Abe et al.
Journal of Physiological Anthropology(2022) 41:24
https://doi.org/10.1186/s40101-022-00298-w

Open Access

ORIGINAL ARTICLE

Effects of exercise habituation and aging
on the intersegmental coordination of lower
limbs during walking with sinusoidal speed
changeDaijiro Abe1* , Kiyotaka Motoyama1, Takehiro Tashiro2, Akira Saito1 and Masahiro Horiuchi3

Abstract
Background: The time courses of the joint elevation angles of the thigh, shank, and foot in one stride during walking
can be well approximated by a “plane” in a triaxial space. This intersegmental coordination (IC) of the lower limb elevation angles is referred to as the planar covariation law. We examined the effects of exercise habituation and aging on
the thickness of the IC plane of the lower limbs under sinusoidal speed changing conditions.
Methods: Seventeen sedentary young (SY), 16 active young (AY), and 16 active elderly (AE) adults walked on atreadmill in accordance with a sinusoidal speed changing protocol at 120, 60, and 30 s periods with an amplitude of± 0.56 m·s−1. Motion of the lower limbs from the sagittal direction was recorded to calculate the elevation angles ofthe lower limbs. When the best-fit IC plane was determined, the smallest standard deviation of the IC plane was considered as the anteroposterior gait variability of the lower limbs. The coefficient of variance of the step width was also
quantified to evaluate the lateral step variability ­(CVSW).
Results: The standard deviation of the IC plane was significantly greater in the order of SY, AY, and AE, regardless ofthe sinusoidal wave periods of the changing speed. The ­CVSW was not significantly different among the three groups.
Conclusions: Exercise habituation influences anteroposterior gait variability of the lower limbs, but not lateral step
variability, even in young adults. Given these, gait adaptability for sinusoidal speed changes does not always declinewith aging.
Trial registration: UMIN000031456 (R0000​35911; registered February 23, 2018).
Keywords: Whole-body coordination, Interlimb coordination, Lack of exercise, Erect bipedalism, Locomotion,
Biomechanics, Dynamic balance
Background
Erect bipedalism is an intrinsic human gait pattern. Oneof the biological benefits of the erect bipedalism has beenreported to be more economical than quadrupedalism*Correspondence: abed@ip.kyusan-u.ac.jp
1
Center for Health and Sports Science, Kyushu Sangyo University, 2‑3‑1
Matsukadai, Higashi‑ku, Fukuoka 813‑8503, Japan
Full list of author information is available at the end of the article[1]. However, it also underlies several functional disadvantages, such as knee-back pain [2], slower top speed
compared with other mammals [3–5], and increased fallrisk [6]. In human gait, the time courses of the elevation
angles of the thigh, shank, and foot in one gait cycle canbe well approximated by a plane in a triaxial space [7],which is referred to as the planar covariation law (PCL)[8–11]. This spatiotemporal intersegmental coordination(IC) of the elevation angles of the lower limbs functions© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. The Creative Commons Public Domain Dedication waiver (http://​creat​iveco​
mmons.​org/​publi​cdoma​in/​zero/1.​0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Abe et al. Journal of Physiological Anthropology(2022) 41:24

as the whole-body coordination, and it contributes tothe simplification of the control of locomotion. The spatiotemporal IC was disrupted when one leg was perturbed [9, 10]. Thus, maintaining the plane planarity of
the IC during the entire gait cycle could represent eachindividual’s gait stability. It has been reported that a rehabilitation program in conjunction with a habitual exercise
improved gait stability in the elderly [12], middle-aged[13], and young patients with chronic ankle instability[14, 15] or cerebral palsy [16]. Postural balance was lessstable in untrained elderly adults than in trained elderly
and young adults [17, 18]. Recreational soccer training
in sedentary young men improved postural balance [19].
These previous findings suggest that an exercise habituation could be associated with gait stability even in healthy
young adults who do not suffer from any chronic disability. However, it is noteworthy that these previous studies used different indices and tested speeds in each study[12–19], indicating that it is impossible to make a directcomparison of those data. One of the benefits of the PCL
is that it can be established regardless of gait speed [11].
To date, aging has been acknowledged as the major
cause of gait instability in healthy adults (e.g., [20]). However, an age-related effect on the ability to walk begin to
decline, at least, around the mid-forties [21]. Given thesebackgrounds, it is currently debated on whether the PCL
concept can detect gait variability differences between
young and elderly adults [9, 22–25]. Some of these previous studies found an age-related difference of the plane
planarity [22, 23], but others did not [9, 24, 25]. These
discrepancies indicated that factors other than aging may
influence gait variability. We also questioned whether
the IC thickness represents the whole-body gait variability. In this sense, step width (SW) variability should
also be evaluated because it would reflect lateral gaitvariability which is associated with fall risks [20, 26–28].
Another important point is that age-related variability ofthe major gait parameters are likely to be greater at fastercompared with slower speeds [29–31]. This could be
attributed to the fact that the neuromuscular system does
not have enough time to accomplish appropriate adjustment of the lower limbs at faster speeds. However, little
information has been available about the effects of changing speed on the gait stability [9, 10]. These studies especially investigated a perturbation-induced compensatory
strategy during walking when one leg was perturbed,
thus indicating that the effects of gradual speed changes
on the plane planarity during walking in the absence of
perturbation have not yet been investigated. Herein, asinusoidal speed-changing protocol will be beneficial
because it involves both slower and faster speeds. It also
requires an adaptability for a consecutive intersegmental
limb adjustment during walking. Therefore, the purpose

Page 2 of 9of this study was to test a hypothesis that anteroposterior
and/or lateral gait variability would be greater in the sedentary young (SY) than in the active young (AY) adults
in conditions where gait speed was sinusoidally changed.
We also hypothesized that anteroposterior and/or lateral
gait variability would be greater in the active elderly (AE)
adults compared with other young groups.

Methods
ParticipantsThis study involved 49 participants, grouped by age andexercise habituation into 17 SY (eight males and nine
females; 20.3 ± 0.9 years old, mean ± standard deviation[SD]), 16 AY (ten males and five females, 19.9 ± 0.6 years
old), and 16 AE participants (10 males and six females,
74.1 ± 4.6 years old). The ethical committee established in
Kyushu Sangyo University approved all procedures (no.
2019-0002). In adherence to the Declaration of Helsinki,
all participants provided written informed consent after
being provided with information about the purposes,
experimental protocols, and possible risks of this study.
The SY participants had no current experience inhabitual dynamic exercise except for that occurred during their participations in physical education classes during their schooldays, indicating that the SY participants
were far from the minimum recommendation of the
WHO guideline (at least 150 min of weekly moderateintensity aerobic physical activity [32]). The AY participants belonged to competitive sports teams and wereassociated with dynamic exercises (e.g., volleyball, soccer, tennis, and track and field) in their high school days.
They are still regularly engaging in sports activities at arecreational level (90–120 min a day, 2–4 times a week)
after university admission. The AE participants belong
to a community walking and mountain climbing clubin their local community (brisk walking habituation for
40–60 min a day, 5–7 days a week). The enrollment criteria in this club required that the person could climb
up the mountains located around the city (300–500 m
above the sea level) without an assistive device and otheraid. Therefore, the AY and AE participants apparently
exceeded WHO’s physical activity recommendation [32].
Protocols and motion analysis

The participants wore a half spats, compression shirts,
and the same shoes (but different sizes) (ADIZERO-RC,
Adidas, Herzogenaurach, Germany). The use of the PCL
concept was effective to clarify the adaptability of continuous speed-changing tasks because an abrupt change in
the gait speed is likely to violate perturbed leg’s IC [9, 10].
After warming-up and habituation on a motor-driven
treadmill (TKK3080, Takei Scientific Instruments, Niigata, Japan), the preferred walking speed (PWS) of each

Abe et al. Journal of Physiological Anthropology(2022) 41:24

individual was determined [33] for the baseline speed(i.e., midpoint speed during sinusoidal walking). A week
later, the participants freely walked on the same treadmill at the baseline speed for 30 s, and then the treadmill
speed was changed in a sinusoidal manner of 120, 60, and30 s periods with amplitudes equal to ± 0.56 m·s−1 (±2 km·h−1) in a randomized order (Fig. 1A).
Twelve reflective markers were placed on both shoulders (acromion), lateral greater trochanters, knees (lateral femur epicondyle), ankles (lateral malleolus), toes
(toe of each shoe), and heels (backend of each shoe) [8,11]. Four additional markers were placed on each cornerof the treadmill. Motion data were captured with eighthigh speed cameras (Kestrel300, MAC3D System, Rohnert Park, CA, USA) with a sampling rate of 100 Hz. The
root mean square errors in the calculation of the threedimensional (3D) marker locations were found to be< 1.0 mm. The entire gait cycle, which was defined fromPage 3 of 9

the heel-contact to the toe-off, was divided distinct partsin the range of 0–100%. The 3 × 3 matrix of the elevation
angles of the right thigh, shank, and foot (30-120 s) was
computed from the marker locations (Fig. 1B) at every
time frame. The best-fit 3D covariation loop did not
perfectly lie on a plane [7–11]. Additionally, the plane
seems to fluctuate during walking according to a sinusoidal speed-changing manner (Fig. S1). Considerablylarge variations in the IC “thickness” were observed when
the shoe sole slightly rubbed the treadmill belt before
the heel strike. To avoid these incomplete motions, eachsinusoidal cycle was repeated continuously four times.
Although the first sinusoidal period was fundamentallyused for subsequent analyses, the second, third, or fourth
cycle was used only when perturbations occurred in theearlier cycles. Thus, we did not use the largest standard
deviation or mean value to represent the IC thickness,and the smallest standard deviation of the fluctuating

Fig. 1 Schematic illustrations of the setup and data analysis. A Sinusoidal speed changes at different periods (120, 60, and 30 s). Shaded areas
are the analyzed sections. B Definitions of elevation angles at the thigh, shank, and foot. C The calculation procedure of the best-fit loop of theelevation angles of thigh, shank, and foot is plotted in a squared 3D space as a “plane” (shaded in yellow). This plane was simultaneously rotatedaround the z and y axes (shaded in blue). The z angle, at which the smallest standard deviation was obtained, is regarded as the “thickness” of thespatiotemporal intersegmental coordination (shaded in red)Abe et al. Journal of Physiological Anthropology(2022) 41:24

Page 4 of 9plane in one gait cycle was regarded as the IC thickness(Fig. 1C).
In a practical computational calculation, the best-fit 3D
approximation of the angular covariation is not a dimensionless plane. Thus, according to the definition of Euler’s
angle, when the best-fit plane of the 3D covariation was
detected, it was rotated around the z-axis (foot elevation
angle) as follows,

 
 
cos θ z sin θz 0ααz
 βz  =  − sin θz cos θz 0  β 
0
0 1
γ
γz


(1)

where α, β, and γ are the original best-fit covariations,and αz, βz, and γz are the covariations after rotated aroundthe z-axis. The matrix described by Eq. (1) was simultaneously rotated around the y-axis (knee elevation angle)
as follows,

 
 
cos θ y 0 sin θ yαzαyβy =  0
1 0  βz − sin θy 0 cos θy
γz
γy


(2)

where αy, βy, and γy are the covariations after rotation
around the y-axis. Thus, the plane was rotated by a combination of the matrices 1 and 2 as follows:(ANOVA) within periods and between groups was
performed on the dependent variables using ANOVA
4 on the web. Eta-squared values (η2) were also presented [35]. If a significant F value was obtained on thedependent variables, Ryan’s post hoc test was applied
to the appropriate datasets to detect significant meandifferences. Its statistical power has been reported tobe equivalent to Tukey’s post hoc test [36] and can beused regardless of the data distribution [36]. That is,assessment of data normality is not always required if
Ryan’s post hoc test is applied for the data sets. Cohen’sd values were further provided for the post hoc test[35]. Statistical significance was set at p < 0.05. All data
were presented as mean ± SD.

Results
The baseline speed was 1.37 ± 0.06, 1.30 ± 0.07, and1.08 ± 0.07 m·s−1 in the AY, SY, and AE adults, respectively. As shown in Fig. S1, the IC plane continuously
lurched during walking under sinusoidal speed-changing conditions. A significant main effect of participantgroups was observed in the thickness of the IC plane(F2,46 = 13.917, p < 0.001, η2 = 0.367; Fig. 2), but no significant interactions (F4,92 = 1.714, p = 0.154, η2 = 0.002;
Fig. 2) and main effects of sinusoidal walking periods
were observed (F2,46 = 1.258, p = 0.289, η2 = 0.001;
Fig. 2). A post hoc test revealed that the thickness of⎛ 𝛼 � ⎞ ⎛ cos 𝜃y 0 sin 𝜃y ⎞⎛ cos 𝜃z sin 𝜃z 0 ⎞⎛ 𝛼 ⎞ ⎛ cos 𝜃y cos 𝜃z cos 𝜃y sin 𝜃z sin 𝜃y ⎞⎛ 𝛼 ⎞⎜ 𝛽� ⎟ = ⎜− sin 𝜃zcos 𝜃z0 ⎟⎜ 𝛽 ⎟0
1 0 ⎟⎜ − sin 𝜃z cos 𝜃z 0 ⎟⎜ 𝛽 ⎟ = ⎜⎟⎜ ⎟⎟⎜ ⎟ ⎜⎟⎜⎜ �⎟ ⎜
00 1 ⎠⎝ 𝛾 ⎠ ⎝ − sin 𝜃y cos 𝜃z − sin 𝜃y sin 𝜃z cos 𝜃y ⎠⎝ 𝛾 ⎠⎝ 𝛾 ⎠ ⎝ − sin 𝜃y 0 cos 𝜃y ⎠⎝Given that both rotation angles, “θz” and ”θy,” ranged
from 0° to 179°, 32400 (180 × 180) combinations can be
defined. The IC thickness was then calculated in a nonarbitrary computational space.
The SW was quantified as the lateral distance between
both heel makers in each step [20], and it was lessdependent on the gait speed [28]. Thus, the SW was
measured during the whole first period (30–120 s), andthe coefficient of variance of the SW ­(CVSW; %) and“actual length” of the SW variability itself (cm) were thencalculated.
Statistical analysis

The software G*Power 3.1 [34] was used to estimate therequired number of participants with an effect size of
0.25 [35], an alpha level of 0.05, and a statistical powerof 80%; at least 12 participants would be needed in eachgroup. Two-way repeated measures analysis of variance(3)

the IC plane was significantly greater in the SY thanthe AY (t97 = 2.227, p = 0.031, d = 0.851; Fig. 2) andAE (t97 = 5.281, p < 0.001, d = 1.608; Fig. 2). It was also
greater in the AY than in the AE (t97 = 3.009, p = 0.004,
d = 1.166; Fig. 2). The ­C VSW was not significantly different between periods (F2,46 = 2.607, p = 0.079, η2 = 0.059;
Fig. 3A) and groups (F2,46 = 1.682, p = 0.197, η2 = 0.007;
Fig. 3A). The actual length of the SW variability was
not significantly different between groups (F2,46 = 0.221,
p = 0.803, η2 = 0.008; Fig. 3B), but it was significantly
smaller in the 30 s period compared with the otherperiods (F2,46 = 3.785, p = 0.026, η2 = 0.011; Fig. 3B). A
post hoc test revealed that the actual length of the SW
variability was significantly smaller in the 30 s period
than in the 60 s period (t96 = 2.209, p = 0.030, d = 0.193;
Fig. 3B) and 120 s period (t96 = 2.526, p = 0.013,
d = 0.170; Fig. 3B). These F and p values of 2-way
ANOVA were summarized in Table S1.

Abe et al. Journal of Physiological Anthropology(2022) 41:24

Page 5 of 9

Fig. 2 Comparisons of the thickness values associated with spatiotemporal intersegmental coordination (IC). Participants walked according to asinusoidal speed-changing protocol for time periods of 30 s, 60 s, and 120 s period, respectively. SY, sedentary young (white bars); AY, active young(black bars); and AE, active elderly adults (gray bars), respectively. #p < 0.05 and †p < 0.01, respectively. Values are means ± standard deviations

Discussion
In support of our first hypothesis, the IC thickness was
significantly greater in the SY compared with the AY
during the application of the sinusoidal speed-changing
protocol at any measured period (Fig. 2). This finding
suggests that the anteroposterior gait variability was not
influenced by aging alone. The ability to walk began to
decline at around the mid-40s [21]. Many previous studies which investigated gait stability aimed to avoid falling
and/or reducing fall risks in the elderly adults [8–15, 20,
22–31]. By contrast, it has been reported that middle-aged[6, 37] and young [6, 37, 38] adults also fall in their daily
lives. Fall accidents in young adults were more frequentas physical activity levels increased [38]. These incidents
tended to occur mostly due to perturbed surface conditions [6]. Thus, little is known about the effects of exercise habituation on gait variability in healthy young adults
without any chronic gait disability. Physiological variability is sometimes associated with adaptability or flexibility(e.g., heart rate variability (HRV)) [39–41]. In the HRV
literatures, a greater variability magnitude means greater
adaptability for exercise tolerance [39]. However, someconsiderations are necessary with regard to gait variability because excessive gait variability is likely linked withan increase in fall risk [26, 27], which is one of the major
biological disadvantages of erect bipedal locomotion.
Owing to a limited treadmill capacity, all previous studies
using the PCL concept have employed a constant speed
protocol [7–11, 22–25, 28, 30]. Conversely, we focused on
the lower limb coordination variability when the walking
speed was sinusoidally changed at different three periods.
Thus, gait speed was gradually and continuously changedin our study, indicating that our protocol did not emulate
exactly unexpected slip-like perturbation [9, 10]. Our present results demonstrate that exercise habituation affected
anteroposterior gait variability of the lower limbs even in
the young generation (Fig. 2), which is supported by the
results of a previous study [19]. This functional difference
between SY and AY was not influenced by sinusoidally
speed-changing periods which ranged from 30 to 120 s(Fig. 2). These results may indicate that anteroposterior
gait adaptability of the lower limbs is independent of therate of change of speed in young adults.
Although a trend for a greater C­ VSW value wasobserved in the order of SY, AY, and AE (p = 0.079;
Fig. 3A), the ­C VSW values were not significantly different between the groups. A recent meta-analysis study
reported that the “optimal reference range of the SW variability” was within 1.97 cm in young adults [20]. In ourpresent study, the actual length of the SW variability was
not significantly different between the groups (Fig. 3B),
and it was ≤ 1.97 cm in all measured periods (Fig. 3B).
Given these results, lateral gait variability was not influenced by exercise habituation in the case of the sinusoidal speed-changing protocol.
To our surprise, the plane planarity (IC thickness) evaluated by the PCL concept was significantly smaller in the
AE than other young groups regardless of the sinusoidal
periods (Fig. 2), indicating that our second hypothesis
was rejected. These results seemed to indicate that the
AE adults presented greater anteroposterior gait variability compared with young adults. However, careful considerations are necessary because the AE adults are likely
to stabilize their ankle joints by means of a coactivation

Abe et al. Journal of Physiological Anthropology(2022) 41:24

Page 6 of 9

Fig. 3 Comparisons of step width (SW) variability. Coefficient of variance values of the SW (­ CVSW; %) and actual length of the SW variability (cm)were compared between groups and periods. A There were no significant differences in the C­ VSW among sedentary young adults (SY, white bars),
active young adults (AY, black bars), and active elderly adults (AE, gray bars). B The length of the SW variability was not significantly different between
groups, but it was significantly smaller in the 30 s period compared with the other periods. # indicates p < 0.05. Values are means ± standard
deviationsof the soleus and tibialis anterior [42, 43], thus potentially resulting in a narrower range of the ankle joint
motion. In fact, there was a smaller time delay between
thigh and shank motions in the elderly adults than young
adults at any gait speed [22]. Such a greater time delayin the shank-foot coordination provided greater distortion of the planarity of the IC plane [22]. Thus, agerelated increase in a stabilization of the ankle joint during
walking could be a strategy to avoid falling in the elderly
adults. Given that the narrower range of the ankle joint

elevation angle can easily synchronize other joint (kneeand hip joints) elevation angles [8, 20], it is perceivablethat the AE adults were associated with a smaller anteroposterior gait variability compared with other younggroups (Fig. 2). In addition, the AE adults walked on thetreadmill at several sinusoidal speed-changing conditions at a C­ VSW of ~ 9% (Fig. 3A). These values were quite
smaller than those of previous studies even when walking
at constant speeds [23, 26, 44, 45], and the actual lengthof the SW variability was small enough with respectAbe et al. Journal of Physiological Anthropology(2022) 41:24

Page 7 of 9

to the optimal reference range of the SW variability(2.50 cm) in the case of elderly adults [20]. It has alreadybeen well acknowledged that lateral gait stabilization canbe established by an interaction between active control of
the central nervous system and passive musculoskeletal
dynamics [46]. A decrease in the sensorimotor precisionpotentially results in greater ­C VSW values [47]. Indeed,
the age difference did not always explain the C­ VSW
between young and elderly adults [31], which was ingood agreement with our present results (Fig. 3A). Basedon these theoretical and experimental backgrounds, lateral gait variability in our AE adults was well maintained
despite of aging.
Methodological considerations should not be
neglected. Some previous studies have presented thatthe elderly adults exhibited different plane planarities
compared with younger adults [22, 23]. Such an age difference could be derived from reduced ankle torque inelderly adults [48], resulting in a gait instability especially at faster speeds [26–31]. In contrast, some others
did not observe any age differences in gait variability
which was evaluated based on the plane planarity [9, 24,25], despite the fact that these studies employed a similar technique to that employed herein. This discrepancy
suggests that gait variability may depend on the protocol(task) itself. A novelty of the present study refers to theuse of a sinusoidal speed changing protocol with different wave periods because it consecutively required spatiotemporal interlimb adjustment. Thus, the sinusoidal
speed-changing protocol used in the present study may
be more sensitive in its ability to detect IC thickness
difference rather than a use of constant speed protocolthat was previously employed [7–11, 22–25]. In otherwords, greater gait adaptability observed in active adults
for sinusoidal speed changes may function as one of thefunctional potentialities [49] to avoid falling regardlessof aging. In addition, no significant differences in the IC
thickness were found among the measured three wave
periods in each group (Fig. 2), suggesting that the ability for spatiotemporal gait adjustment is inherent ineach individual over 30 s period under the sinusoidal
speed-changing protocol. As a limitation of this study,
an amplitude of the sinusoidal speed change was set
at ±0.56 m·s−1 in all measured periods in the present
study. This was set to avoid gait transition, but effects of
the amplitudes of the sinusoidal speed changes should
be further investigated in future studies.

different among the three studies groups. These
results suggest that anteroposterior gait variability, but
not lateral gait variability, was influenced by exercise
habituation even in young adults without any chronic
disability when walking under sinusoidal speed-changing protocol. Given these, individual gait adaptability
does not always decline with aging.

Conclusions
The IC thickness values were significantly greater inthe order of SY, AY, and AE, regardless of sinusoidal
wave periods. The ­C VSW values were not significantly

Competing interests
The authors declare no competing interests.

Abbreviations
ANOVA: Analysis of variance; AE: Active elderly; AY: Active young; CVSW:
Coefficient of variance of step width; HRV: Heart rate variability; IC: Intersegmental coordination; PCL: Planar covariation law; PWS: Preferred walking
speed; SD: Standard deviation; SW: Step width; SY: Sedentary young; 3D:
Three-dimensional.

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40101-​022-​00298-w.
Additional file 1: Table S1. Summary of statistical results.
Additional file 2: Figure S1. Fluctuating intersegmental coordination of
the lower limbs during walking with sinusoidal speed-changing protocol.
Left, middle, and right panels present representative participants insedentary young (SY), active young (AY), and active elderly (AE) groups,
respectively. Red, purple, and green loops refer to the 30 s, 60 s, and 120 s
periods, respectively.
Acknowledgements
We specially thank Mr. Akinobu Sakamoto, Mr. Tomokazu Iwatani, Mr.
Hiromichi Ikegami, Mr. Takeshi Saito, Mr. Masaru Hashimura, and Mr. Shizuo
Takatoh (Takei Scientific Instruments Co., Ltd.) for customizing the treadmill tocontrol the gait speed sinusoidally.
Authors’ contributions
DA, KM, and MH designed the study. DA and KM collected and analyzed the data.
DA drafted the original manuscript, and then KM, AS, and MH revised the manuscript. All authors have read and agreed to the final version of the manuscript.
Funding
This study was financially supported by Grant-in-Aid for Scientific Researchfrom the Japan Society for the Promotion of Science (JP19K11541 and
JP22K11517 to DA, JP20K19623 to KM, JP21K17613 to AS, and JP18K11012 toMH). Equipment and software installation were also supported by Grant-inAid for KSU Scientific Research and Encouragement of Scientists (K035058 andR035098 to DA).
Availability of data and materials
The data of this study are available from the corresponding author upon
reasonable request.

Declarations
Ethics approval and consent to participate
Following the Declaration of Helsinki, all participants provided written informed
consent after being provided with information about the purposes, experimental protocols, and possible risks of this study. The ethical committee establishedin Kyushu Sangyo University approved all procedures (no. 2019-0002).

Author details
1
Center for Health and Sports Science, Kyushu Sangyo University, 2‑3‑1 Matsukadai, Higashi‑ku, Fukuoka 813‑8503, Japan. 2 Human Robotics Research Center,

Abe et al. Journal of Physiological Anthropology(2022) 41:24

Kyushu Sangyo University, Fukuoka, Japan. 3 Division of Human Environmental
Science, Mount Fuji Research Institute, Fuji‑yoshida, Yamanashi, Japan.
Received: 7 March 2022 Accepted: 27 May 2022

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