Development of Adaptive Gait Assist
Algorithm Using Ground Reaction Force
Takuma Igami ∗ Yasuhiro Akiyama ∗∗ Shogo Okamoto ∗∗∗Yoji Yamada ∗∗∗∗∗

Nagoya University, Nagoya, Japan,(igami.takuma@d.mbox.nagoya-u.ac.jp)∗∗
Nagoya University, Nagoya, Japan,(akiyama-yasuhiro@mech.nagoya-u.ac.jp)∗∗∗
Nagoya University, Nagoya, Japan,(shogo-okamoto@mech.nagoya-u.ac.jp)∗∗∗∗
Nagoya University, Nagoya, Japan,(yoji-yamada@mech.nagoya-u.ac.jp)
Abstract: Nowadays, physical assistant robots are considered as a solution against aging ofsociety. Especially, it is essential to support the elderly to walk because walking is an important
activity of the daily life. However, the physical assistant robot is required to adapt to the changeof motion of the wearer for being used in the daily living environment. Our research aims todevelop an adaptive walking assist algorithm which can follow the change of motion of thewearer smoothly. Instead of measuring the joint torque of the wearer directly, pattern of groundreaction force was used to estimate the joint torque based on the hypothesis that the wearable
robot can adjust assist timing and assist torque using a ground reaction force as the input
signal. The result of the walking experiment, which utilized the adaptive gait assist algorithm,
suggested that the change of gait motion strongly diﬀered among individuals. Also the assist
improved gait parameters of some subjects, it was not eﬀective for the subject who was notaccustomed to the assist robot.
Keywords: Physical assistant robot, Assist algorithm, Gait motion
1. INTRODUCTION
Aging of society is a common social problem among
many countries. The gait ability, such as muscle strength,
reaction time, and sense of balance decreases with aging.
Thus, the elderly sometimes gradually becomes inactive.
Physical assistant robots potentially improve the quality of life of such elderly people through assisting their
gait motion and enhancing their daily living activities.
Thus, recently, the usage of the physical assistant robot
is spreading from the rehabilitation in the hospital to thedaily living environment. However, the variety of motion
required in the daily living environment is much larger
than that in the hospital. For example, up and down stairs,
curving, and stopping are frequently required in the daily
life. Furthermore, probably, the gait timing is not stableowing to the various perturbations even during normalwalking. Thus, the gait assist algorithm is required toadapt to the change of gait timing during gait.
There are some methods to sense or anticipate the intention of the wearer. To embed the encoder to the robotic
joint is a basic method. However, this method can beused only when the wearer can move the joint by themselves. Thus, the assist algorithm, which also aﬀects thejoint motion, should not be so complex and strong forthe safety. To attach the force or torque sensor to the⋆joint is another method. It can observe the intention of
the wearer more directly through measuring interaction
force or torque between the wearer and assist robot. GRF(ground reaction force) is another measure to monitor themotion of the wearer. It can be used not only to estimate
the intention of the wearer but also the balance of the bodythrough monitoring the position and motion of the centerof pressure.
In this study, the adaptive gait assist algorithm, which canfollow the change of gait timing of the wearer, is developed
for the gait assist in the daily living environment. Although
the joint torque of the wearer is an accurate signal of gait
motion, it is not general for the assist robot to measure
the joint torque directly. Instead, GRF, which the assist
robot sometimes monitors using pressure sensing sole for
gait evaluation, was used to detect the gait timing andestimate the joint torque.
2. DESIGN OF ASSIST ALGORITHM
The assist algorithm, which aimed to assist hip and knee
joints during stance phase in the sagittal plane, wasdeveloped. The overview of the timing and magnitudeof the assist torque applied to each joint is displayed inFig. 1. Pattern of assist torque was determined basedon the gait cycle, which was the time period between
successive heel contacts (HC) of the leg. Furthermore,stance phase, which generally started from 0% and ended

Fig. 1. Pattern of GRF and assist torque applied to hip
joint (* Hip joint torque is cited from Winter DAet.al(1996))
at approximately 60%, was separated to three phases asfollows using gait events. The period from HC to footflat (FF) was determined as phase (a). Then, phase (b)continued to heel oﬀ (HO). The residual phase, which
ended at toe oﬀ (TO) was determined as phase (c). Gaitevents, which separate these phases, were detected basedon GRF in normal direction.
The target of the gait assist during phase (a) was to absorb
the impact of HC and support body weight. Thus, theassist torque helped the extension of both hip and kneejoints. In contrast, during phase (c), the assist torque wasapplied to help hip and knee flexion, which were the preswing before swing phase. phase (b) was the phase whichconnected neighboring phases smoothly.
Formulae, which calculated the assist torque in direction
to the joint extension, T , of each phase, were determined
as equations (1)-(3). t, which is the gait phase in %, isthe parameter of these formulae. tF F and tHO are thegait phase which separate assist phases. GRF (t) is theGRF of stance leg at t. T t is the maximum value of assist
torque expressed in Nm. They were set at 20.0 Nm for hip
joint and 11.7 Nm for knee joint, which are the capacityof motors. BW is the summation of the weight of thewearer and MALO. prt is the time duration from HC tothe peak of GRF. ∆p is the time distance from prt to tF F .
Then, TF F is the torque in Nm exerted at the beginning ofphase (b), which is same as the torque applied at the endof phase (a). Furthermore, Tmax is the maximum assist
torque exerted during phase (a). These parameters wereindicated in Fig. 1.
T (t) = T t ×

GRF (t)BW(1)t − tF F π)
prt − ∆p 2(2)T (t) = TF F × cos2 (T (t) = −0.9Tmax × sin2 (t − tHO π)
prt 2(3)Fig. 2. MALO : Motor Actuated Lower-limb Orthothis
As suggested in equation (1), this algorithm is based on
the hypothesis that the required assist torque is correlated
to GRF. In this hypothesis, GRF is related to the joint
torque which supports the body weight in the stancephase. Perhaps, it is better to tune T t of each joint foreach wearer although it was set according to the limitationof actuator in this study.
Although the assist algorithm anticipates the typical shapeof GRF pattern drawn in Fig. 1, it varies among individuals. Thus, the developed algorithm determines the target
value and timing of successive assist phases using the shapeof GRF of phase (a), whose GRF shape becomes similar
among individuals.
3. EXPERIMENT OF ASSISTED WALKING
The experiment was performed with the permission of theinstitutional review board of Nagoya University.
Three healthy young males were participated in the experiment of assisted walking. The developed algorithm wasinstalled to a lower limb assist robot named MALO (MotorActuated Lower-Limb Orthosis), which was developed inour lab. The overview of MALO is shown in Fig. 2. MALO
is attached to the wearer using cuﬀs of thigh and shank,shoes, and corset. Hip, knee, and ankle joints are only
capable of rotating in the sagittal plane. Actuators (RE 40,Maxon Motor AG, Sachseln, Switzerland) are attached toboth hip and knee joints to apply assist torque. To measure
GRF of toe and heel part of each foot, mobile force plates(M3D, Tec Gihan Co., Ltd., Kyoto, Japan) are fixed under
the sole of MALO.
To stabilize the gait timing of subject among trials, ametronome, which was adjusted to the comfortable frequency of each subject, was used. Furthermore, to test
the adaptability of the assist algorithm to the various
gait timing, slow condition, whose frequency decreased by
6% from the comfortable frequency, was also used. Thiscondition covered the range of walking speed in the dailyliving environment.

Fig. 3. CoM lateral range (Normal)

Fig. 4. CoM vertical range (Normal)

The gait motion of these two gait speeds were recorded
under the conditions with and without gait assist. For all
subjects, normal speed trials were recorded at first. Then,slow speed trials were recorded. The order of assisted andnot assisted trials were randomized for each subject. Trialsof each condition were recorded eight times. Thus, in total,
32 trials were recorded for each subject.
The gait motion in the walking lane, whose length was 3m except for the acceleration and deceleration area, wasrecorded using motion capture system (MAC 3D System,Motion Analysis Corp., Rohnert Park, CA, US). The angleof hip and knee joints and the position of the CoM (centerof mass) were calculated from the human mode fitted tomarkers attached to the subject.
Fig. 5. Maximam and minimam hip angle (Normal)
4. RESULT
Representative gait parameters of each subject under eachcondition are shown in table 1. Although the gait timing
was controlled by the metronome, it varied approximately
1 to 2 strides/min. The result suggested that there werestrong individual diﬀerence among subjects against theassist. Although the assist torque was applied, the walkingspeed decreased in the normal case of subject 3. In thiscondition, stride length also decreased. However, in theslow speed condition, this trend was disappeared from thesubject 3.
Then, CoM sway, which is determined as the range of
motion of CoM in the lateral direction of each gait cycle,
is shown in Fig. 3. The result suggested that the CoM
motion increased in assisted condition except for subject1 in normal speed. In contrast, the CoM sway of subject 3
decreased in the assisted condition in slow speed. However,
the trend of CoM range in vertical direction did not match
to the CoM sway as shown in Fig. 4.
The maximum and minimum angle of hip joint of each gait
cycle is shown in Fig. 5. This trend of peak angle suggested
that the maximum angle increased in the assisted condition through all subjects. Furthermore, the maximum knee
angle also increased in the assisted condition as shown inFig. 6.

Fig. 6. Maximam knee angle (Normal)
5. DISCUSSION
According to table 1 and Fig. 5, it seems that gait assist
improved gait parameters of subjects 1 and 2. The CoM
range in vertical direction shown in Fig.4 is consistent to
the maximum hip angle, because the CoM height becomesminimum when the leg maximally opens. However, the assist torque slightly aﬀected on some parameters. Perhaps,
it is necessary to tune parameters of assist algorithm suchas T t and prt shown in equations (1)-(3) to increase theTable 1. Walking parameter

Walking speed [m/s]Walking speed [strides/min]

Stride length [cm]

Sub1Sub2Sub3Sub1Sub2Sub3Sub1Sub2Sub3Normalw/o Assist
w/ Assist
1.08 ±0.05 1.12 ±0.07
0.92 ±0.04 1.00 ±0.08
0.77 ±0.04 0.72 ±0.07
49.9 ±1.1
48.0 ±1.2
46.2 ±1.847.2 ±1.443.7 ±1.242.9 ±0.7
130 ±7141 ±9123 ±4125 ±9124 ±9
119 ±9assist torque. Furthermore, it is an option to expand theassist phase to the swing phase.
The increase of CoM sway of assisted condition shown inFig. 3 suggested that this gait assist did not contributeto improve gait balance in lateral direction. However,according to table 1 and Fig. 3, the gait parameters
and CoM sway was changed in an opposite way among
subjects. For example, CoM sway of subject 1 slightly
increased in the assisted trials whereas the walking speed
and stride length increased. In contrast, the improvementof gait parameters of subject 2 and 3 was not large whereas
the CoM sway increased. This diﬀerence of the change ofgait motion among subjects suggested the large individual
diﬀerence against the assist robot. Perhaps, the adaptationof each subject to the robot aﬀected the gait motion
because the subject 1, whose gait motion improved in theassist trials than the other subjects, was accustomed toMALO.
6. CONCLUSION
The adaptive gait assist algorithm, which can fit to thedaily living environment, was developed in this study. Suchan adaptation to the change of gait motion is essential
to spread the physical assistant robot to the society. The
developed assist algorithm was designed to apply assist
torque to the hip and knee joints based on the patternof GRF. GRF of each foot was used as not only toswitch assist phases but also determine the magnitude ofassistive torque instead of measuring joint torque directly.
The change of gait motion caused by assist torque underdiﬀerent speeds was observed.
The peak flexion angle of hip joint increased in the assisted
cases common to subjects. Furthermore, gait parameterssuch as walking speed and stride length improved in somesubjects. In contrast, the CoM sway in the lateral direction, which probably aﬀected the stability, increased in
the assisted cases of some subjects. Considering the trendthat the improvement of gait parameters and increase ofCoM sway did not occur at the same time, the gait assist
improved the gait of some wearer whereas it negatively
aﬀected to the other subject. The adaptation to the assist
robot perhaps improves the eﬀect of gait assist.
REFERENCES
Winter DA, Eng JJ, Ishac M: Three-dimensional moments, powers and work in normal gait: Implication for clinicalassessment. In Harris GF, Smith PA (eds): Human
Motion Analysis: Current Applications and Future
Directions. New York, IEEE Press, 1996Slow
w/o Assist
w/ Assist
0.91 ±0.03
0.95 ±0.04
0.89 ±0.04
0.94 ±0.06
0.89 ±0.04
0.94 ±0.06
47.7 ±1.147.8 ±1.145.5 ±1.9
47 ±241.9 ±0.9
41.0 ±1.1
114 ±6120 ±6121 ±4120 ±4130 ±10132 ±5

United Nations, Department of Economic and Social Affairs. World Population Ageing 2017 Highlights
K. Suzuki, G. Mito, H. Kawamoto, Y. Hasegawa and Y.
Sankai. Intention-based walking support for paraplegia
patients with robot suit HAL. Advanced Robotics,vol.21, no. 12:–1469, 2007.
Honda Corporation. Walking Assist Device with Stride
Management System Honda R&D technical review,vol.21, no. 2:54–62, 2009. (in Japanese)
J. E. Pratt, B. T. Krupp, C. J. Morse, S. Mori and S.
H. Collins. The RoboKnee An Exoskeleton for EnhancingStrength and Endurance During Walking. IEEE
International Conference on Robotics and Automation,
vol.3:2430–2435, 2004
T. Nakamura, K. Saito, Z. Wang, K. Kosuge. Wearable Walking Support System Based on GRF andHuman Model. Proceedings of the 2005 IEEE International Conference on Robotics and Automation,Barcelona,Spain(2005).
Ana M.F. Barela, Marcos Duarte. Biomechanical characteristics of elderly individuals walking on land and inwather. Journal of Electromyography and Kinesiology ,no.18:446–454
D. A. Skelton, J. Kennedy, O. M Rutherford Explosive
power and asymmetry in leg muscle function in frequent
fallers and non-fallers aged over 65. Age and Ageing,
no31:119–125, 2002
B. Abnaya, L. Latha An Advanced Gait Monitoring
System Based on Air Pressure Sensor Embedded in aShoe Procedia Engineering, no38:1634–1643, 2012