ORIGINAL RESEARCH
published: 04 May 2022
doi: 10.3389/fphys.2022.726677

Foot Trajectory Features in Gait ofParkinson’s Disease Patients
Taiki Ogata 1*, Hironori Hashiguchi 2, Koyu Hori 2, Yuki Hirobe 1, Yumi Ono 1, Hiroyuki Sawada 3,
Akira Inaba 3, Satoshi Orimo 3 and Yoshihiro Miyake 1
1

Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan, 2Department of Computational Intelligenceand System Science, Tokyo Institute of Technology, Yokohama, Japan, 3Department of Neurology, Kanto Central Hospital,
Tokyo, Japan

Edited by:
Ahsan H. Khandoker,
Khalifa University, United ArabEmirates
Reviewed by:
Guanghao Sun,
The University of ElectroCommunications, Japan
Sridhar Poosapadi Arjunan,
SRM Institute of Science andTechnology, India
Hanatsu Nagano,
Victoria University, Australia*Correspondence:
Taiki Ogata
ogata@c.titech.ac.jp

Parkinson’s disease (PD) is a progressive neurological disorder characterized by movement
disorders, such as gait instability. This study investigated whether certain spatial features offoot trajectory are characteristic of patients with PD. The foot trajectory of patients with mild
and advanced PD in on-state and healthy older and young individuals was estimated fromacceleration and angular velocity measured by inertial measurement units placed on thesubject’s shanks, just above the ankles. We selected six spatial variables in the foot trajectory:
forward and vertical displacements from heel strike to toe-off, maximum clearance, andchange in supporting leg (F1 to F3 and V1 to V3, respectively). Healthy young individuals had
the greatest F2 and F3 values, followed by healthy older individuals, and then mild PD patients.
Conversely, the vertical displacements of mild PD patients were larger than the healthy older
individuals. Still, those of healthy older individuals were smaller than the healthy young
individuals except for V3. All six displacements of the advanced PD patients were smaller
than the mild PD patients. To investigate features in foot trajectories in detail, a principal
components analysis and soft-margin kernel support vector machine was used in machinelearning. The accuracy in distinguishing between mild PD patients and healthy older individuals
and between mild and advanced PD patients was 96.3 and 84.2%, respectively. The verticaland forward displacements in the foot trajectory was the main contributor. These results revealthat large vertical displacements and small forward ones characterize mild and advanced PD
patients, respectively.

Specialty section:
This article was submitted toComputational Physiology andMedicine,a section of the journal
Frontiers in Physiology

Keywords: foot trajectory, gait, inertial measurement unit, Parkinson’s disease, forward displacement, verticaldisplacement

Received: 17 June 2021
Accepted: 05 April 2022
Published: 04 May 2022

Parkinson’s disease (PD) is a complex neurological disorder in which the dopaminergic neurons of
the substantia nigra gradually degenerate (Post et al., 2007; Jankovic 2008; Haji Ghassemi et al.,2018). Bradykinesia, akinesia, muscular rigidity, and resting tremor are typical movement-related
symptoms of PD (Fahn 2008). These symptoms affect gait instability in PD patients, which is known
to progress with severity (Jankovic et al., 2000; Hausdorff 2009; Hass et al., 2012). The gait instability
decreases patients’ independence and quality of life. Thus, it is necessary to understand thecharacteristics of PD patients’ gait for the proper evaluation and treatment of gait alternation(Mirelman et al., 2019).
Spatiotemporal gait features measured in subjects through motion measurement technology, suchas pressure sensors, optical motion capture, and wearable sensors, have been successfully used to

Citation:
Ogata T, Hashiguchi H, Hori K,
Hirobe Y, Ono Y, Sawada H, Inaba A,
Orimo S and Miyake Y (2022) Foot
Trajectory Features in Gait ofParkinson’s Disease Patients.
Front. Physiol. 13:726677.
doi: 10.3389/fphys.2022.726677

1 INTRODUCTION

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PD Patient’s Foot Trajectory

evaluate the gait of PD patients (Mirelman et al., 2019). Wearable
sensors can be used for diagnosis and monitoring in both clinicaland daily living settings, and are useful because of their relatively
low cost and usability, as compared to non-wearable solutions,such as optical motion capture systems, and force platforms,
which are the current gold standard for motion analysis (deOliveira Gondim et al., 2020). In particular, inertial measurement
units (IMUs), which consist of accelerometers, gyroscopes, andmagnetometers, can be used to determine spatiotemporal andfrequency features of the gait, such as stride and step-lengths, aswell as swing/stance/stride duration, from acceleration and/or
angular velocity (Chang et al., 2016; Del Din et al., 2016; MicóAmigo et al., 2019).
These measurement technologies have revealed thatspatial–temporal gait parameters are altered in PD patients ascompared to healthy individuals. Gait velocity becomes slower
and stride/step-length is shorter than in age-matched healthy
controls, and these symptoms worsen with disease progression(Alban et al., 2014; Galna et al., 2015; Pistacchi et al., 2017;
Mirelman et al., 2019). PD patients compensate for this short
stride/step-length by increasing cadence which can cause
suffering gait (Morris et al., 1994a; Morris et al. 1994b; Morris
et al. 1996). The rhythmic pattern of gait in PD patients alsochanges with progression (Hass et al., 2012; Djurić-Jovičić et al.,2017; Micó-Amigo et al., 2019). For instance, the inter-limb
asymmetry in gait, such as step duration, is well-known in theearly stages of PD (Djurić-Jovičić et al., 2017). In patients withmild PD, the ratio of the swing/stance duration is increased anddecreased, respectively (Hass et al., 2012). Variability in gaitparameters, such as step duration, is larger in PD patients
than in age-matched older individuals (Rochester et al., 2014).
Compared to the temporal and frequency features, little is
known about the spatial features of PD patients’ gait, such as foottrajectory. This may be because evaluation of the foot trajectory
requires detailed measurements of spatial features for opticalmotion capture, which involves high cost and places a heavy loadon the participants. Recently, developments in the analysis of
IMU data have improved the accuracy of the estimation of thefoot trajectory during gait (Bamberg et al., 2008; Mariani et al.,2012; Kitagawa and Ogihara 2016; Kluge et al., 2017; Tunca et al.,2017; Hori et al., 2020; Mao et al., 2021). For instance, Hori et al.
(2020) accurately estimated stride-by-stride foot trajectories
using IMUs attached to the left and right shanks, just above
the ankles. The stride-lengths estimated using this method andthose from an optical motion capture system were correlated,
obtaining a coefﬁcient of determination of approximately 0.98.
The state-of-the-art technology involved in wearable IMUs
could reveal more detailed spatial features of the gait of PD
patients. This study investigated foot trajectory features in PD
patients and their differences from healthy older and young
individuals. The temporal and frequency gait features ofpatients with mild PD are not markedly different from thoseof healthy older individuals, compared to the difference between
advanced PD patients and healthy older individuals (Klucken
et al., 2013; Rovini et al., 2017). Thus, investigating gait trajectoryfeatures in patients would be useful for understanding the gait ofmild PD patients. We recruited healthy young individuals to

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separate the effects of aging and PD on the foot trajectory as older
people are susceptible to developing PD. To investigate the effectsof the severity of PD on foot trajectory, we also recruitedadvanced PD patients. For these purposes, we measured thefoot trajectory of subjects using IMUs attached to both shanks.
In addition, to determine whether the foot trajectory captures thegait characteristics of PD patients, we used principal component
analysis and distinguished PD patients from healthy controls
using machine learning technology.

2 METHODS
2.1 Participants
Thirty mild PD patients (modiﬁed Hoehn and Yahr [H&Y] scalestages 1–2; age, 68.4 ± 10.3 years; 16 women and 14 men), 24
healthy older individuals (age, 70.7 ± 3.9 years; 12 women and 12men), 34 healthy young individuals (age, 24.6 ± 3.5 years; 10
women and 24 men), and 27 advanced PD patients (modiﬁed
H&Y scale at stages 2.5–4; age, 77.3 ± 5.3 years; 15 women and 12men) participated in this study. All PD patients were being
treated with anti-Parkinsonian drugs at the time of the study.
During the experiment, patients were in the on-state, in which
they responded positively to the medication. We excluded
patients with PD who exhibited freezing of gait and those who
needed walking assistance during the experiment. None of theparticipants suffered from any musculoskeletal or neurologicalpathologies other than PD. The study was conducted inaccordance with the Declaration of Helsinki and was approvedby the Research Ethics Review Committee of the Tokyo Instituteof Technology and the Ethics Committee of Kanto Central
Hospital. Written informed consent was obtained from allparticipants.

2.2 Experiment Task
We asked each participant to walk back and forth in a straightcorridor, giving over 60 strides (Figure 1A). The participants
walked alone without any type of walking support. The initial andﬁnal ﬁve strides and those corresponding to direction change
were excluded from the data analysis. We used 40 strides in total(20 strides on the left and 20 on the right).

2.3 Foot Trajectory Estimation
The acceleration and angular velocity of the subjects’ shanks forfoot trajectory estimation were measured using IMUs
(TSDN121 ATR-Promotions, Seika, Japan) (Figure 1B). The
ranges of the triaxial accelerometer and gyroscope were ±8 G
and ±1,000 dps, respectively, and the sampling frequency was
100 Hz. The IMU dimensions were 37 × 46 × 12 mm and thedevice weighed 22 g. This sensor was attached to the shank bymeans of a band, just above the ankle (Figures 1B, C).
We used our previous method for foot trajectory estimation(Hori et al., 2020), which was conducted stride-by-stride, toreduce accumulated error. Speciﬁcally, the time series ofaccelerations and angular velocities were divided at the time
when the shank movement was the steadiest, corresponding to
the lowest pitch velocity of the shank. This timing was set as2

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FIGURE 1 | (A) The experiment task. Participants walked and turned in a straight corridor, giving over 60 strides. (B) The IMU and a belt to attach the IMU on theshank. (C) The belt with the IMU around the shank.

FIGURE 2 | Foot trajectory and six spatial features of the foot trajectory: forward and vertical displacements from the heel strike to toe-off (F1 and F2, respectively),
maximum clearance (F2 and V2, respectively), and change of supporting leg (F3 and V3, respectively). The vertical and horizontal axes are the vertical and forward
displacements, respectively.

the initial timing of each stride during the heel strike. The heel
strike was measured using angular velocity in the sagittal plane
because this value changes on the spike with a heel strike. The
timing of the spike was set the timing of the heel strike. The
acceleration vector was transformed from the sensor
coordinate system to a world coordinate system based on
the assumption that the accelerometer only detects gravity
at mid-stance in the gait cycle because the shank where the
IMU was attached hardly move at this time. The initial angularvelocity was estimated using the gravity vector of theaccelerometer. The foot trajectory was then calculated as
the double integral of the acceleration vector. The effect ofdrift due to IMU error, we performed forward and backward
integration of the foot acceleration and used weighted averages
as foot velocity, assuming that the initial, and ﬁnal velocities in

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the three directions and the position along the verticaldirection were zero.

2.4 Statistical Method
2.4.1 Features in Foot Trajectory
For the foot trajectory features, we used three forward and threevertical displacements, as shown in Figure 2: forward and verticaldisplacements from the onset of the stride (heel strike) to toe-off(F1 and V1, respectively), the timing when the foot reached thehighest point (maximum clearance) (F2 and V2, respectively),
and the timing when the supporting leg changed (F3 and V3,
respectively).
For the statistical test, we used a one-way ANOVA. All post
hoc tests were conducted using the Shaffer Method. The
signiﬁcance level was set at 0.05 for all tests. In addition, we

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FIGURE 3 | Averages of the forward and vertical displacements. (A) F1, (B) F2, (C) F3, (D) V1, (E) V2, and (F) V3. mPD, HO, HY, and aPD represent mild PD
patients, healthy older individuals, healthy young individuals, and advanced PD patients, respectively. The error bars indicate the standard deviations between theparticipants. *, **, and *** represent p < 0.05, p < 0.01, and p < 0.001, respectively.

conducted principal component analysis to investigate thetrajectory features in detail. We determined the number of aprincipal components using a parallel analysis. These analyses
were conducted using R ver. 3.2.4.

The participant data were included in both the training and test
datasets. Finally, accuracy was deﬁned as the average accuracy for
10-fold cross-validation. For SVM analysis and cross-validation,
we used MATLAB statistics and a machine learning toolbox(MathWorks, Inc., Natick, MA, United States).

2.4.2 Distinguishing Method
For distinguishing the PD and healthy groups, we used asupervised learning algorithm: the soft-margin kernel support
vector machine (SVM). Some studies showed better classiﬁcation
accuracy between PD patients and healthy older individuals and/or between patients with different PD severities using the SVM
than when using other classiﬁcation methods (Klucken et al.,2013; Caramia et al., 2018). We conducted binary distinguishing
between mild PD patients and healthy older individuals, mild,
and advanced PD patients, mild PD patients and healthy young
individuals, and healthy older and healthy young individuals.
Because PD is a progressive disorder, distinguishing between
patients with advanced PD and healthy individuals was notconsidered important. We used 10-fold cross-validation. First,
the dataset was divided into 10 balanced groups. Then, one group
was used for the test and the rest for training. Testing and training
were repeated until all the groups were considered for validation.

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3 RESULTS
3.1 Averages of the Forward and Vertical
Displacements
Figure 3 shows averages of the forward and verticaldisplacements for each group. In the forward displacements(Figures 3A–C), the average for the healthy young individuals
seems to be the largest, followed in order by the healthy older
individuals, mild PD patients, and advanced PD patients. A oneway ANOVA showed signiﬁcant differences for all of the forward
displacements (p < 0.001 in all).
Multiple comparisons testing showed that there was no
signiﬁcant difference for F1 between the mild PD patients andhealthy older individuals (p = 0.129) F1 of mild PD patients was
signiﬁcantly smaller than the healthy young individuals (p = 0.003).

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PD Patient’s Foot Trajectorymild PD patients (p = 0.015). There was no signiﬁcant difference
for V3 of advanced PD patients from the healthy older individuals
and healthy young individuals (p = 0.137 and p = 0.426,
respectively).

TABLE 1 | Standard deviation and variance proportion of principal components
for PCA feature extraction.
Principal component

1

2

3

4

5

6

Standard deviation
Variance proportion
Cumulative proportion

1.690
0.476
0.476

1.375
0.315
0.791

0.853
0.121
0.912

0.633
0.067
0.979

0.316
0.016
0.996

0.162
0.004
1.000

3.2 Principal Component Analysis
The averages in the forward and vertical displacements showedsimilar tendencies, respectively. To investigate this tendency, we
conducted the principal component analysis. Table 1 lists thestandard deviations, variance proportions, and their accumulated
value for the principal components. The cumulative proportion is
the total variation in the features explained up to a particularprincipal component. The parallel analysis suggested two
principal components. Figure 4 shows the factor loadings for theﬁrst and second principal components, corresponding to thecorrelation between the components and variables. The ﬁrst
principal component showed a high correlation with the variables
along the forward displacement, F1–F3 (Figure 4A). Larger variablevalues induced positively larger loadings of the ﬁrst principal
component. The second principal component showed a highcorrelation with the variables along the vertical displacement,
V1–V3 (Figure 4B). Larger variable values induced positively
larger loadings of this component. Therefore, the ﬁrst and second
components mainly represent the displacements in the foottrajectory along the forward and vertical displacements, respectively.
Figure 5 shows the scatter plot of the ﬁrst and second principal
components from the subjects in this study. Although there weresigniﬁcant differences in F2 and F3 between healthy older
individuals and mild PD patients, the ﬁrst principal component,
which mainly related to forward displacements, tended to be similar.
The second principal component, mainly related to verticaldisplacements, of the mild PD patients seems smaller thanhealthy older individuals. Comparing healthy older and young
individuals, both components tended to be smaller in older
individuals than in young individuals. In contrast, the ﬁrst
principal component in mild PD patients also tended to besmaller than in young individuals, but their second principal
component tended to be larger. The ﬁrst principal component ofmany advanced PD patients was smaller than patients with mild PD.

In contrast, there was no signiﬁcant difference in F1 between healthyolder and young individuals (p = 0.200). The F1 of the advanced PD
patients was smaller than the mild PD patients, healthy older
individuals, and healthy young individuals (all p < 0.001).
However, the results of a multiple comparisons test showed
there were signiﬁcant differences for F2 between all groups. F2 ofmild PD patients was smaller than the healthy older individuals(p = 0.048). Furthermore, F2 of mild PD patients and healthyolder individuals were smaller than the healthy young individuals(p < 0.001 and p = 0.006, respectively). F2 of the advanced PD
patients was smaller than the other three groups (all p < 0.001).
Multiple comparisons also showed signiﬁcant differences forF3 between all groups. F3 of mild PD patients was smaller thanhealthy older individuals (p < 0.001). In addition, F3 of mild PD
patients and healthy older individuals was smaller than healthy
young individuals (p < 0.001 and p = 0.019, respectively). F3 ofthe advanced PD patients was smaller than the other groups (allp < 0.001).
In the vertical displacements (Figures 3D–F), the average ofthe mild PD patients was the largest and that of the healthy older
individuals seems to be the smallest. For all of the verticaldisplacements (V1–V3), a one-way ANOVA revealed
signiﬁcant differences (p < 0.001 in all).
Multiple comparisons showed that V1 of the mild PD patientswas signiﬁcantly larger than the healthy older individuals (p <0.001). V1 of the mild PD patients was also signiﬁcantly larger
than the healthy young individuals (p = 0.001). Conversely, theV1 of healthy older individuals was signiﬁcantly smaller thanhealthy young individuals (p = 0.001). The advanced PD patientswere signiﬁcantly smaller for V1 than mild PD patients (p =0.008). In addition, V1 of the advanced PD patients was
signiﬁcantly smaller than the healthy older individuals (p =0.004) but not healthy young individuals (p = 0.877).
The multiple comparison revealed that V2 of mild PD patientswas signiﬁcantly larger than the healthy older individuals (p <0.001). There was no signiﬁcant difference for V2 between mild
PD patients and healthy young individuals (p = 0.070). In
contrast, V2 of healthy older individuals was signiﬁcantly
smaller than healthy young individuals (p < 0.001). V2 of theadvanced PD patients was signiﬁcantly smaller than mild PD
patients and healthy older individuals (p < 0.001 and p = 0.003,
respectively) but not than healthy young individuals (p = 0.070).
V3 of mild PD patients was signiﬁcantly larger than thehealthy older individuals (p < 0.001). V3 of the mild PD
patients was also signiﬁcantly larger than young individuals(p < 0.001). Oppositely, there was no signiﬁcant difference
between healthy older and young individuals (p = 0.426). V3of the advanced PD patients was signiﬁcantly smaller than the

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3.3 Distinguishing of PD PatientsTo investigate the tendency shown in the principal component
analysis, we conducted a soft-margin kernel SVM to binary
distinguish each category of subjects, considering four pairs ofsubjects: mild PD patients and healthy older individuals, mild PD,
and advanced PD patients, mild PD patients and, healthy young
individuals, and healthy older and healthy young individuals. We
did not distinguish the advanced PD patients from healthy
individuals because differences in gait are more obvious thanbetween mild PD patients and healthy individuals (Klucken et al.,2013; Rovini et al., 2017).
Table 2 lists the accuracy of distinction using the six spatial
variables and the ﬁrst two principal components. The accuracyof distinguishing between mild PD patients and healthy older
individuals was the highest among all pairs, reaching 96.3%
when using either all the variables or the ﬁrst two principal
components. Although the PCA did not lead to better results5

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FIGURE 4 | Factor loadings of principal components from gait spatial features. Loading is the positive or negative correlation between a principal component and avariable. (A) Factor loadings of the ﬁrst principal component, which is strongly and positively correlated to variables in the forward displacement (F1 to F3) and hence
describes this displacement in the foot trajectory. (B) Factor loadings of the second principal component, which is strongly and positively correlated to variables in thevertical displacement (V1 to V3) and hence describes this displacement in the foot trajectory.

represents the vertical displacements (V1, V2, and V3). To
investigate whether all three vertical displacements or second
component contributed to the accuracy of distinguishing
between mild PD patients and healthy older individuals, we
distinguished them using only V1, V2, or V3 with the same
SVM. The accuracies of the vertical variables V1, V2, and V3
were 79.6, 83.3, and 74.1%, respectively.
The other accuracies varied between 77.6 and 92.7% for all thevariables, and between 82.8 and 89.1% for the ﬁrst two principal
components. Hence, the categories could be suitably distinguishedusing the ﬁrst two principal components, which represent theforward and vertical displacements, respectively. Mild PD patientsand healthy young individuals were distinguished by both of the ﬁrst
two principal components (Figure 6B). The ﬁrst principal
component of patients with mild PD was smaller, while thesecond principal component was larger, than that of healthy
young individuals. Hence, the foot trajectory of patients withmild PD exhibited shorter forward and larger verticaldisplacements than that of healthy young individuals. Healthyolder individuals and young individuals were also distinguishedby both the ﬁrst and second principal components (Figure 6C). In
this case, healthy older individuals exhibited smaller ﬁrst and second
principal components than healthy young individuals, suggestingthat the foot trajectory of the former category tends to be shorter inboth the forward and vertical directions than that of the latter
category by the effects of aging. Patients with mild or advanced PD
were mainly distinguished by the ﬁrst principal component, which
represents the forward displacements (F1, F2, and F3) (Figure 6D).
We investigated how accurately each category waspredicted using confusion matrices obtained using theprincipal component method, as shown in Table 3. Bothmild PD patients and healthy older individuals were
accurately predicted, with 96.7 and 95.8% accuracy,
respectively. In contrast, advanced PD patients were poorly
distinguished from mild PD patients, with an accuracy
reaching only 67.7%.

FIGURE 5 | Scatter plot of the ﬁrst two principal components extracted
from subjects’ spatial gait features. The ﬁrst and second component
magnitudes represent the displacements of the foot trajectories in the forward
and vertical directions, respectively. Healthy young individuals tended tohave a larger ﬁrst principal component than other subjects. The ﬁrst principal
component of mild PD patients and healthy older individuals is larger than thatof advanced PD patients. The second principal component of healthy older
individuals tends to be smaller than that of the other subjects.

over using all variables, the characteristics of thedisplacements in the trajectory were well contracted by the
PCA. Figure 6 shows the boundaries between the groups
obtained by using the ﬁrst two principal components, andFigure 7 shows the ROC curves and AUCs. The boundary
curve between patients with mild PD and healthy older
individuals (Figure 6A) is virtually horizontal. Hence, mild
PD patients and healthy older individuals were mainly
separated by the second principal component, which

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TABLE 2 | Distinguishing accuracy (in percentages).
Pair of subjects

Features

Mild PD patients–Healthy older individuals
Mild PD patients–Healthy young individuals
Healthy older individuals—Healthy young individuals
Mild PD–Advanced PD patients

All variables

First two principal
components

96.3
92.7
77.6
82.5

96.3
89.1
82.8
84.2

FIGURE 6 | Scatter plot for pairs of individual categories and boundaries using the ﬁrst two principal components. (A) Mild PD patients and healthy older individuals.
They are mainly distinguished by the second principal component, which represents the vertical displacements in the foot trajectory. (B) Mild PD patients and healthy
young individuals. They are distinguished by both components. The foot trajectory of the mild PD patients tended to be larger in the vertical direction and smaller in theforward direction than in healthy young individuals. (C) Healthy older individuals and young individuals. They are also distinguished by both components. The foottrajectory of the healthy older individuals tended to be shorter in both directions than in healthy young individuals. (D) Mild and advanced PD patients. They are mainly
distinguished by the ﬁrst principal component, which represents the forward displacements. Each circle represents one subject, and the lines represent the boundaries
between the two categories.

4 DISCUSSION

very different (Klucken et al., 2013; Rovini et al., 2017). Secondly,
to differentiate between the effects of aging and PD on foottrajectory, we investigated the foot-trajectory difference between
mild PD patients as well as healthy older individuals and healthy
young individuals. Finally, to investigate the effect of the severityof PD on foot trajectory, we compared the foot trajectoriesof mild and advanced PD patients. We used six spatial

This study aimed to investigate the characteristics of PD patients’foot trajectories during gait, which was estimated using wearable
sensor data. First, we investigated the difference in foot trajectory
between mild PD patients and healthy older individuals because
the temporal and frequency gait features of these groups are not

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FIGURE 7 | ROC curves and AUCs. (A) Mild PD patients and healthy older individuals. (B) Mild PD patients and healthy young individuals. (C) Healthy older
individuals and young individuals. (D) Mild and advanced PD patients.

features of the foot trajectory: the forward and verticaldisplacements from heel strike to toe-off, maximum clearance,and change of supporting leg.

Although there was no signiﬁcant difference for F1 between
the mild PD patients and healthy older individuals, F2 and F3 ofthe mild PD patients was signiﬁcantly smaller than the healthyolder individuals In contrast, the mild PD patients’ all verticaldisplacements (V1–V3) were signiﬁcantly larger than the healthyolder individuals. Thus, compared to the healthy older
individuals, the trajectory of the mild PD patients wascharacterized as a small forward displacement except for toeoff and large vertical displacement.
In addition, compared to the healthy young individuals, theforward displacement of the older individuals was smaller andthat of the mild PD patients even smaller. However, there wasno signiﬁcant difference for F1 between the healthy older andyoung individuals. Thus, the forward displacements of thehealthy the mild PD patients are considered small because ofboth aging and PD. On the other hand, the all verticaldisplacements of the mild PD patients were larger than thehealthy young individuals. This is in contrast to the smaller V1
and V2 of the healthy older individuals compared to the healthy
young individuals. Thus, the mild PD patients raised their footTABLE 3 | Confusion matrix for distinguishing subject categories using the ﬁrst
two principal components (in percentages).
Obtained

True

mPD
HO

mPD

HO

96.7
4.2

3.3
95.8

Obtained

True

mPD
HY

Obtained

True

HO
HY

HO

HY

71.0
8.8

29.0
91.2

mPD

HY

87.0
15.0

13.3
85.0

Obtained

True

mPD
aPD

mPD

aPD

100
33.3

0
67.7

Percentages along the diagonal and off-diagonal represent the correct and incorrect
prediction rates, respectively. mPD, HO, HY, and aPD represent mild PD patients, healthyolder individuals, healthy young individuals, and advanced PD patients, respectively.

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more than the decreasing of the vertical displacements due tothe effects of aging.
The contrast shown in the vertical displacements between themild PD patients and healthy older individuals could characterize
the gait of the mild PD patients more than the forward
displacements. This was supported by the results of theprincipal component analysis and distinguishing using the
SVM. The ﬁrst and second principal components obtained by
PCA were mainly related to the displacements in the forward andvertical directions, respectively. The accuracy for distinguishingmild PD patients from healthy older individuals using the ﬁrst
two components reached 96.3%. These two categories weremainly distinguished by the second component (Figure 6A),
which is related to the vertical displacements in the foottrajectory.
Regarding the distinction between healthy older and healthy
young individuals, the vertical displacements of older individuals
tended to be shorter than that of the young individuals(Figure 6D). Nagano et al. found that max clearance of toe of
healthy older individuals was lower than that of healthy young
individuals (Nagano et al., 2011). Our results showed that healthyolder individuals raise their toe and lower shank lower thanhealthy older individuals. The vertical displacements of patientswith mild PD was greater than that in healthy young individuals(Figure 6C). These results suggested that foot clearance inpatients with mild PD was larger than in healthy older andhealthy young individuals. Although foot clearance during gaitin PD patients at stages 1–3 of the H&Y scale has been reported tobe smaller than that in healthy young individuals (Alcock et al.,2016), foot clearance in patients with mild PD (i.e., those with
H&Y scale in stages 1 and 2) remains unclear. Foot clearance infaller older individuals is larger than that in non-faller older
individuals (Barrett et al., 2010). Thus, mild PD patients lift theirfeet considerably to avoid falling, which is a common concern forpatients with PD.
The accuracy in distinguishing mild PD patients and healthyolder individuals when using each vertical displacement (V1–V3)
was lower than when using all variables and when using the ﬁrst two
principal components. This ﬁnding suggests that the foot clearanceof patients with mild PD did not increase uniformly compared tohealthy older individuals. In fact, the factor loadings in the second
component were different among V1, V2, and V3 (see Figure 4B).
This reﬂects the gait trajectory feature in PD patients. The gait of PD
patients is less automatic and requires more cognitive resources. For
this reason, dual tasks impair PD patients, causing falls (Rochester
et al., 2014). In addition, prefrontal activation increases in PD
patients during imaginary gait tasks, which reﬂects the increasein cognitive demand for gait (Peterson et al., 2014; Maidan et al.,2016). Thus, our results reﬂect the less automatic and more
intentional gait of PD patients.
All forward and vertical displacements of advanced PD
patients were smaller than mild PD patients. This result
suggests that the whole foot trajectory of PD patients shank as
the disease progressed. However, the ﬁrst principal component
related to the forward displacements mainly contributes todistinguishing between mild and advanced PD patients(Figure 6D). In addition, in distinguishing between mild PD

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and advanced PD patients, the accuracy was higher when using
the ﬁrst two principal components than when using the six gaitfeatures (Table 2). Thus, the small forward displacements would
characterize the gait of advanced PD patients compared to mild
PD patients. However, the accuracy of distinguishing between themild and advanced PD patients was lower than over 10% thanbetween the mild PD patients and healthy older individuals.
Therefore, small forward displacements would not characterizeadvanced PD patients compared to large vertical displacements inmild PD patients.
Many studies have sought to distinguish PD patients fromhealthy subjects using gait features and machine learningtechnology (Barth et al., 2011; Sant’Anna et al., 2011; Klucken
et al., 2013; Cuzzolin et al., 2017; Micó-Amigo et al., 2017;
Caramia et al., 2018). As movement impairment in PD patients
increases with disease severity (Hausdorff 2009; Hass et al., 2012), aclassiﬁcation that includes advanced patients should be easier thanthat which includes only mild PD patients (Rovini et al., 2017).
Klucken et al. (2013) extracted 694 temporal and frequency featuresfrom measurements during gait and toe-tapping tasks through IMUs
attached to the heel and used linear discriminant analysis, adaptive
boosting, and linear/nonlinear SVMs as classiﬁers. They found thatthe classiﬁcation accuracy for mild and advanced PD patients andhealthy older individuals reached only 82% and that it was mostly
the mild PD patients that were misclassiﬁed. Therefore, temporaland frequency features may compromise the classiﬁcation between
mild PD patients and healthy older individuals when using few
IMUs and simple gait tasks. In the future, it should be investigatedwhether gait trajectory features contribute to the classiﬁcation ofpatients with PD.
This study’s limitations include that the advanced PD patientswere not age-matched to the mild PD patients or healthy older
individuals. The accuracy of distinguishing between mild andadvanced PD patients was lower than between mild PD patientsand healthy older individuals (Table 2), for reasons that are not clear.
As age affects spatial gait features, the accuracy of distinguishingpatients with different PD severity should be determined by age
matching in future studies. In addition, the PD patients who
participated in our experiment were in on-state. Thus, thetrajectory features of the PD patients could be affected by the
medication. In the future, the foot trajectory of PD patients who
have not yet been treated should be investigated. Furthermore, ourresults cannot be used for the assessment or diagnosis of PD. For
assessment or diagnosis usage, more data regarding foot trajectoryshould be collected for machine learning and validation and tests
should be conducted. In addition, multi-class classiﬁcation should be
used in the future. Furthermore, other pathologies can cause thelarger vertical trajectory in PD patients compared to healthy older
individuals. Thus, the foot-trajectory data of other pathologies
should be also collected and classiﬁed.
Overall, we found characteristics in foot trajectory during gaitin mild and advanced PD patients by employing IMU
measurements. The forward displacements of mild PD patientswere smaller than healthy older individuals, which were smaller
than healthy young individuals. The vertical displacements ofmild PD patients and healthy older individuals were larger andsmaller than healthy young individuals, respectively. These9

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Ogata et al.

PD Patient’s Foot Trajectory

ETHICS STATEMENT

results showed that mild PD patients decreased the forward
displacements in the foot trajectory through the effects ofaging and PD but increased the vertical displacements
regardless of decrease by aging. All forward and verticaldisplacements of advanced PD patients were smaller than themild PD patients. Thus, foot trajectory shrunk during the
progression of PD. The principal component analysis andSVM revealed that the components, which related to thevertical and forward displacements, mainly contributed to theaccuracy of distinguishing between mild PD patients and healthyolder individuals and between mild and advanced PD patients,
respectively. Therefore, the vertical and forward displacements infoot trajectory characterized gait of mild and advanced PD
patients, respectively.

The studies involving human participants were reviewed
and approved by the Research Ethics Review Committee of
the Tokyo Institute of Technology and the Ethics Committeeof Kanto Central Hospital. The patients/participants
provided their written informed consent to participate inthis study.

AUTHOR CONTRIBUTIONS
HH, KH, YO, SO, and YM designed and conducted theexperiments. TO, HH, KH, and YO analyzed the data. All theauthors discussed and interpreted the data. TO wrote themanuscript, and all authors reviewed the manuscript andapproved the ﬁnal version of the paper for publication.

DATA AVAILABILITY STATEMENT
The datasets presented in this article are not readily available
because the data set for this study is not publicly available because
there is an agreement for data exchange between the Tokyo
Institute of Technology and Kanto Central Hospital. Requests toaccess the datasets should be directed to TO, ogata@c.titech.ac.jp.

FUNDING
This work was supported by JST CREST under Grant Number
JPMJCR1433, Japan.
Del Din, S., Godfrey, A., Galna, B., Lord, S., and Rochester, L. (2016). Free-living
Gait Characteristics in Ageing and Parkinson’s Disease: Impact of Environment
and Ambulatory Bout Length. J. Neuroengineering Rehabil. 13, 46. doi:10.1186/
s12984-016-0154-5
Djurić-Jovičić, M., Belić, M., Stanković, I., Radovanović, S., and Kostić, V. S. (2017).
Selection of Gait Parameters for Differential Diagnostics of Patients with De
Novo Parkinson’s Disease. Neurol. Res. 39, 853–861. doi:10.1080/01616412.
2017.1348690
Fahn, S. (2008). “Clinical Aspects of Parkinson Disease,” in Parkinson’s Disease:
Molecular and Therapeutic Insights Form Model Systems. Editors R. Nass, and
S. Przedborski (Amsterdam: Elsevier), 3–48. doi:10.1016/b978-0-12-374028-1.
00001-4
Galna, B., Lord, S., Burn, D. J., and Rochester, L. (2015). Progression of Gait
Dysfunction in Incident Parkinson’s Disease: Impact of Medication andPhenotype. Mov Disord. 30, 359–367. doi:10.1002/mds.26110
Haji Ghassemi, N., Hannink, J., Martindale, C. F., Gaßner, H., Müller, M., Klucken,
J., et al. (2018). Segmentation of Gait Sequences in Sensor-Based Movement
Analysis: A Comparison of Methods in Parkinson’s Disease. Sensors (Basel) 18,
1–15. doi:10.3390/s18010145
Hass, C. J., Malczak, P., Nocera, J., Stegemöller, E. L., Shukala, A., Malaty, I., et al. (2012).
Quantitative Normative Gait Data in a Large Cohort of Ambulatory Persons with
Parkinson’s Disease. PLOS ONE 7, e42337–5. doi:10.1371/journal.pone.0042337
Hausdorff, J. M. (2009). Gait Dynamics in Parkinson’s Disease: Common andDistinct Behavior Among Stride Length, Gait Variability, and Fractal-like
Scaling. Chaos 19, 026113. doi:10.1063/1.3147408
Hori, K., Mao, Y., Ono, Y., Ora, H., Hirobe, Y., Sawada, H., et al. (2020). Inertial
Measurement Unit-Based Estimation of Foot Trajectory for Clinical Gait
Analysis. Front. Physiol. 10, 1530. doi:10.3389/fphys.2019.01530
Jankovic, J. (2008). Parkinson’s Disease: Clinical Features and Diagnosis. J. Neurol.
Neurosurg. Psychiatry 79, 368–376. doi:10.1136/jnnp.2007.131045
Jankovic, J., Rajput, A. H., McDermott, M. P., and Perl, D. P. (2000). The Evolutionof Diagnosis in Early Parkinson Disease. Arch. Neurol. 57, 369–372. doi:10.
1001/archneur.57.3.369
Kitagawa, N., and Ogihara, N. (2016). Estimation of Foot Trajectory during
Human Walking by a Wearable Inertial Measurement Unit Mounted to theFoot. Gait & Posture 45, 110–114. doi:10.1016/j.gaitpost.2016.01.014
Klucken, J., Barth, J., Kugler, P., Schlachetzki, J., Henze, T., Marxreiter, F., et al.
(2013). Unbiased and Mobile Gait Analysis Detects Motor Impairment in

REFERENCES
Albani, G., Cimolin, V., Fasano, A., Trotti, C., Galli, M., and Mauro, A. (2014). "Masters
and Servants" in Parkinsonian Gait: a Three-Dimensional Analysis of Biomechanical
Changes Sensitive to Disease Progression. Funct. Neurol. 29, 99–105.
Alcock, L., Galna, B., Lord, S., and Rochester, L. (2016). Characterisation of Foot
Clearance during Gait in People with Early Parkinson‫׳‬s Disease: Deﬁcits
Associated with a Dual Task. J. Biomech. 49, 2763–2769. doi:10.1016/j.
jbiomech.2016.06.007
Bamberg, S., Benbasat, A. Y., Scarborough, D. M., Krebs, D. E., and Paradiso, J. A.
(2008). Gait Analysis Using a Shoe-Integrated Wireless Sensor System. IEEE
Trans. Inform. Technol. Biomed. 12, 413–423. doi:10.1109/TITB.2007.899493
Barrett, R. S., Mills, P. M., and Begg, R. K. (2010). A Systematic Review of the Effectof Ageing and Falls History on Minimum Foot Clearance Characteristics during
Level Walking. Gait & Posture 32, 429–435. doi:10.1016/j.gaitpost.2010.07.010
Barth, J., Klucken, J., Kugler, P., Kammerer, T., Steidl, R., Winkler, J., et al. (2011).
“Biometric and mobile Gait Analysis for Early Diagnosis and Therapy
Monitoring in Parkinson’s Disease,” in 2011 Annual International
Conference of the IEEE Engineering in Medicine and Biology Society,
Boston, MA, USA, 30 Aug.-3 Sept. 2011 (Piscataway, New Jersey, US:IEEE), 868–871. doi:10.1109/IEMBS.2011.6090226
Caramia, C., Torricelli, D., Schmid, M., Muñoz-Gonzalez, A., Gonzalez-Vargas, J.,
Grandas, F., et al. (2018). IMU-Based Classiﬁcation of Parkinson’s Disease from
Gait: A Sensitivity Analysis on Sensor Location and Feature Selection. IEEE
J. Biomed. Health Inform. 22, 1765–1774. doi:10.1109/JBHI.2018.2865218
Chang, H.-C., Hsu, Y.-L., Yang, S.-C., Lin, J.-C., and Wu, Z.-H. (2016). A Wearable
Inertial Measurement System with Complementary Filter for Gait Analysis ofPatients with Stroke or Parkinson’s Disease. IEEE Access 4, 8442–8453. doi:10.
1109/ACCESS.2016.2633304
Cuzzolin, F., Sapienza, M., Esser, P., Saha, S., Franssen, M. M., Collett, J., et al.
(2017). Metric Learning for Parkinsonian Identiﬁcation from IMU Gait
Measurements. Gait & Posture 54, 127–132. doi:10.1016/j.gaitpost.2017.02.012
de Oliveira Gondim, I. T. G., de Souza, C. d. C. B., Rodrigues, M. A. B., Azevedo, I.
M., de Sales Coriolano, M. d. G. W., and Lins, O. G. (2020). Portable
Accelerometers for the Evaluation of Spatio-Temporal Gait Parameters in
People with Parkinson’s Disease: an Integrative Review. Arch. Gerontol.
Geriatr. 90, 104097. doi:10.1016/j.archger.2020.104097

Frontiers in Physiology | www.frontiersin.org

10

May 2022 | Volume 13 | Article 726677

Ogata et al.

PD Patient’s Foot TrajectoryPeterson, D. S., Pickett, K. A., Duncan, R. P., Perlmutter, J. S., and Earhart, G.
M. (2014). Brain Activity during Complex Imagined Gait Tasks in
Parkinson Disease. Clin. Neurophysiol. 125, 995–1005. doi:10.1016/j.
clinph.2013.10.008
Pistacchi, M., Gioulis, M., Sanson, F., Giovannini, E. D., Filippi, G., Rossetto, F.,et al. (2017). Gait Analysis and Clinical Correlations in Early Parkinson’s
Disease. Fn 32, 28–34. doi:10.11138/fneur/2017.32.1.028
Post, B., Merkus, M. P., de Haan, R. J., and Speelman, J. D,and CARPA Study
Group (2007). Prognostic Factors for the Progression of Parkinson’s Disease: A
Systematic Review. Mov Disord. 22, 1839–1851. doi:10.1002/mds.21537
Rochester, L., Galna, B., Lord, S., and Burn, D. (2014). The Nature of Dual-Task
Interference during Gait in Incident Parkinson’s Disease. Neuroscience 265,
83–94. doi:10.1016/j.neuroscience.2014.01.041
Rovini, E., Maremmani, C., and Cavallo, F. (2017). How Wearable Sensors Can
Support Parkinson’s Disease Diagnosis and Treatment: A Systematic Review.
Front. Neurosci. 11, 555. doi:10.3389/fnins.2017.00555
Sant’Anna, A., Salarian, A., and Wickstrom, N. (2011). A New Measure ofMovement Symmetry in Early Parkinson’s Disease Patients Using Symbolic
Processing of Inertial Sensor Data. IEEE Trans. Biomed. Eng. 58, 2127–2135.
doi:10.1109/TBME.2011.2149521
Tunca, C., Pehlivan, N., Ak, N., Arnrich, B., Salur, G., and Ersoy, C. (2017). Inertial
Sensor-Based Robust Gait Analysis in Non-hospital Settings for Neurological
Disorders. Sensors 17, 825. doi:10.3390/s17040825

Parkinson’s Disease. PLOS ONE 8, e56956–9. doi:10.1371/journal.pone.
0056956
Kluge, F., Gaßner, H., Hannink, J., Pasluosta, C., Klucken, J., and Eskoﬁer, B.
(2017). Towards mobile Gait Analysis: Concurrent Validity and Test-Retest
Reliability of an Inertial Measurement System for the Assessment of SpatioTemporal Gait Parameters. Sensors 17, 1522. doi:10.3390/s17071522
Maidan, I., Rosenberg-Katz, K., Jacob, Y., Giladi, N., Deutsch, J. E., Hausdorff,
J. M., et al. (2016). Altered Brain Activation in Complex Walking Conditions inPatients with Parkinson’s Disease. Parkinsonism Relat. Disord. 25, 91–96.
doi:10.1016/j.parkreldis.2016.01.025
Mao, Y., Ogata, T., Ora, H., Tanaka, N., and Miyake, Y. (2021). Estimation ofStride-By-Stride Spatial Gait Parameters Using Inertial Measurement Unit
Attached to the Shank with Inverted Pendulum Model. Sci. Rep. 11, 1391.
doi:10.1038/s41598-021-81009-w
Mariani, B., Rochat, S., Büla, C. J., and Aminian, K. (2012). Heel and Toe Clearance
Estimation for Gait Analysis Using Wireless Inertial Sensors. IEEE Trans.
Biomed. Eng. 59, 3162–3168. doi:10.1109/tbme.2012.2216263
Micó-Amigo, M. E., Kingma, I., Faber, G. S., Kunikoshi, A., van Uem, J. M. T., vanLummel, R. C., et al. (2017). Is the Assessment of 5 Meters of Gait with a Single
Body-Fixed-Sensor Enough to Recognize Idiopathic Parkinson’s Disease-AssociatedGait? Ann. Biomed. Eng. 45, 1266–1278. doi:10.1007/s10439-017-1794-8
Micó-Amigo, M. E., Kingma, I., Heinzel, S., Rispens, S. M., Heger, T., Nussbaum, S.,et al. (2019). Potential Markers of Progression in Idiopathic Parkinson’s Disease
Derived from Assessment of Circular Gait with a Single Body-Fixed-Sensor: A
5 Year Longitudinal Study. Front. Hum. Neurosci. 13, 59. doi:10.3389/fnhum.2019.
00059
Mirelman, A., Bonato, P., Camicioli, R., Ellis, T. D., Giladi, N., Hamilton, J. L., et al.
(2019). Gait Impairments in Parkinson’s Disease. Lancet Neurol. 18, 697–708.
doi:10.1016/S1474-4422(19)30044-4
Morris, M. E., Iansek, R., Matyas, T. A., and Summers, J. J. (1994a). Ability to
Modulate Walking Cadence Remains Intact in Parkinson’s Disease. J. Neurol.
Neurosurg. Psychiatry 57, 1532–1534. doi:10.1136/jnnp.57.12.1532
Morris, M. E., Iansek, R., Matyas, T. A., and Summers, J. J. (1996). Stride Length
Regulation in Parkinson’s Disease. Brain 119, 551–568. doi:10.1093/brain/119.2.551
Morris, M. E., Iansek, R., Matyas, T. A., and Summers, J. J. (1994b). The
Pathogenesis of Gait Hypokinesia in Parkinson’s Disease. Brain 117,
1169–1181. doi:10.1093/brain/117.5.1169
Nagano, H., Begg, R. K., Sparrow, W. A., and Taylor, S. (2011). Ageing and Limb
Dominance Effects on Foot-Ground Clearance during Treadmill andOverground Walking. Clin. Biomech. 26, 962–968. doi:10.1016/j.
clinbiomech.2011.05.013

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