Best Paper Award. Gait Posture 2017, 55,
131–137. [CrossRef]
Hausdorff, J.M. Gait dynamics, fractals and falls: Finding meaning in the stride-to-stride fluctuations of human walking. Hum.
Mov. Sci. 2007, 26, 555–589. [CrossRef]
Hausdorff, J.M. Gait dynamics in Parkinson’s disease: Common and distinct behavior among stride length, gait variability, andfractal-like scaling. Chaos 2009, 19, 026113. [CrossRef] [PubMed]

Sensors 2023, 23, 9263

32.

33.

34.

35.
36.

37.

38.
39.
40.
41.
42.
43.

44.

45.
46.
47.
48.

49.
50.
51.
52.
53.
54.
55.
56.

14 of 15

Aoyagi, Y.; Yamada, S.; Ueda, S.; Iseki, C.; Kondo, T.; Mori, K.; Kobayashi, Y.; Fukami, T.; Hoshimaru, M.; Ishikawa, M.; et al.
Development of Smartphone Application for Markerless Three-Dimensional Motion Capture Based on Deep Learning Model.
Sensors 2022, 22, 5282. [CrossRef] [PubMed]
Yamada, S.; Aoyagi, Y.; Iseki, C.; Kondo, T.; Kobayashi, Y.; Ueda, S.; Mori, K.; Fukami, T.; Tanikawa, M.; Mase, M.; et al.
Quantitative Gait Feature Assessment on Two-Dimensional Body Axis Projection Planes Converted from Three-Dimensional
Coordinates Estimated with a Deep Learning Smartphone App. Sensors 2023, 23, 617. [CrossRef] [PubMed]
Iseki, C.; Hayasaka, T.; Yanagawa, H.; Komoriya, Y.; Kondo, T.; Hoshi, M.; Fukami, T.; Kobayashi, Y.; Ueda, S.; Kawamae, K.; et al.
Artificial Intelligence Distinguishes Pathological Gait: The Analysis of Markerless Motion Capture Gait Data Acquired by an iOS
Application (TDPT-GT). Sensors 2023, 23, 6217. [CrossRef] [PubMed]
Clark, R.A.; Pua, Y.H.; Bryant, A.L.; Hunt, M.A. Validity of the Microsoft Kinect for providing lateral trunk lean feedback during
gait retraining. Gait Posture 2013, 38, 1064–1066. [CrossRef] [PubMed]
Macpherson, T.W.; Taylor, J.; McBain, T.; Weston, M.; Spears, I.R. Real-time measurement of pelvis and trunk kinematics during
treadmill locomotion using a low-cost depth-sensing camera: A concurrent validity study. J. Biomech. 2016, 49, 474–478. [CrossRef][PubMed]Merchant, R.A.; Banerji, S.; Singh, G.; Chew, E.; Poh, C.L.; Tapawan, S.C.; Guo, Y.R.; Pang, Y.W.; Sharma, M.; Kambadur, R.; et al.
Is Trunk Posture in Walking a Better Marker than Gait Speed in Predicting Decline in Function and Subsequent Frailty? J. Am.
Med. Dir. Assoc. 2016, 17, 65–70. [CrossRef] [PubMed]
Jehu, D.; Nantel, J. Fallers with Parkinson’s disease exhibit restrictive trunk control during walking. Gait Posture 2018, 65, 246–250.
[CrossRef]
Clark, R.A.; Mentiplay, B.F.; Pua, Y.-H.; Bower, K.J. Reliability and validity of the Wii Balance Board for assessment of standingbalance: A systematic review. Gait Posture 2018, 61, 40–54. [CrossRef]
Jeon, E.T.; Cho, H.Y. A Novel Method for Gait Analysis on Center of Pressure Excursion Based on a Pressure-Sensitive Mat. Int. J.
Environ. Res. Public Health 2020, 17, 7845. [CrossRef]
Granacher, U.; Gollhofer, A.; Hortobágyi, T.; Kressig, R.W.; Muehlbauer, T. The Importance of Trunk Muscle Strength for Balance,
Functional Performance, and Fall Prevention in Seniors: A Systematic Review. Sports Med. 2013, 43, 627–641. [CrossRef] [PubMed]
Ueda, N.; Hakii, Y.; Koyano, S.; Higashiyama, Y.; Joki, H.; Baba, Y.; Suzuki, Y.; Kuroiwa, Y.; Tanaka, F. Quantitative analysis ofupper-limb ataxia in patients with spinocerebellar degeneration. J. Neurol. 2014, 261, 1381–1386. [CrossRef]
Coghe, G.; Corona, F.; Pilloni, G.; Porta, M.; Frau, J.; Lorefice, L.; Fenu, G.; Cocco, E.; Pau, M. Is There Any Relationship between
Upper and Lower Limb Impairments in People with Multiple Sclerosis? A Kinematic Quantitative Analysis. Mult. Scler. Int. 2019,2019, 9149201. [CrossRef] [PubMed]
Heremans, E.; Broeder, S.; Nieuwboer, A.; Bekkers, E.M.; Ginis, P.; Janssens, L.; Nackaerts, E. When motor control gets out of
hand: Speeding up triggers freezing in the upper limb in Parkinson’s disease. Parkinsonism Relat. Disord. 2019, 64, 163–168.
[CrossRef] [PubMed]
Bonnefoy-Mazure, A.; Sagawa, Y., Jr.; Lascombes, P.; De Coulon, G.; Armand, S. A descriptive analysis of the upper limb patterns
during gait in individuals with cerebral palsy. Res. Dev. Disabil. 2014, 35, 2756–2765. [CrossRef] [PubMed]
Elsworth-Edelsten, C.; Bonnefoy-Mazure, A.; Laidet, M.; Armand, S.; Assal, F.; Lalive, P.; Allali, G. Upper limb movement analysis
during gait in multiple sclerosis patients. Hum. Mov. Sci. 2017, 54, 248–252. [CrossRef] [PubMed]
Marmarou, A.; Bergsneider, M.; Relkin, N.; Klinge, P.; Black, P.M. Development of guidelines for idiopathic normal-pressurehydrocephalus: Introduction. Neurosurgery 2005, 57, S1–S3. [CrossRef] [PubMed]
Stolze, H.; Kuhtz-Buschbeck, J.P.; Drucke, H.; Johnk, K.; Diercks, C.; Palmie, S.; Mehdorn, H.M.; Illert, M.; Deuschl, G. Gait
analysis in idiopathic normal pressure hydrocephalus-which parameters respond to the CSF tap test? Clin. Neurophysiol. 2000,
111, 1678–1686. [CrossRef] [PubMed]
Postuma, R.B.; Berg, D.; Stern, M.; Poewe, W.; Olanow, C.W.; Oertel, W.; Obeso, J.; Marek, K.; Litvan, I.; Lang, A.E.; et al. MDS
clinical diagnostic criteria for Parkinson’s disease. Mov. Disord. 2015, 30, 1591–1601. [CrossRef]
Peng, C.K.; Buldyrev, S.V.; Havlin, S.; Simons, M.; Stanley, H.E.; Goldberger, A.L. Mosaic organization of DNA nucleotides. Phys.
Rev. E 1994, 49, 1685–1689. [CrossRef]
Hausdorff, J.M.; Peng, C.K.; Ladin, Z.; Wei, J.Y.; Goldberger, A.L. Is walking a random walk? Evidence for long-range correlations
in stride interval of human gait. J. Appl. Physiol. 1995, 78, 349–358. [CrossRef] [PubMed]
Goldberger, A.L.; Amaral, L.A.; Hausdorff, J.M.; Ivanov, P.C.; Peng, C.K.; Stanley, H.E. Fractal dynamics in physiology: Alterations
with disease and aging. Proc. Natl. Acad. Sci. USA 2002, 99 (Suppl. S1), 2466–2472. [CrossRef] [PubMed]
McIntosh, G.C.; Brown, S.H.; Rice, R.R.; Thaut, M.H. Rhythmic auditory-motor facilitation of gait patterns in patients with
Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 1997, 62, 22–26. [CrossRef] [PubMed]
Grosu, G.F.; Hopp, A.V.; Moca, V.V.; Barzan, H.; Ciuparu, A.; Ercsey-Ravasz, M.; Winkel, M.; Linde, H.; Muresan, R.C. The fractal
brain: Scale-invariance in structure and dynamics. Cereb. Cortex 2023, 33, 4574–4605. [CrossRef] [PubMed]
O’Donnell, L.J.; Westin, C.F. An introduction to diffusion tensor image analysis. Neurosurg. Clin. N. Am. 2011, 22, 185–196.
[CrossRef] [PubMed]
Nunez, P.L.; Srinivasan, R. Electric Fields of the Brain: The Neurophysics of EEG, 2nd ed.; Oxford University Press: Oxford, UK, 2006.
[CrossRef]

Sensors 2023, 23, 9263

57.
58.
59.
60.

61.
62.
63.
64.
65.
66.
67.

15 of 15

Uhlhaas, P.J.; Pipa, G.; Lima, B.; Melloni, L.; Neuenschwander, S.; Nikolic, D.; Singer, W. Neural synchrony in cortical networks:
History, concept and current status. Front. Integr. Neurosci. 2009, 3, 17. [CrossRef] [PubMed]
Pirici, D.; Mogoantă, L.; Mărgăritescu, O.; Pirici, I.; Tudorică, V.; Coconu, M. Fractal analysis of astrocytes in stroke and dementia.
Rom. J. Morphol. Embryol. 2009, 50, 381–390.
Gallagher, R.M.; Marquez, J.; Osmotherly, P. Cognitive and upper limb symptom changes from a tap test in Idiopathic Normal
Pressure Hydrocephalus. Clin. Neurol. Neurosurg. 2018, 174, 92–96. [CrossRef]
Sirkka, J.; Parviainen, M.; Jyrkkanen, H.K.; Koivisto, A.M.; Saisanen, L.; Rauramaa, T.; Leinonen, V.; Danner, N. Upper limb
dysfunction and activities in daily living in idiopathic normal pressure hydrocephalus. Acta Neurochir. 2021, 163, 2675–2683.
[CrossRef]
Takakusaki, K. Functional Neuroanatomy for Posture and Gait Control. J. Mov. Disord. 2017, 10, 1–17. [CrossRef]
Mikami, K.; Shiraishi, M.; Kamo, T. Subjective Vertical Position Allows Prediction of Postural Deterioration in Patients with
Parkinson’s Disease. Parkinson’s Dis. 2019, 2019, 1875435. [CrossRef]
Kaminishi, K.; Chiba, R.; Takakusaki, K.; Ota, J. Increase in muscle tone promotes the use of ankle strategies during perturbed
stance. Gait Posture 2021, 90, 67–72. [CrossRef]
Pickering, R.M.; Grimbergen, Y.A.; Rigney, U.; Ashburn, A.; Mazibrada, G.; Wood, B.; Gray, P.; Kerr, G.; Bloem, B.R. A metaanalysis of six prospective studies of falling in Parkinson’s disease. Mov. Disord. 2007, 22, 1892–1900. [CrossRef]
Kwon, K.Y.; Lee, M.; Ju, H.; Im, K. Risk Factors for Falls in Patients with de novo Parkinson’s Disease: A Focus on Motor andNon-Motor Symptoms. J. Mov. Disord. 2020, 13, 142–145. [CrossRef]
Thomas, J.C.; Odonkor, C.; Griffith, L.; Holt, N.; Percac-Lima, S.; Leveille, S.; Ni, P.; Latham, N.K.; Jette, A.M.; Bean, J.F.
Reconceptualizing balance: Attributes associated with balance performance. Exp. Gerontol. 2014, 57, 218–223. [CrossRef]
Weiss, A.; Brozgol, M.; Dorfman, M.; Herman, T.; Shema, S.; Giladi, N.; Hausdorff, J.M. Does the evaluation of gait quality during
daily life provide insight into fall risk? A novel approach using 3-day accelerometer recordings. Neurorehabilit. Neural Repair 2013,27, 742–752. [CrossRef] [PubMed]

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury topeople or property resulting from any ideas, methods, instructions or products referred to in the content.