Proceedings of the 2021 IEEE International
Conference on Intelligence and Safety for Robotics
Nagoya, Japan, March 4-6, 2021

Effect of different gait phase-based assist patterns of a wearable robot
on gait motion
Kiichi Kondo1 , Yasuhiro Akiyama1 , Shogo Okamoto1 and Yoji Yamada1
Abstract— Wearable walking assist robots are expected toimprove the quality of life of the elderly, and a few such robots
have been developed recently. However, the human response tosuch robots depending on the assist pattern must be evaluated.
We aimed to compare the gait under multiple assist patterns todetermine what assist pattern is effective in improving gait. We
developed two assist patterns related to human motion or torqueexerted on the gait cycle. We conducted an experiment using
our exoskeleton MALO and measured the joint angle, muscle
activity, and interaction force. As a result, we found that hip
and knee ﬂexion is assisted by our robot. The maximum ﬂexion
angle of the knee joint was high in motion-based assist, and adecrease in biceps femoris and gluteus maximus muscle activity
was observed in torque-based assistance.

I. INTRODUCTION
Presently, many developed countries have a high elderly
population. A wearable walking robot is expected to improve
the quality of life of the elderly. Wearable walking assist
robots such as HAL [1] and HONDA Rhythm Assist [2]have been developed. The HAL detects a person’s motion
intention through myoelectricity and assists the wearer inmoving their joint toward the intended direction. In contrast,
the HONDA Rhythm Assist assists the user by estimating thegait phase and gait ratio to maintain a constant gait ratio. In
this way, all assistive robots have a different assist strategy;
they have been shown to be effective in assisting.
The relationship between the assist pattern and the change
in gait motion has not been compared or evaluated on asingle device. The human response to such robots must beevaluated depending on the timing of the application of theassist torque or the differences in joints to design future assist
robots [3].
The aim of this study was to compare the gait undermultiple assist patterns to determine which assist pattern iseffective in improving the gait. We examined whether assistance decreases the energy required for gait and facilitateswalking motion.
II. M ETHODSFig. 1.

Experimental conditions

surface electromyography (TeleMyo DTS EM-801, Noraxon)
during walking. The surface electromyograph (EMG) wasattached to the subject’s right leg. To measure the activityof the hip and knee ﬂexors, surface EMG electrodes wereattached to the sartorial muscles, gluteus maximus, biceps
femoris, and rectus femoris. To measure the interaction force
between the body and the robot, a six-axis force sensor(WEF-6A200-4-RCD, Wako-tech Inc.) was attached to therobot. This force sensor was attached to the thigh and lowerleg cuffs.
The physical assistance robot, MALO, which includes acorset and long leg braces, is shown in Fig. 2. It has one
degree of freedom in the hip, knee, and ankle joints. Therobot is ﬁxed to the wearer via the corset that is ﬁxed tothe torso, belts attached to the thigh and shank, and shoes.
The hip and knee joints of the MALO are equipped with DCmotors (Maxon RE40), which can apply sagittal torque to therespective joints. To detect the gait phase and the gait cycle,
a portable force plate (M3D-FP Tec-Gihan) was attached tothe soles of the MALO.

A. Apparatus

B. Assist pattern

The experimental scene is shown in Fig. 1. Subjects
walked on a treadmill for 90 seconds per trial. The walking
motion of the subjects was measured using a motion capturesystem (Motion Analysis, MAC 3D System). The markers
were attached to the subjects, and the hip and knee joint
angles were calculated. Muscle activity was measured via

The assist pattern was designed based on the periodicityof the gait cycle. The gait phase was determined by detecting
the heel contact using a force plate. The maximum torqueapplied by the assist was 14 Nm at the hip joint and 11 Nmat the knee joint.
Furthermore, few previous studies have focused on theeffect on gait by changing the assist timing of each joint
parametrically [4][5]. In this study, two patterns of assistancewere developed based on the following hypothesis.

1 Kiichi Kondo, Yasuhiro Akiyama, Shogo Okamoto and Yoji Yamada
are with Faculty of Mechanical Systems Engineering, Nagoya University,
Aichi Japanakiyama-yasuhiro@mech.nagoya-u.ac.jp

978-0-7381-4674-4/21/$31.00 ©2021 IEEE378

Fig. 2.

Fig. 3.

Assist for motion control

Fig. 4.

Assist for torque control

Lower limb exoskeleton MALO

The ﬁrst is assist for motion control, and the outline of theassist is shown in Fig. 3. The solid line shows the angularvelocity of the joint during steady-state walking, and thedotted line shows assisted torque. The gait cycle is deﬁned as
the time period in which one foot contact the ground (0%) to
when the same foot again contact the ground (100%). This
assist is designed based on the motion of the joints of ahealthy person. In this assist pattern, an extension torque isapplied to the hip joint in the range of 0–40% and ﬂexion
torque in 45–85% of the gait cycle, and the ﬂexion torqueis applied to the knee joint in the range of 40–70% andextension torque in 70-100% of the gait cycle. These torques
act in a direction that assists and facilitates the motion of thejoint angle. Therefore, it is assumed that the push-off andswinging motion of the leg is promoted because the torquerises when the leg starts to move. On the other hand, thedirection of assist torque and internal torque of human areopposite when human decelerates joint motion.
The second one is assist for torque control, and the outlineof the assist is shown in Fig. 4. The solid line shows theinternal torque of a person during steady-state walking [6],and the dotted line shows the assisted torque. In this assistpattern, extension torque is applied to the hip joint by 0–30%
of the gait cycle, ﬂexion torque by 35–75%, and extension
torque by 80-100% of the gait cycle. Extension torque to theknee joint is applied by 5-30% of the gait cycle and ﬂexion
torque by 70–100%. These torques act in a timing when thejoint torque is required. Joint torque is exerted before joint
motion. A torque that supports the body weight and cancels
the inertia of the body is required. By reducing the joint
torque required by the muscles, it is expected that metabolic
cost of walking may decreased[7].
C. Protocol
In this study, the experiment was conducted on twosubjects who were 24-25 years old. The experiment wasconducted with the approval of the Nagoya University ethics

committee (approval number 20-10). In the experiments, thesteady-state gait with the two aforementioned assist patterns
and with only friction compensation such that the rotational
resistance of the motor was zero were measured. In thisstudy, the friction compensation assist is described as noassist. Walking was controlled at a speed of 3.0 km/h witha cadence of 95 stride/min for each assisted pattern. The
comfortable speed and cadence were adjusted for subjects
before recording the trial.
D. Data analysis
The hip and knee joint angles were calculated from thefeature points measured by the motion capture system. The
joint angles on the sagittal plane were calculated from thetrochanter, knee, and ankle markers. These were deﬁned as
0 degrees in the upright position at the beginning of theexperiment. These data, including the force sensors, wereanalyzed for 60 s of a 90-s walk. A force plate attachedto the heel of the robot was used to determine heel contact,and data were separated for each walking cycle. The meanand standard deviation (SD) of all the cycles were thencalculated.
III. R ESULTS
A. Controlled gait
The joint angles of subject 1 under controlled walking
conditions are shown in Fig. 5 and Fig. 6. The data weredivided based on the heel contact of the right leg. Thelines represent the means of all cycles, and colored areas

379

TABLE I
M AXIMUM FLEXION ANGLE OF THE RIGHT LEG AND THE GAIT CYCLEAT CONTROLLED GAIT (S UB .1)

Max. hip angle[deg]Gait cycle [%]Max. knee angle[deg]Gait cycle [%]∗ mean angle ± SD

Without assist
30.6±1.58∗
93.9
62.4±2.85
79.5

Motion control
32.5±2.4688.8
73.3±3.40
78.7

Torque control
32.6±1.5490.6
68.7±2.34
76.5

TABLE II
M AXIMUM FLEXION ANGLE OF THE RIGHT LEG AND THE GAIT CYCLEAT CONTROLLED GAIT (S UB .2)

Max. hip angle[deg]Gait cycle [%]Max. knee angle[deg]Gait cycle [%]∗ mean angle ± SD

Fig. 5. Hip angle of Sub.1 at controlled gait. Data were averaged acrossgaits, and shaded regions represent SD.

Without assist
22.8±1.63∗
90.1
52.7±3.04
73.3

Motion control
28.8±3.69
82.7
66.6±3.54
72.5

Torque control
26.7±2.0785.7
59.9±3.51
72.3

B. Comfortable gait
Comfortable speed and cadence of each assist condition
were determined through gait practice. Comfortable gait
speed and cadence are shown in Table 3. For both subjects,
a faster speed was chosen when the assist was applied.
The maximum ﬂexion angle and the maximum walking
cycle of the right leg under comfortable walking conditions
are shown in Table 4 and Table 5. An increase in the hipand knee angle was also observed during comfortable gait.
In addition, the lower leg compression force of the assisted
condition was increased in the 50–70% gait cycle comparedto the unassisted condition.
IV. D ISCUSSIONS

Fig. 6. Knee angle of Sub.1 at controlled gait. Data were averaged acrossgaits, and shaded regions represent SD.

are in the ± SD. These ﬁgures show an increase in themaximum ﬂexion angle with assistance in both the hip andknee joints. Table 1 and Table 2 show the maximum ﬂexion
angle and maximum walking cycle of the right leg undercontrolled walking conditions. The maximum hip and knee
ﬂexion angles were larger, and the timing of the maximum
ﬂexion angle was earlier in the assisted condition than in theunassisted condition. In addition, the timing when the knee
ﬂexion angle reached its maximum was earlier in the assist
for torque control than in the assist for motion control, which
is faster in hip extension and ﬂexion timing.
The compression forces of the right leg cuff sensor undercontrolled gait conditions of subject 1 are shown in Fig. 7and Fig. 8. The lines represent the means of all cycles, andthe colored areas are in the ± SD. In the assisted condition,lower leg compression was increased in the 50–70% gait
cycle compared with the unassisted condition.

The lower leg compression force was increased in the50–70% gait cycle compared to the unassisted condition.
This is when the hip ﬂexion torque is applied. It is consideredthat the hip ﬂexion torque pushed the lower leg in the forward
direction and increased the ﬂexion of the knee and hip joint.
Pushing the support leg immediately before toe-off can be anFig. 7. Compressive force when motion control is applied. Data wereaveraged across gaits, and shaded regions represent SD.

380

to that in the unassisted condition. This may be because
the hip extension torque applied by the MALO altered thetorque of the subject. In addition, the activity of the biceps
femoris during the late swing phase was smaller in the torquebase than in the motion base of subject 1. This trend can beunderstood as an effect of the hip extension and knee ﬂexion
torque applied by MALO.
V. C ONCLUSION

Fig. 8. Compressive force when torque control is applied. Data wereaveraged across gaits, and shaded regions represent SD.
TABLE IIICOMFORTABLE GAIT SPEED AND CADENCE OF SUBJECTSAssist pattern
Walking speed of Sub.1
Cadence of Sub.1
Walking speed of Sub.2
Cadence of Sub.2

No assist
2.575
2.680

Motion control
3.1
95
3.085

Torque control
3.5
100
3.085

Walking speed is expressed in km/h, and cadence in steps/min

In this study, we aimed to compare the gait under twoassist patterns to determine which assist pattern is effectivein improving the gait. In the experiment, subjects walked ona treadmill using a walking assist robot. Gait motion, muscle
activity, and interaction force were measured.
Our results showed that hip and knee ﬂexion was assisted
by our robot, as shown by the changes in joint angles andinteraction force. The changes in muscle activity indicatedthat hip extension may be assisted. It is also possible that
torque control assistance reduced biceps femoris activity.
In this study, we were unable to show the difference inthe effect of assistance of different patterns. However, it was
shown that the magnitude of the maximum ﬂexion angle ofthe knee joint and some muscle activities may differ for eachassist type. We intend to show the difference in the assisted
effect by increasing the number of subjects in future studies.
R EFERENCES

effective assist [8]. Furthermore, the knee ﬂexion angle formotion-controlled assistance is greater than that for torquecontrolled assistance. This may be due to the fact that theknee ﬂexion assistance was applied to the initial swing leg
only when motion control assistance was applied.
In addition, in the torque control assist, an increase intensile force on the lower leg at the early stance phase
was observed. At this time, the knee joint extension torque
was applied. Therefore, the knee extension torque may haveincreased. In the initial stages of the stance, the activity of thehip extensors was reduced in the assisted condition compared
TABLE IV
M AXIMUM FLEXION ANGLE OF THE RIGHT LEG AND THE GAIT CYCLEAT COMFORTABLE GAIT (S UB 1)

Max. hip angle [deg]Gait cycle [%]Max. knee angle [deg]Gait cycle [%]∗ mean angle ± SD

Without assist
28.1±1.55∗
93.2
57.1±2.41
76.7

Motion control
32.3±2.4388.8
75.8±3.51
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Torque control
33.4±1.37
90.3
69.3±1.44
75.7

TABLE VM AXIMUM FLEXION ANGLE OF THE RIGHT LEG AND THE GAIT CYCLEAT COMFORTABLE GAIT (S UB 2)

Max. hip angle [deg]Gait cycle [%]Max. knee angle [deg]Gait cycle [%]∗ mean angle ± SD

Without assist
25.7±2.44∗90.352.1±3.67
74.8

Motion control
28.7±4.55
83.1
66.2±6.55
72.4

Torque control
28.7±2.3085.762.0±2.92
74.7381