ORIGINAL RESEARCH
published: 27 February 2020
doi: 10.3389/frobt.2020.00010

Synchronized Tactile Stimulation on
Upper Limbs Using a Wearable
Robot for Gait Assistance in Patients
With Parkinson’s Disease
Takayuki Kishi 1 , Taiki Ogata 2*, Hiroki Ora 2 , Ryo Shigeyama 1 , Masayuki Nakayama 3 ,Masatoshi Seki 4 , Satoshi Orimo 5 and Yoshihiro Miyake 2
1

Department of Systems and Control Engineering, Tokyo Institute of Technology, Yokohama, Japan, 2 Department ofComputer Science, Tokyo Institute of Technology, Yokohama, Japan, 3 Kikuchi Seisakusho Co. Ltd., Hachioji, Japan,
4
WALK-MATE LAB Co. Ltd., Hachioji, Japan, 5 Department of Neurology, Kanto Central Hospital, Setagaya, Japan

Edited by:
Jungwon Yoon,
Gwangju Institute of Science andTechnology, South Korea
Reviewed by:
Arturo Forner-Cordero,
University of São Paulo, Brazil
Beom-Chan Lee,
University of Houston, United States
Muhammad Raheel Afzal,
KU Leuven, Belgium*Correspondence:
Taiki Ogata
ogata@c.titech.ac.jp
Specialty section:
This article was submitted to
Biomedical Robotics,
a section of the journal
Frontiers in Robotics and AI
Received: 02 April 2019
Accepted: 20 January 2020
Published: 27 February 2020
Citation:
Kishi T, Ogata T, Ora H, Shigeyama R,
Nakayama M, Seki M, Orimo S and
Miyake Y (2020) Synchronized Tactile
Stimulation on Upper Limbs Using a
Wearable Robot for Gait Assistance inPatients With Parkinson’s Disease.
Front. Robot. AI 7:10.
doi: 10.3389/frobt.2020.00010

This study aimed to investigate whether using a wearable robot applying interactive
rhythmic stimulation on the upper limbs of patients with Parkinson’s disease (PD)
could affect their gait. The wearable robot presented tactile stimuli on the patients’
upper limbs, which was mutually synchronized with the swing of their upper limbs. We
conducted an evaluation experiment with PD patients (n = 30, Modified Hoehn-Yahr= 1–3, on-state) to investigate the assistance effect by the robot and the immediate
after-effect of intervention. The participants were instructed to walk 30 m under four
different conditions: (1) not wearing the robot before the intervention (Pre-condition), (2)wearing the robot without the rhythm assistance (RwoA condition), (3) wearing the robot
with rhythm assistance (RwA condition), and (4) not wearing the robot immediately after
the intervention (Post-condition). These conditions were conducted in this order over a
single day. The third condition was performed three times and the others, once. The
arm swing amplitude, stride length, and velocity were increased in the RwA condition
compared to the RwoA condition. The coefficient of variance (CV) of the stride duration
was decreased in the RwA condition compared to the RwoA condition. These results
revealed that the assistance by the robot increased the gait performance of PD patients.
In addition, the stride length and velocity were increased and the stride duration CV wasdecreased in the Post-condition compared to the Pre-condition. These results showthat the effect of robot assistance on the patient’s gait remained immediately after theintervention. These findings suggest that synchronized rhythmic stimulation on the upper
limbs could influence the gait of PD patients and that the robot may assist with gait
rehabilitation in these patients.
Keywords: gait assist, rhythm synchronization, stimulation on upper limbs, wearable robot, Parkinson’s disease,abnormal gait

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INTRODUCTION

people (Hausdorff et al., 2000; Bartsch et al., 2007; Hausdorff,
2009). Hove et al. revealed that using the auditory Walk-Matesystem, the 1/f -like structure in the stride duration distribution
reappeared in PD patient’s gait but not using RAS (Hove et al.,2012). In addition, the improvement of fluctuation seen in PD
patients who used the auditory Walk-Mate system continued forseveral days (Uchitomi et al., 2013).
Yap et al. developed a wearable robot for gait assistance using
the Walk-Mate system (Yap et al., 2019). This robot produces
tactile stimuli on the upper limbs. The stimuli were synchronizedwith the swing of the upper limbs. The authors applied therobotic device to healthy elderly people and compared gaitparameters of these individuals with and without wearing therobot. The results showed that the participants’ hip swing angle
increased in the gait while wearing the robot individuals. This
result revealed that the synchronized tactile stimuli on the upper
limbs improved people’s gait performance via the coordination
between the upper and lower limbs in walking (Dietz, 2002).
Although arm swing amplitude reduces, the coordination
between the upper and lower limbs is preserved in PD
patients (Dietz and Michel, 2008; Dietz, 2011). Therefore, if
the upper-limb movements increased using the synchronizedtactile stimulation on the upper limbs, the gait performance of
the PD patients would increase. The purpose of this study was
first to verify whether presenting synchronized tactile rhythmon the patient’s upper limbs, as generated by the Walk-Materobot, increased the gait performance of the patients with PD.
As described in a previous study (Yap et al., 2019), we usedtactile stimuli on the patient’s upper limbs for two reasons.
For evaluation of the robot, we used the following clinicallyimportant indexes: arm swing amplitude, stride length, velocity,and coefficient of variation (CV) of stride duration. A weakarm swing increases the fall risk of patients with PD (Woodet al., 2002). The smaller the stride length or velocity, the higher
the risk of freezing (Hausdorff et al., 2003) of gait of patientswith PD (Nieuwboer et al., 2001; Chee et al., 2009; Contreras
and Grandas, 2012). The stride duration of CV is one index
of gait rhythm stability. The larger the CV of stride duration,
the higher the fall risk of patients with PD (Schaafsma et al.,2003). These three indexes were compared between gait withand without the presence of synchronized tactile stimuli withthe patient’s arm swing to reveal the functional effect of therobot’s assistance on the patient’s gate. As mentioned above,
an increase of the stride length and velocity continued after
the intervention by auditory rhythmic stimulation (Thaut et al.,
1996; Hausdorff et al., 2007; Uchitomi et al., 2013). Thus, we
compared these indexes between pre- and post-intervention
measurements to reveal the immediate after-effect. The postintervention trial was conducted several minutes after the trial
with robot assistance. This post-intervention was performed
to investigate the possibility of using the proposed system forrehabilitation in the future.

Studies have shown that when two individuals walk side byside, their gait rhythms spontaneously synchronize with eachother (Zivotofsky and Hausdorff, 2007). We focused on thismutual synchronizing phenomenon of gait rhythm to develop
the Walk-Mate system to guide people’s gait. The Walk-Matesystem configures the mutual synchronization of human-humangait rhythms into a human-mechanical system. The WalkMate system uses a model of the mutual entrainment between
non-linear oscillators, such as people’s gait synchronized withrhythmic auditory cues generated by the system (Miyake, 2009).
These auditory rhythmic cues that synchronize with people’s gait
could guide people’s gait rhythm and reduce gait asymmetry(Miyake, 2009; Muto et al., 2012).
Parkinson’s disease (PD) is one of many neurologic diseases
characterized by an abnormal gait (Sveinbjornsdottir, 2016).
PD is a progressive neurodegenerative disease in which the
dopaminergic neurons in the substantia nigra pars compacta
degenerate (Morris et al., 1994). In PD patients, gait rhythm
is abnormal (Avanzino et al., 2016). This abnormal gait in
PD patients is thought to be caused by the disruption ofrhythm generation performed on the projection path entering
the spinal cord via the basal ganglia and the brain stem andinterferences of repeated rhythm movement (Freeman et al.,1993). Thus, rhythmic auditory stimulation has been used asa gait rehabilitation method for PD patients. One method is
the rhythmic auditory stimulation (RAS) method in which arhythmic sound with a constant tempo is presented to thepatients. Studies have shown that gait improved when RAS
was applied to patients with PD (Hausdorff et al., 2007; Thautand Abiru, 2010). For example, RAS increased the stride
length and velocity of the PD patients (McIntosh et al., 1997;
Howe et al., 2003; Willems et al., 2006), and decreased the
double support time (Freedland et al., 2002). The effects of
RAS on PD patient’s gait continued after the intervention(Thaut et al., 1996; Hausdorff et al., 2007). For instance, theincrease in stride length and velocity were observed for 3
weeks (Thaut et al., 1996). These results show the effects ofauditory stimulation and rehabilitation on gait assistance in
PD patients.
Another method is auditory stimulation synchronizing withpeople’s gait (Miyake, 2009; Muto et al., 2012). This auditory
Walk-Mate system reduced the accelerated gait (Giladi et al.,2001) of PD patients, with the effect remaining immediately after
the intervention (Uchitomi et al., 2016). Hove et al. investigatedthe effect of synchronized auditory cues on PD patient’s gait fromthe viewpoint of the fractal scaling of stride times (Hove et al.,2012). The distribution of stride times in the gait of healthypeople is not random; it has a 1/f -like structure, which represents
a fractal-like long-range correlation of the stride times (Hausdorff
et al., 1996; Hausdorff, 2009). That is, the fluctuations of theirstride time are self-similar across multiple time scales. This selfsimilarity of the stride time is shown as log power proportional
to log frequency in power analysis. The elderly with low fractal
scaling of stride times have a higher risk of falling than thosewith high fractal scaling (Herman et al., 2005). In PD patients’gait, the fractal scaling of stride times is weaker than in healthy
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MATERIALS AND METHODS
Algorithm to Present Rhythm Stimuli
Figure 1 shows the flowchart of the core algorithm of therhythm presenting system. The system comprises a rhythm
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FIGURE 1 | Flow chart of the core algorithm for presenting synchronized tactile stimuli with the patient’s arm swing.

mutual synchronization module, a phase difference control
module, and a motor driving module. The first two modules
are used for synchronizing the arm swings and tactile stimuli
presented by the robot. For these modules, we used ourprevious method (Yap et al., 2019) based on the Walk-Matemodel (Miyake, 2009), which assumed that each oscillator

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corresponding to both human arms and robot motors performed
periodic motion. The motor driving module was used forcontrolling the motors equipped on the robot and presenting thetactile stimuli.
Mutual synchronization between the robot rhythm
and human arm swing rhythm is determined by the two

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equations below:θ̇ m_l =ωm_l +khm sin θ h_l −θ m_l +km_rl sin θ m_r −θ m_lθ̇ m_r =ωm_r +khm sin θ h_r −θ m_r +km_rl sin θ m_l −θ m_r
where θm_l and θm_r represent the phases of the left and rightrobot motors. θh_l and θh_r represent the phases of the left andright arms. ωm_l and ωm_r represent the angular frequencies of theleft and right robot motors. khm represents the coupling strength
between the left or right phases of the robot and human. We
used the same coupling strength for the left and right coupling
between the phases of the robot and humans. km_rl was thecoupling strength between θm_l and θm_r . θh_l and θh_r were not
be able to be measured continuously. Therefore, we updated θm_l
and θm_r only at the moment people’s arms started swinging
forward, as shown in Figure 1. The timing of the swing forward
start was defined as the time from when the amount of change in
the arm swing angle changed from a negative to a positive value.
0 rad and π rad were defined as the timing of the swing forward
start of the left and right arms, respectively; θm_l and θm_r were
updated if θh_l = 0 and θh_r = 0, respectively.
The phase different control module controlled the robot
angular frequencies, ωm_l and ωm_r , to make the phase differences
between the robot and humans close to the pre-determinedtarget value. Because the frequency of gait is different between
individuals, the robot’s frequencies should be modified; the robot
can produce phase frequencies close to individual’s frequency.
In detail, the differentials of ωm_l and ωm_r were determined
as follows:

FIGURE 2 | Sample time series of the human left arm swing angle and timingof the robot’s motor driving.

synchronized auditory stimulation, the human’s gait cycle could
be guided faster or slower using θd (Miyake, 2009). In this study,
to present the tactile stimuli at the exact timing we set, θd wasset at 0.
The motor driving module controlled the timing of the motor
driving using θm_r and θm_r , which were calculated in the above
two modules. The left motor was driven if α < θm_l < α + β andthe right motor was driven if 1.0π + α < θm_r < 1.0π + α + β.
α indicates the starting time of the motor. β is the duration of
motor driving. If 0 < α < π, the tactile stimuli were presented
while participants swung their arms forward. If π ≤ α < 2π,
the tactile stimuli were presented while the participants swung
their arms backward. Because the arm swing retroversion of
the patients with PD was reduced (Roggendorf et al., 2012), we
assumed that it was necessary to assist the backward arm swingof the patients with PD. Thus, α and β were set to 1.3π and 0.6π,
respectively. That is, the motors were driven while the people
swung their arm backwards from the timing at which the patientsarm passed the side of the trunk backwards. Figure 2 shows the
sample time series of the left arm swing angle and the robot’s
motor driving. Note that the motor driving module did not workin the first four gait cycles of the human after the robot firstdetects the human’s gait phase because the robot phases were
often unstable in the early stage of synchronization.

ω̇m_l =−µsin(1θ d −(θ h_l −θ m_l ))ω̇m_r =−µsin(1θ d −(θ h_r −θ m_r ))where θd was the target value of the phase difference between the
robot and humans. When θd = 0, the robot produced phasefrequencies to match the individual’s frequency. µ was the gain
required to bring the pre-determined target value close to thephase difference values between the robot and humans. As in
the rhythm mutual synchronization module, ωm_l and ωm_r were
updated if θh_l = 0 and θh_r = 0, respectively.
In this study, we set khm = 0.25, km_rl = 2.5, µ = 0.08,
and θd = 0. If the coupling strength between the robot andhuman, khm , is over 1, the robot modifies the robot’s phase morethan the phase difference between the robot and human. This
overcorrection destabilizes the robot’s rhythm, which would also
disturb human rhythm. If the coupling strength is under 0, therobot phase changes to increase the phase difference between
the robot and human. Thus, the coupling strength should be
between 0 and 1. The coupling strength between the left andright of the robot, km_rl , was set much larger than khm to preventthe rhythm of the left and right tactile stimulation from being
unstable by the phase correction using the phase difference
between the robot and human. In human synchronization withexternal rhythmic stimuli, the correction of frequency is much
weaker than that of the phase (Loehr et al., 2011). In addition, a
rapid change of the robot’s frequency would induce instability of
the human’s gait rhythm. Therefore, we set µ smaller than khm . In

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Wearable Device
The wearable device comprised actuator, control, and power
modules. Figure 3 shows the appearance of the device, whichweighed 4.6 kg. The power module included the battery. The
actuator and control modules are described below.

Actuator Module
DC brushless motors (DR-4316-X14B00421, ShinanoKenshi,
Nagano, Japan) were mounted on the shoulder portions of thedevice. The motor torque to present rhythmic tactile stimuli was
0.074 kg. Because the weight of human’s arm is several kg, thetorque was not strong enough to mechanically move the patient’s

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FIGURE 3 | Appearance of the wearable robot and the three modules included in the robot: actuator, control, and power modules.

arms when the arms does not move. However, the torque would
enhance the arm swing a little bit because the toque was generated
in the same direction of the arm swing. The left and right frames
were used to present the tactile stimuli. One of the two ends ofthe frame were fixed to the motor, and the other was fixed near
the elbow by belts. The tactile stimuli were presented by rotating
the frames through driving the motor.

the approval from Tokyo Institute of Technology’s Research
Ethics Review Committee and Kanto Central Hospital Ethics
Committee. Informed consent was obtained in writing fromall participants.

Task and Conditions
The experimental task involved walking 30 m on a flat floor. The
task was conducted under four conditions: (1) pre-intervention
condition, in which the participants walked without the robot
before the intervention (Pre-condition); (2) robot-withoutassistance condition, in which participants wore the robot andwalked without rhythm assistance (RwoA condition); (3) robotwith-assistance condition, in which participants wore the robot
and walked with the robot assistance (RwA condition); and (4)Post-intervention condition, in which the participants walked
without the robot after the intervention (Post-condition). The
RwoA condition was performed to eliminate the effect of wearing
the robot. We compared the gaits between the RwoA and RwA
conditions to evaluate the effect of gait assistance provided by
the robot. We also compared the Pre- and Post-conditions toevaluate the immediate after-effect.

Control Module
The control module comprised a microcomputer and encoders
in the box placed on the back of the device. A microcomputer
generated the robot rhythms and motor driving commands.
The encoders were mounted on the left and right motors andmeasured the arm swing. The encoder measured the arm swing
amplitude and the measurement range was ±150 degrees withreference to the position where the robot was turned on. The
resolution was 1.2 degrees and the sampling frequency was
100 Hz. The swing angle data were measured to detect the timingof people’s arm swing start for updating θm_l , θm_r , ωm_l , andωm_r . The encoders were calibrated as the following before eachtrial. When the participants stood upright and their both arms
straightened along their body, the robot was turned on. At thattime, the angular of the encoders was set at 0.

Procedure
The participants began and stopped walking after receiving cues
from the experimenter. We instructed participants to walk as
usual, whether they wore the robot or not. In addition, theparticipants were asked to not synchronize with tactile stimuli
from the robot under the RwA condition. It is well-known thatthe dual task decreases the gait performance of PD patients(Kelly et al., 2012; Raffegeau et al., 2019). When PD patients
perform another task such as carrying a tray, generating words,
and reacting to auditory of visual stimuli while walking, theirstride length and velocity reduced. Therefore, we asked theparticipants not to consciously synchronize with the tactile
stimuli. People unconsciously synchronize their movement toexternal stimuli (Zivotofsky and Hausdorff, 2007). Thus, we
assumed that the participant’s gait spontaneously synchronized
to the tactile stimuli, even if they did not intend to synchronize
their gait rhythm with the stimuli. The trials were conducted inExperimentParticipants
The study group comprised 30 patients with PD (male, 18;
female, 12). A prior power analysis (f = 0.25, α = 0.05, power= 0.9, correlation among repeated measures = 0.5) estimated thesample size to be 30. The mean age was 70.8 ± 8.8 years, theheight was 163.9 ± 7.6 cm, and the weight was 58.7 ± 9.9 kg. The
Modified Hoehn-Yahr severity stage (Goetz et al., 2004) ranged
from 1 to 3. All participants were able to walk independently. All
participants were idiopathic PD patients. They had taken medical
treatment for PD and had been on-state before the experiment.
We excluded the patients with freezing of gait or festination
gait and those who needed walking assistance for gait. All
participants did not suffer from musculo-skeletal or neurologicalpathologies, except for PD. This experiment was conducted with

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analysis to exclude the gait changing term by the assistance. For
the Pre-, RwoA, and Post- conditions, we excluded the first andlast three steps in each trial. For the RwA condition, we excluded
three steps following the start of the rhythm assistance and thelast three steps in each trial. For participants 6, 11, 14, and 15,
the robot did not work properly in one trial under the RwA
condition. Therefore, we excluded the trials from the calculationof the mean in the RwA condition. For participant 2, the robot
did not work properly in two trials under the RwA condition.
We then excluded the trials from the mean calculation. We used
the Shaprio-Wilk normality test to confirm the normality ofour data. To compare the mean values of the arm amplitude
between the RwoA and RwA conditions, we performed thepaired t-test. For the stride length, velocity, and CV of thestride duration, we used the one-way repeated ANOVA or theFriedman test when the normality of the data was confirmed
or not, respectively. In the ANOVA, the sphericity of the data
was checked using Greenhouse-Geisser method. If the sphericity
was not confirmed, we adjusted the data by Greenhouse-Geisser’s
epsilon. For the post-hoc tests after the ANOVA and Friedman
test, the Shaffer’s method and multiple pairwise Wilcoxon signedrank test using the Bonferroni correction (Bland and Altman,
1995) were conducted, respectively.

the order of Pre-, RwoA, RwA, and Post- conditions. Under thePre-, RwoA, and Post- conditions, the participants performed
one trial. Under the RwA condition, the participants performed
three trials. In the RwA condition, at the beginning of the trials,
it took several strides to detect the participant’s arm swing andfor synchronization with the individual’s gait rhythm. Thus, thenumber of the strides used for analysis was smaller in the RwA
condition than the other conditions. Thus, we performed three
trials with the RwA condition. Six trials were conducted with eachparticipant. Between the trials, a break was taken for 1–2 min toprevent the effect of fatigue. It took ∼40 min to complete theexperiment. Two experimenters walked behind the participantsin each trial to support them if they would fall down.

Gait Analysis
We used the arm swing amplitude as the evaluation index toevaluate the effect of rhythm synchronization on the arm swing ofpatients with PD. The arm swings were measured by an encoder
mounted on the robot. We defined arm swing amplitude as
the absolute value of displacement of the arm swing angle in
one cycle.
Stride length, velocity, and CV of stride duration were
estimated using the IMU sensors to evaluate the lower limb
movements (Hori et al., 2020). IMU sensors (TSND121, ATRPromotions) were equipped on both of the participant’s shanks.
The IMU sensors measured three-axis acceleration and threeaxis angular velocity. Measurement ranges were ± 8 G for theacceleration and ± 1,000 dps for the angular velocity; thesampling frequency was 100 Hz. Using the IMU sensor data,
the trajectories of the shank and the timing of mid-stance were
estimated; based on the trajectories, the stride length, velocity,and stride duration were calculated. This method was developed
to measure the people’s gait easily. The correlation was over
0.99 between the stride lengths estimated by the method andthose measured by an optical motion capture system, though
the method tended to slightly underestimate the stride length(Hori et al., 2020). The stride length was defined as the distancefrom one step to the next step in the anterior-posterior axis.
The velocity was defined as the value obtained by dividing thestride length by the walking cycle in one cycle of gait. The stride
duration CV was defined as the value obtained by dividing thestandard deviation of the stride duration by the mean value of
stride duration in one trial. We used the stride length and velocity
as the evaluation indexes of the amplitude of the lower limb
movement and stride duration CV as the evaluation index of gaitrhythm stability.

RESULTS
The means and standard deviations (SDs) between theparticipants for the arm swing amplitude were 37.9 ± 18.3 and58.1 ± 23.7 (degrees) under the RwoA and RwA conditions. A
Shapiro-Wilk normality test revealed the normality of the arm
swing amplitude under the RwoA and RwA conditions (p = 0.082
and p = 0.380, respectively). For the swing amplitude, there wasa significant difference between the RwoA and RwA conditions[t (29) = 8.89, p < 0.001].
Figure 4 shows the samples of the shank trajectories in four
conditions from the same participant. Comparing the RwoA andRwA conditions, the stride lengths under the RwA condition
were larger than that under the RwoA condition (Figure 4A).
Comparing the Pre- and Post-conditions, the stride lengths
under the Post-condition were larger than that of the Precondition (Figure 4B). Figure 5 shows the mean values of thestride length, which were 1.15 ± 0.18, 1.13 ± 0.19, 1.17 ± 0.20,
and 1.19 ± 0.19, respectively in the Pre, RwoA, RwA, and Post
conditions, respectively. A Shaprio-Wilk normality test revealed
the normality of the stride length data for the Pre, RwoA, RwA,
and Post conditions (p = 0.506, p = 0.735, p = 0.723, and p= 0.994, respectively). A one-way repeated ANOVA revealed asignificant difference between the four conditions [F (2.42,70.12) =13.97, p < 0.001]. The results of a post-hoc test show in Table 1.
There was a significant difference between the RwoA and RwA
conditions [t (29) = 3.93, p = 0.001]. There was also a significant
difference between the Pre and Post conditions [t (29) = 4.90,
p < 0.001]. Figure 6 depicts the mean values of the velocity.
The means and SDs of the velocity between the participants
were 1.11 ± 0.18, 1.09 ± 0.20, 1.14 ± 0.21, and 1.18 ± 0.20
in the Pre, RwoA, RwA, and Post conditions, respectively. A

Statistical Analysis
We calculated the means of the indexes using the following
method. Firstly, we calculated the mean value of each evaluationindex on each limb for each trial. Secondly, we calculated themean value of both limbs for each trial. Finally, we calculated themean value of the three trials of the robot assistance condition.
We used the data of the stationary period of gait for analysis.
That is, we discarded the acceleration and deceleration phasesin the first and end phases of gait, respectively. In addition, we
removed the initial phase of the robot assistance from the data

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FIGURE 4 | Sample shank trajectories under the four conditions. This sample trajectories were measured from the same participant. (A) RwoA and RwA conditions,(B) Pre- and Post-conditions. The error bars indicate the standard deviations between the patients.

TABLE 1 | Results of the multiple comparison of the arm swing amplitude, stride
length, velocity, and the stride duration CV.
Conditions

Differencep

Arm swing amplitude (degrees)

RwoA-RwA−20.9<0.001

Stride length (m)Pre-RwoA

0.018

0.151

Pre-RwA−0.0160.151<0.001

Velocity (m/s)

Stride duration CV

FIGURE 5 | Means values of the stride length. *, **, and *** represent p <0.05, p < 0.01, and p < 0.001, respectively. The error bars indicate thestandard deviations between the patients.

Shapiro-Wilk normality test showed the normality of the velocity
data for the Pre, RwoA, RwA, and Post conditions (p = 0.731,
p = 0.214, p = 0.293, and p = 0.565, respectively). A one-way
repeated ANOVA revealed a significant difference between thefour conditions [F (2.39,69.40) = 25.43, p < 0.001]. A post-hoc test
revealed a significant difference between the RwoA and RwA
conditions [t (29) = 4.44, p < 0.001]. Additionally, we found asignificant difference between the Pre and Post conditions [t (29)
= 6.59, p < 0.001]. Figure 7 shows the mean values of thestride duration CV. The means and SDs of the stride duration
CV between the participants were 0.020 ± 0.010, 0.023± 0.016,
0.017 ± 0.009, and 0.017 ± 0.009 in the Pre, RwoA, RwA, andFrontiers in Robotics and AI | www.frontiersin.org

Pre-Post−0.037

RwoA-RwA−0.0340.001

RwoA-Post−0.055<0.001

RwA-Post−0.0210.014

Pre-RwoA

0.019<0.001

Pre-RwA−0.0270.121

Pre-Post−0.068<0.001

RwoA-RwA−0.046<0.001

RwoA-Post−0.087<0.001

RwA-Post−0.046<0.001

Pre-RwoA−0.003

1.000

Pre-RwA

0.004<0.001

Pre-Post

0.003

0.024

RwoA-RwA

0.006<0.001

RwoA-Post

0.006

0.019

RwA-Post−0.001

1.000

Post conditions, respectively. A Shapiro-Wilk normality test did
not reveal the normality of the stride duration CV for the Pre,
RwoA, RwA, or Post conditions (all, p < 0.001). A Friedman
test revealed a significant difference between the four conditions(χ 2 (3) = 17.4, p < 0.001). A post-hoc test revealed a significant
difference between the RwoA and RwA conditions (p = 0.002). In
addition, there was a significant difference between the Pre andPost conditions (p = 0.024).
Figure 8 shows the means of the arm swing amplitude, stride
length, velocity, and stride duration CV for the patients witheach severity. For all indices, the tendency of the difference

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and RwA conditions were decreased compared to the Pre and
RwoA conditions.

DISCUSSION
The purpose of this study was to verify the effect of upperlimb-rhythm assistance on the gait of patients with PD via
synchronization between the patient’s gait and the rhythm of therobot. We verified the two effects of a gait-assistance robot: thegait-assistance effect and after-effect. For the gait-assistance effectand after-effect, we compared the RwoA and RwA conditions,and Pre- and Post-conditions, respectively. The results revealed
a gait-assistance effect and an after-effect. The arm swing
amplitude, stride length, and velocity increased under the RwA
condition compared to the RwoA condition. The stride duration
CV decreased under the RwA condition compared to the RwoA
condition. Additionally, the arm swing amplitude, stride length,and velocity were larger under the Post-condition than under
the Pre-condition. The stride duration CV was smaller under the
Post-condition than under the Pre-condition.
As one of the robot-assistance effects, the patient’s arm swing
was larger under the RwA condition than under the RwoA
condition. Thus, the rhythm synchronization via tactile stimuli
on the patient’s upper limbs improved the patient’s arm swing.
The arm swing of the patients with PD was reduced (Lewek et al.,2010; Mirelman et al., 2016) and this weak arm swing led to
an increased fall risk (Wood et al., 2002). Thus, an increase in
the patients’ arm swing using the proposed robot is expected toreduce their fall risk.
The robot was found to have a gait-assist effect on the lowerlimb movements. The stride length and velocity increased under
the RwA condition when compared to the RwoA condition.
As mentioned above, there are neural couplings between theupper limbs and lower limbs (Huang and Ferris, 2009). In
addition, periodic movements of the upper limbs increased
the muscular activity of the lower limbs (Ferris et al., 2006;
Kawashima et al., 2008). Thus, presenting a synchronizing tactile
stimuli can influence patients’ movements of the lower limbs
via coupling between the upper and lower limbs. Although
the velocity increase may increase the fall risk in the elderly(Van den Bogert et al., 2002), the stride duration CVs, which
was one index for the gait stability and related to the fall risk
(Schaafsma et al., 2003), was smaller in the RwA condition
compared to the RwoA condition. These results suggest thatusing the robot assistance, the velocity would be improved while
the fall risk decreased. Because fall risk assessment using datafrom wearable sensors is still challenging (Patel et al., 2020),
investigation of whether rhythm assistance on the upper limbs
decreases the risk of falls is required in the future. These effects
on the lower limbs were shown in participants with all theseverities (Figure 8). Thus, the robot assistance would improve
the patient’s lower limb movement with both mild and moderate
severity. However, we could not conduct the statistical analysis
for the results of each severity because of the small number ofthe patients for each severity. Thus, the effects of the proposed
method on the gait of the patients with each severity should be

FIGURE 6 | Mean values of the velocity. *** represents p < 0.001. The error
bars indicate the standard deviations between the patients.

FIGURE 7 | Mean values of the stride duration CVs. * and ** represent p <0.05 and p < 0.01, respectively. The error bars indicate the standard
deviations between the patients.

between the conditions was similar to the means for allparticipants. That is, the arm swing amplitude in the RwA
condition was increased compared to the RwoA condition foreach severity. The stride lengths and velocity in the Post andRwA conditions were increased compared to the Pre and RwoA
conditions, respectively. The stride duration CVs in the Post

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FIGURE 8 | Mean values of the arm swing width, stride length, velocity, and stride duration CVs for each severity. (A) Arm swing amplitude, (B) stride length, (C)velocity, (D) stride duration CV. N = 4, 18, 3, and 5 for the patients with mH&Y 1, 2, 2.5, and 3, respectively. The error bars indicate the standard deviations between
the patients.

There were no significant differences between the Pre- andRwA conditions with regard to the stride length and velocity.
Yap et al. revealed that the rhythm assistance provided by the
robot increased the hip swing angle of healthy elderly participants
when compared to pre-intervention (Yap et al., 2019). A reason
for why we did not find a difference in the stride length andvelocity between the Pre- and RwA conditions may be attributed
to the small number of trials conducted. In a previous study (Yapet al., 2019), 15 trials under the robot-rhythm-assist conditions
were conducted for each participant. In contrast, this study only
performed three trials under the robot-rhythm-assist condition
for each patient.

investigated in a larger cohort of patients. These improvements
in lower limb movements are the result of an enhancementof the central pattern generator (CPG) for gait. CPG includes
specialized neural circuits in the spinal cord and generates
the rhythmic movement for gait independently from the brain(Dimitrijevic et al., 1998; Yang et al., 1998). The coupling ofcervical and thoraco-lumbar propriospinal systems coordinates
the upper and lower limbs during walking (Dietz, 2002). The
tactile stimuli on the upper limbs by the robot increased the arm
swing amplitude, which increased the arm movement, enhancing
CPG activation. As the result, the lower limb movement would
be increased.

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the Pre conditions for the patients with mH&Y 2, 2.5, and 3.
In addition, the stride duration CV in the RwoA conditions
was higher than that in the Pre-condition for mH&Y 2.5 and 3.
Thus, the weight of the robot might be heavy for these patients;
however, it is not yet clear because of the small sample size forthe patients with each severity. The decrease in gait performance
would increase fall risk (Schaafsma et al., 2003) and decreaseor cancel out the effects of the robot assistance. In future work,
weight reduction of the robot is required.
There are some limitations in this study. The values of theparameters in the system, α, β, khm , km_rl , θd , and motor
torque were determined by trial and error. Thus, more efficientvalues for each parameter should be investigated in future
work. In addition, these parameters might be different between
individuals. Therefore, the method to determine the values
suitable for individuals should be constructed. For patient safety,
the experimenters walked behind the patients in the trials. Thus,
the patients might hear the footsteps of the experimenter, thus theinfluence of auditory signals has not been completely eliminatedin this experiment. In addition, we could not omit the ordereffect. Although we could not perform more trials due to patientburden, in the future, the repetition effect should be consideredusing another experimental design such as the A-B-A-B design.
In addition, although we conducted three trials for the RwA
conditions, the number of the trials should be the same between
all conditions in the future.
We found that the rhythm synchronization between the
gait of the PD patients and the tactile stimuli on their upper
limbs statistically changed their gait. In future studies, it isnecessary to verify how the rhythm synchronization improved
symptoms in the gait of the patients with PD, as the cause of
gait ability decline in patients with PD is different from that inhealthy elderly individuals (Rocha et al., 2014). The appearanceof symptoms in gait depends on the severity of the conditionand each individual. Therefore, it is important to investigate
the relationship between the gait symptoms of each participant
and the assistance by the proposed robot in a large number ofpatients with PD. In addition, study of the long-term after-effects
and/or intervention for patients with PD using the robot is also
required. Although the after-effects observed in this study, withregard to stride length, velocity, and stride duration CV, were
just immediate effects after gait with the robot assistance device,these results show the possibility of using the proposed robotic
device in gait rehabilitation for patients with PD. Furthermore,
an improvement of patient’s gait function should be investigatedfrom a medical viewpoint using standard clinical tests such as the
10 m walk and dynamic gait indexes.

The stride duration CV was smaller under the RwA condition
than under the RwoA condition. Thus, this result revealed theassist-effect of the proposed robot on gait stability. In addition,
the stride duration CV was smaller under the RwA condition
than under the Pre-condition. Thus, this effect on the stride
duration CV would be higher than the stride length and thevelocity. These results revealed that the synchronized tactile
stimulation increased gait stability in PD patients. Hove et al.
pointed out that the 1/f -like structure in the stride duration
could increase the gait stability (Hove et al., 2012). The 1/f –likestructure makes the time series more predictable, which would
increase the perceived movement stability. In fact, the fractal
scaling is an index of gait stability (Hausdorff, 2009). In our
experiment, the synchronized tactile stimulation on the upper
limbs would reinstate the 1/f -like structure in the stride duration
as is the case with synchronized auditory stimulation. Thus, thestride duration CV decreased in the RwA condition. Note that
we could not calculate the fractal scaling in this study as a much
longer time series is needed; however, it is impossible for theparticipants to walk such a long distance whilst wearing the robot.
Future work should investigate the effect of the robot assistanceon the fractal scaling of the stride duration.
We found the after-effects with regard to the stride length,
velocity, and stride duration CV. The stride length and velocity
were larger under the Post-condition than those under the Precondition. In addition, the stride duration CV was smaller under
the Post-condition than that under the Pre-condition. It wasreported that the stride length and velocity was related to therisk of freezing of gait (Nieuwboer et al., 2001; Chee et al.,2009; Contreras and Grandas, 2012). Therefore, gait training by
providing rhythm assistance on the upper limbs could reduce therisk of freezing of gait. In addition, one of the gait disorders foundin patients with PD is hypokinesia of the lower limbs (Morris
et al., 1994; Hass et al., 2012). The after-effect of increasing
stride length and velocity by the proposed robot is expected to
improve lower limb hypokinesia. Additionally, in patients with
PD, the stride duration CV was higher than that of healthyelderly participants (Weiss et al., 2011), and it was related to fallrisk (Schaafsma et al., 2003). Thus, the after-effect of the stride
duration CV may lead to a reduction in fall risk.
It is important to note that the after-effect observed in thisstudy included the effect of gait under the RwoA condition.
The reason we applied this condition was to observe the effectof wearing the robot. As a result, there were no significant
difference between the Pre- and RwoA conditions for the stride
length, velocity, and stride duration CV, suggesting there was noinfluence from wearing the robot. In future work, it is necessary
to verify the after-effect when excluding the effect of gait while
wearing the robot without rhythm assistance.
We did not observe any significant differences between the Pre
and RwoA conditions for the stride length, velocity, and stride
duration CV (Table 1 and Figures 5–7). This result suggests thatthe weight of the robot did not affect the patient’s gait acrossall participants nor did it increase the fall risk. However, therobot weight might decrease the gait performance of the patients,
especially those with a relatively high severity. The stride lengthand velocity in the RwoA conditions were lower than those in

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CONCLUSION
The purpose of this study was to investigate whether the rhythmassist robot changed the gait of the patients with PD. The
robot produced tactile stimuli on the patient’s upper limbs,
which synchronized with the arm swing of the patients. First,
we verified the gait assistance effect by the proposed robot.
The arm swing amplitude, stride length, and velocity were

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Upper-Limb Rhythm Assistance for PD

ETHICS STATEMENT

increased in the gait with rhythm-assistance provided by the
robot when compared to the gait when wearing the robot
without assistance. In addition, the stride duration CV wassignificantly decreased in the gait with the robot’s rhythm
assistance compared to the gait with wearing the robot without
assistance. Secondly, we found immediate after-effects of therobot’s rhythm assistance. The stride length and velocity wereincreased after the robot intervention when compared to the preintervention. Additionally, the stride duration CV was decreasedin the post-intervention when compared to the pre-intervention.
These results suggest that the rhythm assistance provided by the
robot may play a role in gait rehabilitation for patients with PD.

This experiment was conducted with the approval from Tokyo
Institute of Technology’s Research Ethics Review Committee andKanto Central Hospital Ethics Committee. Informed consent was
obtained in writing from all participants.

AUTHOR CONTRIBUTIONS
TK, TO, RS, MN, and MS conducted the experiments. SO
recruited the participants. TK and RS analyzed the data. All
authors interpreted the results of the analyses. TK drafted themanuscript. All authors gave approval of the final version of themanuscript to be published.

DATA AVAILABILITY STATEMENTFUNDING

The datasets for this article are not publicly available because
Ethics Committee does not allow the disclosure of patient data.
Requests to access the datasets should be directed to Taiki Ogata,ogata@c.citech.ac.jp.

This work was supported by JSPS KAKENHI (Grant
Number JP16K12950).

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Conflict of Interest: MN was employed by Kikuchi Seisakusho Co. Ltd. and MS
was employed by WALK-MATE LAB Co. Ltd.
The remaining authors declare that the research was conducted in the absence ofany commercial or financial relationships that could be construed as a potential
conflict of interest.
Copyright © 2020 Kishi, Ogata, Ora, Shigeyama, Nakayama, Seki, Orimo andMiyake. This is an open-access article distributed under the terms of the Creative
Commons Attribution License (CC BY). The use, distribution or reproduction inother forums is permitted, provided the original author(s) and the copyright owner(s)are credited and that the original publication in this journal is cited, in accordancewith accepted academic practice. No use, distribution or reproduction is permitted
which does not comply with these terms.

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