ORIGINAL RESEARCH
published: 24 April 2019
doi: 10.3389/frobt.2019.00025

Gait-Assist Wearable Robot Using
Interactive Rhythmic Stimulation to
the Upper Limbs
Robin Miao Sin Yap 1*, Ken-ichiro Ogawa 2 , Yuki Hirobe 1 , Terumasa Nagashima 1 ,Masatoshi Seki 3 , Masayuki Nakayama 3 , Ken Ichiryu 3 and Yoshihiro Miyake 2
1

Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Japan,
Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan, 3 Kikuchi Seisakusho Co. Ltd., Hachioji,
Japan

2

Edited by:
Isaac Chairez,
National Polytechnic Institute, Mexico
Reviewed by:
Qianqian Yang,
Sun Yat-sen University, China
Ioannis Georgilas,
University of Bath, United Kingdom*Correspondence:
Robin Miao Sin Yap
robinyms78@gmail.com
Specialty section:
This article was submitted to
Biomedical Robotics,
a section of the journal
Frontiers in Robotics and AI
Received: 18 September 2018
Accepted: 28 March 2019
Published: 24 April 2019
Citation:
Yap RMS, Ogawa K, Hirobe Y,
Nagashima T, Seki M, Nakayama M,
Ichiryu K and Miyake Y (2019)
Gait-Assist Wearable Robot Using
Interactive Rhythmic Stimulation to theUpper Limbs. Front. Robot. AI 6:25.
doi: 10.3389/frobt.2019.00025

Many power-assist wearable exoskeletons have been developed to provide walking
support and gait rehabilitation for elderly subjects and gait-disorder patients. Most
designers have focused on a direct power-assist to the wearer’s lower limbs. However,gait is a coordinated rhythmic movement of four limbs controlled intrinsically by central
pattern generators, with the upper limbs playing an important role in walking. Maintaining
a normal gait can become difficult as a person ages, because of decreases in limb
coordination, stride length, and gait speed. It is known that coordination mechanisms
can be governed by the principle of mutual entrainment, in which synchronization
develops through the interaction between nonlinear phase oscillators in biologicalsystems. This principle led us to hypothesize that interactive rhythmic stimulation toupper-limb movements might compensate for the age-related decline in coordination,
thereby improving the gait in the elderly. To investigate this hypothesis, we developed
a gait-assist wearable exoskeleton that employs interactive rhythmic stimulation to theupper limbs. In particular, we investigated the effects on spatial (i.e., hip-swing amplitude)
and temporal (i.e., hip-swing period) gait parameters by conducting walking experimentswith 12 healthy elderly subjects under one control condition and five upper-limb-assist
conditions, where the output motor torque was applied at five different upper-limb
swing positions. The results showed a statistically significant increase in the mean
hip-swing amplitude, with a mean increment of about 7% between the control andupper-limb-assist conditions. They also showed a statistically significant decrease in themean hip-swing period, with a mean decrement of about 2.3% between the control andone of the upper-limb-assist conditions. Although the increase in the hip-swing amplitude
and the decrease in the hip-swing period were both small, the results indicate thepossibility that interactive rhythmic stimulation to the upper limbs might have a positiveeffect on the gait of the elderly.
Keywords: gait-assist wearable robot, interactive rhythmic stimulation, mutual entrainment, upper limbs, hipswing amplitude, hip-swing period

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INTRODUCTION

an important role in walking (Zehr et al., 2001; Zehr andHaridas, 2003; Frigon et al., 2004; Zehr and Duysens, 2004). It
is therefore reasonable to consider intervening in the upper-limb
movements to support the lower-limb movements. For example,
the medical exoskeleton, eLEGS (California, USA) applies aninertial measurement unit to the upper limbs of patients withspinal cord injuries to assist the patient to stand up, walk and sit
down independently, based on intention signals from the patient(Strausser and Kazerooni, 2011). However, eLEGS simply applies
inertial measurements to the patients’ upper limbs to correct their
gait trajectories without considering fully the global dynamics
of upper- and lower-limb coordination in human locomotion(Delwaide et al., 1977; Baldissera et al., 1982, 1988; Guadagnoli
et al., 2000; Dietz et al., 2001; Huang and Ferris, 2004, 2009;
Kawashima et al., 2008).
To investigate this upper-limb intervention hypothesis, wehave developed a gait-assist system called the WalkMate(Tokyo, Japan). This system provides subjects with auditory
stimulation as an interactive rhythmic stimulation to establish
synchronization between the stimulation and their walking
footsteps. The stimulation is generated by the interaction
between the artificial nonlinear phase oscillators and the subject’s
walking footsteps and is based on the principle of mutual
entrainment (Miyake, 2009). This principle explains biologicalorganization phenomena in which a set of nonlinear phaseoscillators become synchronized by interaction via appropriate
coupling (Kuramoto, 1984). Such biological phenomena havebeen observed in many internal parts of living organisms,
such as segmental oscillators in the lamprey spinal generator(Cohen et al., 1982), swimming patterns of aquatic animals(Yuasa and Ito, 1990), and the coordinated movements ofbipedal human locomotion (Taga et al., 1991). Therefore, the
WalkMate enables us to test the hypothesis that the upper limbs’movement can affect the lower limbs’ movement by applying
interactive rhythmic stimulation to the upper limbs from theinteraction between upper limbs and lower limbs. There is thenthe possibility that applying appropriate interactive rhythmic
stimulation to the subjects’ upper limbs could improve theirlower limb gait patterns.
In this study, we aimed to implement the WalkMate as awearable exoskeleton that could provide interactive rhythmic
stimulation to the upper limbs. Then, to investigate ourhypothesis, we measured the effect of interactive rhythmic
stimulation to the upper limbs on spatial (i.e., hip-swing
amplitude) and temporal (i.e., hip-swing period) gait parameters.
Our experiments involved overground walking by healthy elderly
subjects under a baseline control condition and five upper-limbassist conditions with different motor-torque outing timings.
Here, the timings correspond to the onset of an output motor
torque at different arm-swing positions of the upper limbs.

In recent years, many power-assist wearable exoskeletons
have been developed to provide walking support and gait
rehabilitation for elderly subjects (Kawamoto et al., 2003; Denget al., 2017; Choi et al., 2018) and gait-disorder patients (Riener
et al., 2005; Veneman et al., 2007; Kim et al., 2010; Strausser andKazerooni, 2011; Barbareschi et al., 2015; Bortole et al., 2015;
Chen et al., 2017; Lerner et al., 2017). Most of these exoskeletons
provide direct power-assist support to the wearer’s lower limbs.
For example, the hybrid assistive leg, HAL (Tsukuba, Japan)
uses the patient’s intention-based electromyography signals toprovide power-assist support to the lower limbs of patientswith a gait disorder (Kawamoto et al., 2003). For patientswith neurological disorders, the robotic-gait orthosis, Lokomat(Hacoma AG, Switzerland) provides task-oriented repetitive
movement using a patient-cooperative strategy based on
impedance, adaptive control, and visual biofeedback to control
the hip and knee joint trajectories on a treadmill (Riener et al.,2005). The lower-extremity powered exoskeleton, LOPES (EA
Enschede, The Netherlands) offers both patient-in-charge androbot-in-charge modes to control the patient’s hip and knee
trajectories on a treadmill (Veneman et al., 2007). The active leg
exoskeleton, ALEX (Delaware, USA) uses a force-field controller
and visual guidance on a treadmill to control hip and knee joint
trajectories (Kim et al., 2010).
More recently, the lower limb robotic exoskeletons, REX(Florida, USA) (Barbareschi et al., 2015), H2 (Technaid S.L.,
Spain) (Bortole et al., 2015), active power-assist lower limb, APAL
(Harbin, China) (Deng et al., 2017), CUHK-EXO (Hongkong,
China) (Chen et al., 2017), novel powered lower-extremity
exoskeleton (Maryland, USA) (Lerner et al., 2017), and gaitenhancing mechatronic system, GEMS (Samsung, Korea) (Choiet al., 2018) have been developed. The REX exoskeleton
uses a touch-panel graphical user interface for the patient inproviding an output torque to the lower limbs to achievetarget performance. The H2 robotic exoskeleton provides output
torque to the patient’s lower limbs using a force-field controller
and improvement in the patient’s walking functions has beenreported. The APAL exoskeleton provides output torque to thewearer’s lower limbs from the torso posture and joint angles
obtained from the inertial measurement units (IMU) to enable
the user to walk on rough ground, climb stairs, and crossobstacles. The CUHK-EXO exoskeleton provides assistive torqueto the patient’s lower limbs using position control and was able
to support the patient’s stand up/sit down and walking motions.
The novel powered lower-extremity exoskeleton provides output
torque to the patient’s knee joint using a proportional-integralderivative control algorithm and an improvement in the hip andknee joint angle was reported. The GEMS exoskeleton assists
elderly patients with walking difficulty by providing an output
torque to the hip using particularly shaped adaptive oscillators.
Although many power-assist wearable exoskeletons have beendeveloped, these exoskeletons aim to provide direct torque tothe lower limbs to assist the patients’ gait. However, gait isa coordinated movement of four limbs intrinsically controlled
by central pattern generators, with the upper limbs playing

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MATERIALS AND METHODS
Gait-Assist Wearable Exoskeleton
The WalkMate gait-assist wearable exoskeleton comprises (1)hardware to provide motor torque (rhythmic stimulation) to theleft and right upper limbs of a subject, triggered by rhythmic

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signals, and (2) software to generate the rhythmic signals insynchronization with the walking rhythm of the subject based on
the mutual entrainment principle.

Hardware
The hardware comprises an actuator module, a control module,
and a power module. Its overall weight is 5.8 kg. The actuator
module is rigidly attached to an adjustable harness that can be
secured to the upper limbs (between the elbow and shoulder
joint) using a Velcro belt. The wearable exoskeleton is rigidly
secured to the upper body (i.e., chest) and lower body (i.e.,
waist) of the subject using adjustable belts. Figures 1A,B show
the appearance and schematic diagram of the hardware moduleof the wearable exoskeleton, respectively.

Actuator ModuleThis module comprises two three-phase DC brushless motors(DR-4316-X14B00420; Shinano Kenshi, Nagano, Japan), each ofwhich provides output torque to the upper limbs, adjustableharnesses, and a Velcro belt. The motors have a drive voltage of
24.0 V, a rated load current of 2.0 A, and a maximum thrust loadof 3.8 N.

Control ModuleThis module comprises two upper-limb DC motor controllers,
two upper-limb DC motor encoders, and an I/O unit. The
encoders provide the control interface between the upper limbs
and the I/O unit by detecting the subject’s shoulder-joint angulardisplacement and sending this information to the I/O unit.
The controllers provide the control interface between the I/O
unit and the upper-limb DC motors by causing the motors
to output the desired torque to the subject’s upper limbs. An
Android smartphone (ASUS Z00ED; Zenfone, Taiwan) controls
the magnitude and phase of the output motor torque to theupper limbs though wireless communication with the I/O unit
via Bluetooth.

Power ModuleThis module comprises an external power unit
(7LPP545483AHR-1M01-WS; Hitachi, Japan) and an external
on/off switch strapped to the adjustable belt at the waist level.
The rechargeable external power unit provides power to thecontrol module and drives the upper-limb DC motors. The
external on/off switch acts as an emergency switch to enable
the subject to override the human operator by switching off
the motor torque to the upper limbs in case of an emergency
or discomfort.

FIGURE 1 | Overview of the gait-assist wearable exoskeleton: (A) hardware
appearance, (B) schematic diagram of the hardware.

output motor torques and outputs the desired magnitude andphase information to the subject. Figure 2A shows a schematic
diagram of the software for the WalkMate.

SoftwarePhase Control Module

The software comprises three modules. Module 1 (phase control
module) controls the left and right upper-limb motor phases (i.e.,their timing) by coordinating the phase differences of the left andright upper limbs and the upper-limb motors to a target value
based on the mutual entrainment principle. Module 2 (subject
phase-input module) receives both of the subject’s upper-limb
angular displacements in real time via the upper-limb motor
encoders. Finally, Module 3 (motor-torque output module)
controls the magnitude and phase for each of the upper-limb

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This module comprises the mutual entrainment submoduleand the phase control submodule. The mutual entrainment
submodule controls the left upper-limb motor phases θm,l suchthat the phase difference 1θm,l between the left upper-limb
arm-swing phase θh,l and the left upper-limb motor phase θm,lconverges to 0◦ , where 1θm,l = θh,l – θm,l . The phase θh,l is definedas the angle measured from the rearmost position of the left
upper limb, with the rearmost position corresponding to 0◦ . The

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FIGURE 2 | Schematic diagram of the software: (A) schematic diagram of the software modules, (B) definition of all phases and phase differences used in the phase
control module, (C) flowchart of the software control algorithm.

The phase control submodule controls the intrinsic angularfrequencies ωm,l and ωm,r of the upper-limb motors by
converging the phase differences 1θm,l and 1θm,r to a target
phase difference 1θd . This operation of this submodule can be
represented as:phase θm,l is defined as the motor phase that corresponds to theangle measured from the rearmost position. The submodule also
controls the right upper-limb motor phase in the same manner,in terms of the variables θm,r , 1θm,r , and θh,r .
The phase control submodule comprises two nonlinear
coupled oscillators based on the Kuramoto model (Kuramoto,1984), which can be represented as:θ̇m,l = ωm,l + Klr sin θm,r − θm,l + Km sin 1θm,l ,θ̇m,r = ωm,r + Klr sin θm,l − θm,r + Km sin 1θm,r .

(1)
(2)

(3)

ω̇m,r = εsin(1θm,r − 1θd ).

(4)Here, ε (>0) is a control gain. In this study, we initialize ε to0.16, 1θd to 0◦ , and both ωm,l and ωm,r to 4.0 rad/s, because thefrequency of complete human gait cycles is about 1.0 Hz.

Here, ωm,l and ωm,r are the variable intrinsic angular frequenciesof the upper-limb motors. Klr is the coupling between the left andright upper-limb motor phases and Km denotes the strength ofthe phase difference convergence between the upper limbs andmotors. In this study, we set Klr = 5.0 and Km = 0.5, following
the parameter values used in the previous WalkMate framework(Miyake, 2009). Figure 2B is a schematic diagram showing therelationships between all phases and phase differences used in thephase control module.

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ω̇m,l = εsin(1θm,l − 1θd ),Subject Phase-input ModuleThis module receives the subject’s upper-limb angulardisplacement from the upper-limb DC motor encoders.
The control algorithm comprises four steps. First, both upperlimb motors are activated after four complete arm-swing cycles.
At that time, both upper limbs are at their rearmost position (i.e.,θh,l = 0◦ and θh,r = 0◦ ). Second, the phase differences between

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at their own natural speed with a natural arm swing. This
process established a baseline called the “free condition”(Data Sheet 2). Three complete trials were conducted under thiscondition. Second, three experimental sessions were conducted.
In each experimental session, the subjects put on the wearable
exoskeleton and walked at their own natural walking speed
under upper-limb-assist conditions involving five different lag
times (0, 10, 20, 30, and 40%) conducted randomly. For eachcondition, the subjects walked the same horizontal distance of
55.4 m along the corridor. Three complete trials were conducted
for each condition. There was no rest time given between thefirst and second experimental sessions, but a rest time of about
5 min was given between the second and third sessions. The
experiment ended when the subjects had completed all threeexperimental sessions.
During the experiment, each subject also wore a wearable
sensor (TSND121; ATR-Promotion, Japan) on each of his lower
limbs. The sensor comprised an accelerometer and a gyroscope
capable of measuring acceleration and angular velocity in all
three dimensions. The sensor was rigidly secured to the lower
limb using elastic Velcro belts at a vertical height of 15.0 cm
above the knee joint on the sagittal plane. The time-series datafrom the wearable sensors were recorded during the experimentusing a sampling frequency of 100 Hz and then transmitted toa portable laptop computer (Dell Latitude, E5440; Dell, USA)
via Bluetooth for off-line analysis. The sampling precision of
the angular velocity is 0.01 degree per second. The time taken
for each subject to complete each experimental trial was alsomeasured using a digital stopwatch (HS44-001; Citizen, Japan)
to calculate the average walking speed of each subject.

each upper-limb phase and its corresponding upper-limb motor
phase, (i.e., 1θm,l and 1θm,r ) are updated in real time such thatthe phase differences converge to the target phase difference1θd = 0◦ . Third, the intrinsic angular frequencies of both
upper-limb motors, ωm,l and ωm,r , are updated using Equations(3) and (4) with the phase differences being updated from thesecond step. Fourth, the upper-limb motor phases θm,l andθm,r are updated using Equations (1) and (2) with the intrinsic
angular frequencies updated from the third step. This cycle
repeats from the second to the fourth steps during walking.
Finally, the upper-limb motors stop when the operator switches
off the exoskeleton using the Android phone controller or theend-user switches it off using the external switch. Figure 2C
represents a flowchart of the software control algorithm for thewearable exoskeleton.

Motor-Torque Output ModuleThis module follows each arm swing during one complete humangait cycle with temporal control of the motor torque. The upperlimb motor torque is applied at the contact point between theupper limb and the adjustable harness (i.e., just above the elbow
joint). The phase of the output motor torque differs from thesubject’s arm-swing phase with respect to the rearmost position
by an amount (i.e., lag time) that can be varied from 0 to 50% of
one complete arm-swing cycle. The upper-limb-assist conditions
for 0 and 50% lag times represent the output motor torquebeing applied to the upper limb at its rearmost and foremost
positions, respectively. However, because the foremost position
corresponds to the exact time that the arm reverses its swing
direction, using a 50% lag time might cause instability in thesubject. We therefore restricted the maximum lag time to 40%
in this study, where the upper limb is at an arm-swing position
between the subject’s frontal plane and the limb’s foremost
position. Figures 3A–F show schematically the upper-limb-assist
conditions for various lag times.

Gait Analysis
We performed a gait analysis of the left and right hip-swing
angular displacements and hip-swing periods using the timeseries data obtained from the wearable sensors on the left andright lower limbs to evaluate the effect of the interactive rhythmic
stimulation applied to the upper limbs under all conditions. The
gait analysis comprised eight steps (Data Sheet 1).
First, we applied a fourth-order zero-phase shift Butterworth
low-pass filter with a cut-off frequency of 6.0 Hz to the timeseries data for the hip-swing angular velocity in the sagittal plane,
because it has been reported that the frequencies for normal gait
are within a narrow band, with the upper limit between 4.0–
6.0 Hz (Winter et al., 1974; Angeloni et al., 1994). Second, we
performed a simple numerical integration of the time-series dataon the hip-swing angular velocities using the trapezoidal rule toobtain the left and right hip-swing angular displacements θz,l (t)and θz,r (t). Third, we applied a small recursive filter to θz,l (t)and θz,r (t) to correct for the accumulation drift error causedby the numerical integration in the second step. Fourth, we
extracted each peak and trough of the corrected time-series data
for the hip-swing angular displacements using a peak-detection
algorithm to calculate the peak-to-peak hip-swing amplitudes
for all complete stable gait cycles in each complete trial. Fifth,
we calculated the mean peak-to-peak amplitude of the timeseries data for the left and right hip-swing angular displacements,
removing the first and last five gait cycles because of acceleration

EXPERIMENT
Ethics Statement
The experiment protocol in this study was approved by the
Ethics Committee at the Tokyo Institute of Technology throughwritten consent. We recruited healthy elderly subjects from
the Machida Silver Center in Tokyo, Japan. They were all freefrom documented neurological disorders. Before the start of theexperiment, all participants were briefed about the experimental
procedures and written informed consent was obtained.

Participants
We evaluated the wearable exoskeleton by conducting walking
experiments with 12 healthy elderly male subjects. The mean
age of the subjects was 74.5 ± 2.6 years. Their mean height
and weight were 166.2 ± 5.1 cm and 63.3 ± 10.1 kg, respectively(Data Sheet 2).

Experimental Procedure
The experimental procedure comprised two steps. First, thesubjects walked a horizontal distance of 55.4 m along a corridor

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FIGURE 3 | Schematic diagram of the upper-limb-assist conditions for various lag times: (A) 0% lag time, (B) 10% lag time, (C) 20% lag time, (D) 30% lag time,(E) 40% lag time, (F) 50% lag time.

that the peak-to-peak amplitude for each of the right and left hipswing angular displacements is higher for the upper-limb-assist
condition with a 40% lag time than for the free condition.
Figure 5A shows the results of the statistical analysisof the mean hip-swing amplitude for the elderly subjects(Data Sheet 2). Table 1 gives the mean hip-swing amplitude andthe mean hip-swing period for each experimental condition, andthe p-value for the upper-limb-assist conditions with respect tothe free condition. A Friedman test revealed a significant effectof upper-limb conditions on the hip-swing amplitude [A2 (5)= 27.1, p < 0.01]. A post-hoc test using Wilcoxon signed-rank
test with Bonferroni correction showed a statistically significant
difference between the free condition and each of the upper-limbassist conditions with respect to the mean hip-swing amplitudeunder all conditions (p = 0.0024). In particular, the mean hipswing amplitude shows a statistically significant increase of about
2.8◦ from the free condition to each of the upper-limb-assist
conditions. The results show an increase in the mean hip-swing
amplitude for each of the upper-limb-assist conditions against
the free condition.
Figure 5B shows the results of the statistical analysis of
the mean hip-swing period for the elderly subjects (File S5).
A Friedman test revealed a significant effect of upper-limb
conditions on the hip-swing period (A2 (5) = 17.2, p = 0.00412).
A post hoc test using Wilcoxon signed-rank test with Bonferroni
correction showed a statistically significant difference between
the free condition and the upper-limb-assist condition witha 40% lag time (p = 0.024), where the mean hip-swing
period decreased on average by 23 ms. However, no statisticallysignificant differences were observed for other upper-limb-assist
conditions (p = 0.088, 0.081, 0.061, and 0.061, respectively).

and deceleration issues, to obtain the mean left and right hipswing amplitudes. Sixth, we calculated the mean hip-swing
amplitude as the mean value of the mean left and right hip-swing
amplitudes. Seventh, we calculated the time difference between
two consecutive peaks and troughs of the time-series data forthe left and right hip-swing angular displacements to calculate themean left and right hip-swing periods. Finally, we calculated themean hip-swing period, which is the mean of the mean left andright hip-swing periods. Postprocessing of the time-series datafrom the wearable sensors was performed using the Scientific
Computing Library, SciPy in Python (version 2.7) and numericalanalysis of the time-series data was performed using a Microsoft
Excel spreadsheet.

Statistical Analysis
Statistical analysis comprised a two-step process. First, we
performed a statistical analysis of the mean hip-swing angulardisplacement and the mean hip-swing period between the freecondition and each of the upper-limb-assist conditions, using
the Friedman test because of the nonparametric distribution of
the numerical data (n = 12). Second, if a statistically significant
difference existed from the previous test, we performed a multiple
pairwise Wilcoxon signed-rank test. To address the type-I andtype-II errors introduced as a result of multiple pairwise testing,
we performed a correction to the statistical test using the
Bonferroni correction (Bland and Altman, 1995). All statisticalanalyses were performed using R (version 3.3.3). A statisticallysignificant difference between the free condition and each of theupper-limb-assist conditions was confirmed at p < 0.05.

RESULTSDISCUSSION

Figures 4A–D shows examples of the time-series data for both
the right and left hip-swing angular displacements, for both thefree condition and the upper-limb-assist condition with a 40% lagtime, for one elderly subject (Data Sheet 2). These results show

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The gait-assist wearable exoskeleton developed in this study,
the WalkMate, applied interactive rhythmic stimulation to the6

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FIGURE 4 | Example of the time-series data for the right and left hip-swing angular displacements for an elderly subject: (A) right hip-swing angular displacement
under the free condition, (B) right hip-swing angular displacement under the upper-limb-assist condition with a 40% lag time, (C) left hip-swing angular displacement
under the free condition, (D) left hip-swing angular displacement under the upper-limb-assist condition with a 40% lag time.

walking at the same speed (DeVita and Hortobagyi, 2000). We
feel that it is important to assist healthy elderly subjects to regain
their normal gait ability as a result of aging to assist them intheir activities of daily living. In this regard, we consider our
gait-assist wearable exoskeleton able to meet this objective andencourage voluntary efforts (i.e., active mode) by the subjects,
thereby providing appropriate support to the lower limbs.
Although many power-assist wearable exoskeletons have beendeveloped, these exoskeletons aim to provide a direct torqueto the wearer’s lower limb by controlling the gait trajectory toreach a target trajectory (Kawamoto et al., 2003; Riener et al.,2005; Veneman et al., 2007; Kim et al., 2010; Barbareschi et al.,2015; Bortole et al., 2015; Chen et al., 2017; Deng et al., 2017;
Lerner et al., 2017; Choi et al., 2018). Moreover, there is no directcomparison between the baseline condition (i.e., free walking)and the robot-assist condition. Hence, the effectiveness of thedirect power-assist support of these wearable exoskeletons to thelower limbs remains unclear.
In contrast, in this study, we conducted walking experimentswith healthy elderly subjects using the baseline condition (i.e.,elderly subjects’ upper limbs, aiming to support their gait basedon the principle of mutual entrainment (i.e., interpersonal
coordination) in human–robot interaction and upper–lower–limbs’ neural coupling (i.e., intrapersonal coordination) as
mediated by central pattern generators. We postulate that thisapproach would provide support to the lower limbs based on
the intrinsic mechanisms of upper–lower–limbs’ coordinationin human locomotion. This support is in contrast with that ofprevious power-assist wearable exoskeletons that aim to provide
direct torque to the lower limbs. To verify our hypothesis,
we investigated the effect of such stimulation on spatial (i.e.,
hip-swing amplitude) and temporal (i.e., hip-swing period) gaitparameters by conducting walking experiments with healthyelderly subjects.
It has been reported that changes in gait pattern withincreasing age are associated with decreasing muscle strength andthat there is a need for increased stability during locomotion withincreasing age (Nigg et al., 1994). In addition, it has been reported
that age causes a redistribution of joint torques and powers, withthe elderly using their hip extensors more than young adults

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FIGURE 5 | Statistical analysis of the mean hip-swing amplitude and the mean hip-swing period for the free condition and for each of the upper-limb-assist conditions
with elderly subjects: (A) mean hip-swing amplitude, (B) mean hip-swing period (*p < 0.05; **p < 0.01).

effectively increase the hip-swing amplitude for elderly adults’
gait to be comparable to the gait of healthy young adults.
In addition, Lerner et al. (2017) reported that power-assist
support to the wearer’s knee joint showed a statistically significant
increase in the peak hip extension of children with cerebral palsy
by 8◦ between the baseline walking condition (i.e., free walking)and the power-assist condition. Although the increase in themean hip-swing amplitude in our present study is smaller thanthat measured in the previous study (Lerner et al., 2017), it
should be noted that all the participants in the previous study
are unhealthy subjects, whereas all our participants are healthy
subjects. Hence, the result supports the hypothesis that applying
appropriate interactive rhythmic stimulation to the elderly adult’s
upper limbs would affect their lower limbs’ gait patterns as
manifested through an increase in the hip-swing amplitude.
We can speculate from the results that the mutual entrainment
resulting from such stimulation could activate the central patterngenerators of the upper limbs, thereby stimulating the lower
limbs as mediated through a cooperative relationship (intrasynchronization) between the upper and lower limbs. Such a
relationship has been reported in terms of upper–1ower–imbs’
neural coupling in many previous studies (Delwaide et al., 1977;
Baldissera et al., 1982, 1988; Guadagnoli et al., 2000; Dietzet al., 2001; Zehr et al., 2001; Zehr and Haridas, 2003; Frigon
et al., 2004; Huang and Ferris, 2004, 2009; Zehr and Duysens,
2004; Kawashima et al., 2008; Strausser and Kazerooni, 2011).
In particular, the soleus muscles of the lower limbs could be
activated (Huang and Ferris, 2004, 2009; Kawashima et al., 2008)
and the presynaptic inhibition could be suppressed (Zehr andDuysens, 2004) by the stimulation of the upper limbs via theupper–lower–limbs’ neural coupling.
In a second main result, we observed a statistically significant
decrease in the mean hip-swing period of the subjects for theupper-limb-assist condition with a 40% lag time compared withTABLE 1 | Mean and p-values for the mean hip-swing amplitude and the mean
hip-swing period for each experimental condition.
ParameterHip-swing amplitude (◦ )Hip-swing period (ms)Condition

Mean (±SD)

p-valueFree

38.90 (± 5.24)–

0% lag time

41.62 (± 5.16)

0.0024**

10% lag time

41.63 (± 5.10)

0.0024**20% lag time

41.72 (± 5.04)

0.0024**30% lag time

41.69 (± 4.98)

0.0024**40% lag time

41.60 (± 4.94)

0.0024**

Free

1000.5 (± 60.4)–

0% lag time982.3 (± 67.5)

0.088

10% lag time

980.1 (± 66.8)

0.081

20% lag time

978.0 (± 66.7)

0.061

30% lag time

974.9 (± 67.3)

0.061

40% lag time

977.1 (± 67.3)

0.024**p < 0.05; **p < 0.01.

free walking) and upper-limb-assist conditions at different lag
times. The results showed a statistically significant increase in
the mean hip-swing angular displacement for all upper-limbassist conditions compared with the free condition, with a mean
increment of about 7% (i.e., 2.8◦ ). It has been reported that thestep length was 4% shorter in healthy elderly adults comparedwith healthy young adults (DeVita and Hortobagyi, 2000). In
addition, it has been reported that the stride length was 6.7%
(Ostrosky et al., 1994) and 10% (Winter et al., 1990) shorterin healthy elderly adults compared with healthy young adults,
respectively. In this study, we observed a statistically significant
increase in the hip-swing amplitude by about 7% between the freecondition and each upper-limb-assist condition at different lag
times with the healthy elderly subjects. Hence, the result indicatesthat interactive rhythmic stimulation to the upper limbs could

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Gait-Assist Robot to Upper Limbs

sessions over a longer time frame including “free” walking
experiments immediately after walking experiments using thewearable exoskeleton (i.e., “wash-out” effect).
Despite the aforementioned limitations, our results suggestthat the WalkMate gait-assist wearable exoskeleton succeeds ingenerating interactive rhythmic stimulation in synchronization
with an elderly subject’s arm swing, based on the mutual
entrainment principle. Moreover, this principle plays animportant role in establishing intrapersonal coordination
between the upper and lower limbs of the subjects. In addition,
it should also be noted that the autonomous gait ability ofsubjects has been an important aspect of the establishment of
this principle.
We have three research plans for the future. First, we recruited
male-only subjects for this study. Because there are genderdifferences in gait patterns because of a different range of bodily
mass and the position of the center of gravity, we will evaluate
the effect of stimulation to the upper limbs on female subjects’gait. Second, we plan to conduct experiments on walking for
longer periods to determine the “wash-out” effects of the upperlimb stimulation. Finally, it has been reported that elderly adults
with lower extremity dysfunction rely excessively on the passive
action of the hip flexors to provide propulsion in the late stanceto enhance stability (McGibbon and Krebs, 2004). Hence, we aim
to extend our experiments to this group of elderly adults as wellas patients with neurological disorders such as hemiplegia andParkinson’s disease.

the free condition. However, there is no statistically significant
difference between the upper-limb-assist condition with a 0,
10, 20, and 30% lag time, and the free condition. It has beenreported that the stride velocity of healthy elderly adults is
7.9% lower than that of healthy young adults (Ostrosky et al.,1994). In addition, it has been reported that there are 7.7%
(DeVita and Hortobagyi, 2000) and 2.3% (Krasovsky et al., 2014)
reported decreases in the swing time of healthy elderly adults
compared with healthy young adults. In this study, we observed
a statistically significant decrease in the hip-swing period by 2.3%
(i.e., 23 ms) between the free condition compared with the upperlimb-assist condition at a 40% lag time. Although the decrease in
the hip-swing period (i.e., increase in the gait speed) is smaller inour present study compared with that measured by DeVita andHortobagyi (2000), it is comparable with the results presented by
Krasovsky et al. (2014). Hence, the result indicates that interactive
rhythmic stimulation to the upper limbs could increase the gait
speed of the elderly to be comparable to the gait of healthy
young adults.
Although this decrease in the hip-swing period is small,the result indicates that interactive rhythmic stimulation to theupper limbs at an optimal arm-swing position can increase thearm-swing activity. Because it has been reported in previous
studies that an increase in arm-swing activity increases thegait speed (Eke-Okoro et al., 1997; Marks, 1997; Long et al.,2011), the results indicate the possibility that interactive rhythmic
stimulation to the upper limbs could increase the gait speedwith these healthy elderly subjects due to an increase in armswing activity, particularly at a 40% lag time. Moreover, it has
been reported in a previous study that postural changes of theupper limb affect the reflex transmission of the lower limbs,
which is maximal when the upper limb is at an angle of 45◦ withrespect to the frontal plane (Delwaide et al., 1977). This position
corresponds to the arm-swing position of the upper limbs at a
40% lag time in our study. It would be possible that stimulation
at this arm-swing position could activate the soleus reflex of thelower limbs significantly, again leading to increased gait speed.
Therefore, the effect of the stimulation on the gait speed might be
optimized for the 40% lag time.
However, there are some limitations in our present study.
First, the weight of the gait-assist wearable exoskeleton used
is heavy (5.8 kg) for the elderly subjects. Although our results
showed a statistically significant increase in the hip-swing
amplitude and gait speed of these subjects, the marginal change
indicates that the weight of the wearable exoskeleton might havecounteracted the benefits of the interactive rhythmic stimulation.
Because of this possibility, the development of a lightweight
gait-assist wearable exoskeleton for the elderly is underway.
Second, the sample size of our elderly subjects (n = 12) can
be considered small. Our preliminary results showed that someinter-subject variability exists even for healthy elderly subjects
in the hip-swing amplitude and hip-swing period. Thus, there
is a need to increase the sample size of the elderly subjects
to minimize the effect of this variability. Third, we have only
conducted three experimental sessions for each experimental
condition on the same day. In order to account for the effectof neuroplasticity, there is a need to conduct more experimental

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ETHICS STATEMENT
The experiment protocol in this study was approved by the
Ethics Committee at the Tokyo Institute of Technology throughwritten consent. We recruited healthy elderly subjects from
the Machida Silver Center in Tokyo, Japan. They were all freefrom documented neurological disorders. Before the start of theexperiment, all participants were briefed about the experimental
procedures and written informed consent was obtained.

AUTHOR CONTRIBUTIONS
RY, TN, KO, and YM: conceptualization; RY: data curation; RY:
formal analysis; YM: funding acquisition; RY: investigation; RY,
TN, KO, and YM: methodology; YM: project administration;
YH, MS, MN, and KI: resource; YH, MS, and MN: software; KO
and YM: supervision; RY: validation, visualization, and writing(original draft preparation); RY, KO, and YM: writing (review
and editing).

FUNDING
The funder (Kikuchi Seisakusho Co. Ltd) provided support in theform of salaries for authors MS, MN, and KI, but did not have any
additional role in the study design, data collection, and analysis,
decision to publish, or preparation of the manuscript. This work
was supported by grant-in-aid for scientific research (16K12950).

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Gait-Assist Robot to Upper Limbs

ACKNOWLEDGMENTS

SUPPLEMENTARY MATERIAL

The authors would like to thank Dr. Leo Ota at
the University of Tsukuba for his helpful advice on
this study.

The Supplementary Material for this article can be foundonline at: https://www.frontiersin.org/articles/10.3389/frobt.
2019.00025/full#supplementary-material

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Conflict of Interest Statement: MS, MN, and KI were employed by company
Kikuchi Seisakusho 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 © 2019 Yap, Ogawa, Hirobe, Nagashima, Seki, Nakayama, Ichiryu 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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