Proceedings of the 2014 IEEE
International Conference on Robotics and Biomimetics
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Analysis of Recovery Motion of Human to Prevent Fallin Response to Abnormality with a Physical Assistant Robot
Yasuhiro Akiyama1 , Ikuma Higo1 , Yoji Yamada1 , and Shogo Okamoto1
Abstract— A physical assistant robot is useful for elderly anddisabled people, as well as workers in certain industries, as itenhances their locomotive capabilities. However, risk assessment
has identified some unique risks such as wounding of the skinand fall because of the use of such physical assistant robots.
Among these potential risks, falls can be particularly severe.
This study focuses on the effect of a sudden interference to the
motion of the wearer while using a physical assistant robot.
Changes in the gait motion are measured by an experimentthat considers the unexpected torques experienced by a healthy
wearer. The results indicate that the wearer can effectively
recover despite changes in the joint angles and centers ofpressure that can be used to analyze the risk of fall with aphysical assistant robot.

I. INTRODUCTION
Greater longevity has increased the importance of qualityof life (QOL) for elderly and disabled people. In addition,
falling birth rates in some countries have led to a need for
greater worker efficiency. A physical assistant robot is one
solution to such problems. These are robots that are fixed
to the wearer by cuffs, bands, and other means, and thereby
apply torque to the wearer’s joints. They support locomotion,
rehabilitation, and manual tasks. Such close contact between
the physical assistant robot and the wearer increases theimportance of safety measures. Therefore, previous studies,such as that by Akiyama et al. [1], focused on the contact
safety of the physical assistant robot, and ISO 13482 hasrecently been published to specify the levels of safety that
service robots should satisfy.
However, the amount of consideration given to the riskof falls is insufficient despite the potential danger. Someof the strategies and tolerances of healthy humans towardcertain kinds of perturbation were revealed by Yang et al. [2]and Zhou et al. [3]. However, the presence of a robot couldchange these motions even if the user can still apply thesestrategies to their motion, even if only partially. The effect of
the change in physical characteristics was tested by Arellano
et al. [4] and Meuleman et al. [5]. The results of these studies
can help us consider the effect of the mass and inertia of therobot. However, the effect of the robot’s actuation cannot be
estimated.
Table I lists the results of a risk assessment for a physicalassistant robot. Considering both the results of previous
research and the contents of this table, we can see that the
1 Yasuhiro Akiyama, Ikuma Higo, Yoji Yamada, and Shogo Okamoto
are with the Department of Mechanical Science and Engineering, Nagoya
University,Japanakiyama-yasuhiro, yamada-yoji,okamoto-shogo @mech.nagoya-u.ac.jp,
higo.ikuma@e.mbox.nagoya-u.ac.jp

978-1-4799-7396-5/14/$31.00 © 2014 IEEE

effect of the application of unexpected torques by the physical assistant robot has not yet been sufficiently considered,despite its importance. An applied torque will interfere withthe motion of the wearer if it is applied at an inappropriate
timing or in other than the required direction, regardless ofwhether or not it is caused by a failure. In the worst case, it
could even lead to a fall. Both the severity of the hazard andthe difficulty of its predictability increase the significance of
this unexpected torque. Therefore, this study focuses on theeffect of unexpected torques on the gait of a subject wearinga physical assistant robot as a basic approach to analyzing
human reactions when using a physical assistant robot. An
analysis of how humans fall when using a physical assistantrobot would be helpful to evaluate the safety of a physicalassistant robot.
II. SETUP OF TRIPPING EXPERIMENT
A. Apparatus
1) Measurement system: An overview of the experimentis shown in Fig. 1. There is a 5 m walking lane, 2 m of which
is a runway. In the recording area, the attitude and positionof the subject are recorded using a 3D motion capturesystem (MAC 3D system, Motion Analysis Corporation,USA) and a video recorder (SONY, Japan). The groundreaction force (GRF) is measured by force plates (M3D force
plate, Tech Gihan Co., Ltd., Japan) attached to the soles ofthe subject’s shoes. The recording of the subject’s position
and reaction force is done at 500 Hz. To monitor somefeatures of the subject that are covered by other devices,cluster markers are used. After recording, a motion simulator(SIMM, MusculoGraphics, Inc., USA) is used to calculate
the joint angles of the subject.
To ensure the safety of the subject, he wore a safety
harness that was suspended from a gondola that moves along
the walking lane. The gondola does not, however, support
the weight of the subject, except in the event of a fall. In
addition, a supporter was used to prevent sprains.
2) Physical assistant robot: A physical assistant robot
was developed for use in the tripping experiment. This legtype motor-actuated lower-limb orthosis (L-MALO), shownin Fig. 2, has two separate legs. Each leg is fixed to thewearer using cuffs and shoes. The cuffs are fixed to thewearer’s thigh and lower thigh. This L-MALO has a 1DOF foot joint and a 1-DOF knee joint. The hip joint is
not restrained. Only the knee joints of the L-MALO are
actuated by a motor (RE40, Maxon), and compensation for
the rotational friction of the knee joint is applied. The total
weight of the L-MALO is 8 kg.

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TABLE IR ISK FACTORS OF FALL

Category

Potential consequences
Loss of balance because it is impossible to perform the normal motion
Loss of balance because it is impossible to perform the normal motion
Loss of balance because there is a mismatch between purpose and motion
Factors related toof userphysical assistant robot
Loss of balance because mechanical characteristics are unfamiliar
for user
Loss of balance because of unexpected torque
Loss of balance because there is sudden external force
Loss of balance because it is impossible to swing the leg
Loss of balance because it is impossible to support body weight
Environmental factors
Loss of balance because it is impossible to keep supporting leg
Narrow way
polygonNote: Device failure and those factors which are not associated with contact (light, sound etc.) are omitted.

2Starting position

Fig. 1.

Hazard source
Restraint of joint DOF
Decrease of joint motion range
Restraint and torque of
joint motion
Change of center of gravity andinertia moment
Mismatch of assist timing
Collision with obstacle
Collision with low obstacle
Instability of footing3



Recording area

Gait measurement system

Fig. 2.

The L-MALO is designed to mimic a general powerassistance device. Therefore, gait cycle based assistance isadopted. The gait time is measured by counting the timing of
the peak knee angle, and the mean gait time of the last threecycles is used to calculate the assistance timing for the next
gait cycle. From the viewpoint of mechanical design, Prattet al. [6] studied a physical assistant robot with a similar
configuration. In addition, Lewis and Ferris [7] adopted asimilar strategy for counting the gait timing. Although there
are other types of robots such as [8], these robots are notconsidered in this study because of the difference in theirassistance strategies.
The L-MALO applies 8.5 N·m of flexion torque in the
35%–50% range of the gait cycle. Then, in the 85%–100%
range, an 8.5 N·m extension torque is applied. The gait cycle
was calculated from the timing of the maximum knee flexion.
Flexion assist is applied in the middle of the support phase,
which helps to move the supporting leg backward. Extension
assist is applied between the middle and end of the swing
phase. This helps to extend the knee before the heel strikes
the ground. The magnitude of the assist torque corresponds toabout 30% of the torque at the knee joint, which is assumed
to be the maximum assist rate that can be applied by a

L-MALO (left: front view, right: side view)

general gait-assist robot. An overview of the assist timingis shown in 3.
As the tripping method, an extension torque of 8.5 N·m
was applied from the start of the stance phase of the left
leg. Then, that torque was applied until the end of the gait.
This torque represents the unique timing of a timing error
in the assist algorithm because a person is not used to theapplication of an external extension torque in daily life. For
safety, however, a mechanical limiter restricts the extensionof the knee joint to no more than 180 °.
B. Protocol
This experiment was conducted using a healthy malevolunteer. Fig. 4 shows an overview of the experimental
protocol. After putting on the L-MALO, force plate, markers,
and safety harness, the subject was asked to walk so thatthe assistance being provided by the L-MALO could be
adjusted. This was continued until the subject no longer felt
uncomfortable. Then, the subject walked along the test lane
at a comfortable speed while his motion was recorded. As
the subject was walking, however, torques were randomly
applied (the subject was not told when these torques would
be applied). The number of cases was N = 8 (assisted cases)
and N = 9 (tripped cases). The total time taken for the

1494

0%

10%

Fig. 3.

15 min60 min20 min

20%

30%

50%

40%

60%

70%

80%

100%

Assist timing (continuous line: main leg, dashed line: opposite leg)

20 min

5 min

20 minTABLE IIG AIT PARAMETERS OF ASSISTED WALKING

15 min: Description and agreement

Speed [km/h]
Step length (left) [m]Step length (right) [m]Cadence [/min]
Double support ratio [%]: Attach experimental devices and safety devices: Adaptation: Experiment on the overground (Assisted trial, Perturbed trial): RestTotal : 155 min: Remove devices and report

Fig. 4.

90%

Average
2.68
0.579
0.623
36.9
28.0

Standard deviation
0.14
0.049
0.023
0.93
0.97

Protocol of the tripping experimentIII. RESULTS
A. Gait parametersexperiment was about 2.5 h.
This experiment was done with the permission of the IRBof Nagoya University.
C. Data processing
Data related to the joint angles and GRFs is filtered by a 6Hz low-pass filter. Then, the offsets of the joint angles caused
by inaccuracies in the positions of the markers are correctedusing the data for a standing posture. Then, the data for the
motion from one left heel contact to the next left heel contact
is clipped to give the gait cycle. Each gait is normalized using
the time period for the gait motion or that for the stance.
Cases of normal assist are processed statistically.
The gait parameters for assisted walking were calculated
from the positions of the motion capture markers. The
forehead marker was used to calculate the subject’s speed.
The step length was assumed to be the distance between
the heel markers of each leg in the direction of travel. The
cadence was given by the frequency of the gait of one leg.
To compare the GRF values, the magnitude of the GRF isnormalized by the weight of each subject and the horizontal
axis is normalized by the stance.
Cases with an unexpected torque are normalized using the
average gait time or stance time for the normal assist caseand processed in the same way as for a normal case. Thisis because the aim of the normalization is to compare themotion after the application of an unexpected torque to thatfor a normal case, including the time scale. However, thesubject would sometimes move out of the recording area.
Therefore, the data for those cases in which an unexpected
torque is applied is shorter than that for a normal case.

Table II lists the gait parameters for assisted walking.
Gait parameters were calculated using the gait cycle fornon-perturbed trials. The comparison of the gait parameterswith the normal gait shown by Neumann [9] shows that thewalking speed was low compared to the average speed of ahealthy adult (4.98 km/h in normal gait). The main reason forthis low speed was the small cadence (55 /min for a normal
gait). In addition, the double support ratio is about 10%
higher than that for a normal gait. A synchronous change inthe speed and double support ratio is a general trend observedin a normal gait.
When using the L-MALO, the wearer walked more slowly.
The reason for this change was that the weight and fixing of
the L-MALO caused the subject to be more careful. Some
physical assistant robots exhibit such a tendency, although
it varies with the degree of usage of the physical assistantrobot.
The difference in the step length of each leg suggests anasymmetry in the gait of the subject. In our experiment, theunexpected torque was applied to the left leg. Therefore,
the subject probably changed his gait in preparation for theunexpected torque, even though the subject claimed that hewas not doing so.
B. Joint Angle
The joint flexion angle for assisted walking is shownin Fig. 5. This result suggests that the gait of the subject
resembles a general gait, except for the knee angle in the
stance phase. In the stance phase (about 20–30%), the left
knee angle appears to be small in comparison with that of anormal gait. This trend seems to be the result of extension
assist at the end of the swing phase.

1495

Fig. 5.

Joint flexion angle on sagittal plane

Fig. 6.

Joint flexion angle of affected side

In addition, the maximum knee angle appears to beasymmetric. This trend also points to the subject changing
his gait to ready himself for the unexpected torque.
Because of application of the unexpected torque, thejoint angle of the perturbed leg changed after about 60%
through the step, which is the early swing phase of theperturbed leg. Fig. 6 suggests that there is a decrease in
the knee flexion. This is a result of the knee extension
torque providing unexpected assistance. At the same time,
the flexion of the hip of the perturbed leg became smaller.
This is because the prevention of knee flexion decreases theclearance between the shoe and the ground, which the subject
needs to swing his leg. In contrast, Fig. 7 suggests that the
motion of the unaffected leg in the sagittal plane does notchange drastically, even when the flexion of the other leg isdisturbed.
C. Reaction forces
Fig. 8 shows the GRF for the left leg in normal assist trials.
Fx is the front-back direction and Fy is the vertical direction.
Therefore, Fz indicates the lateral direction. Generally, Fy
immediately increases and reaches a maximum shortly afterheel contact. Although Fy of the early stance phase (0–30%)
is smaller than this trend, the result does not appear unusual.
The effect of the shoe thickness should be investigated toevaluate this trend. However, this trend in the GRF does not

Fig. 7.

Joint flexion angle of unaffected side

Fig. 8.

Ground reaction force of assist walking

affect the motion of the next swing of the left leg, because
the reaction force after the middle stance phase resembles
the general gate motion.
Then, the GRF of the other leg is shown in Fig. 9. Thisgraph is normalized by the time between the heel contact of
the right leg and the heel contact of the left leg. Therefore,
the GRF before the middle stance phase of the right leg
is drawn in this graph. From this graph, the effect of theunexpected torque is not clear despite it being the support
leg during a tripped swing. Therefore, this is further analyzed
in the discussion.
IV. DISCUSSION
The L-MALO changes the gait of the subject to somedegree. There is no major change in the joint angle, except
for the knee angle in the stance phase. In the stance phase,
flexion of the knee angle is suppressed. The reason for thisphenomenon is the effect of the assist torque. Although theextension torque at the end of the swing phase can effectively
prevent tripping and helps to support heel contact, it also
prevents the start of flexing of the knee joint in the earlystance phase. In addition, the knee flexion angle of the rightleg increases compared to that of the average gait motion
because the assist torque helps to flex the knee joint before
the swing phase. Although this flexion angle is larger than1496

Fig. 9.

Fig. 10.

Ground reaction force after trippingthat required to walk, it does not disturb the gait of a healthy
person but will help a person with lower muscle power.
On the other hand, the L-MALO reduces the walking
speed, because the cadence is lower. An increase in the
double support ratio is also caused by a change in the speed.
One reason for this change is the mass of the L-MALO. It
was suggested by Browning [10] that the additional massleads to a reduction in speed when the subject walks witha natural gait. Therefore, the subject will be affected by themass of the L-MALO because it is not self-supporting in theswing phase, as each side of the L-MALO is independent.
Then, any unexpected torque drastically changes the joint
angle of the affected leg in the sagittal plane. However, thischange cannot be described only in terms of the sagittal
plane because the straight leg disturbs the swing motion.
The abduction angle of the hip joint is shown in Fig. 10.
This graph suggests that the affected leg (left leg) moves
outward in the swing phase due to the abduction of the hip
joint of the unaffected leg. At the same time, the hip jointof the affected leg is adducted. This means that the pelvis
inclines toward the unaffected side to move the leg being
swung forward without dragging.
The same tendency can be observed in a pathological gait.
A patient who has limited hip flexion exhibits symptoms thatare similar to those of the subject when tripped. In this case,the patient swings his/her leg by using the rotation of thepelvis, and abduction of the support leg is also observed at
the same time, which maintains sufficient clearance between
the swing leg and the ground. The gait of the subject
resembles this gait in spite of the differences in the limitationof the joint motion.
From the viewpoint of the risk of a fall, the center ofpressure (CoP) of the support leg provides a useful meansof analysis, in addition to the analysis of the reaction force.
The distance between the CoP and the edge of the support
polygon indicates the stability margin of the support leg.
Fig. 11 shows the position of the CoP in the longitudinal
direction (COP L) and cross direction (COP C). The larger
deviation in the perturbed case indicates the randomness of
the recovery motion. The graph in Fig. 11 shows that the
CoP moves forward faster when the subject is tripped than

Abduction angle of hip joint

Fig. 11.

CoP of support leg

in a normal assist case. In addition, the CoP changes to theinner side (negative direction on the graph) in almost every
stance period.
Fig. 12 shows the trajectory of the CoP. This trajectoryalso suggests that the CoP moves toward the inner side of
the support leg. This change can be described by the motionof the swing leg. The centrifugal force of the swing leg pulls
the body toward the inside. As a result, the CoP suggests thatthe subject is likely to overlean toward a point in front of histrunk on the side of the leg being swung. However, the leg isswung in the direction of the subject’s overlean. Therefore,
the subject can easily perform this motion by stepping.
Therefore, in this experiment, although the unexpectedassist changes the balance of the subject, it was found that theprobability of a fall does not increase drastically. This result
also suggests that the subject can change his gait strategy
instantly. Although the gait changes from a normal gait, a
perturbed gait seems optimal in terms of the limitation of
the joint angle because it resembles the gait of a patient who
has limited hip flexion. Therefore, the advanced adaptivityof the human gait is suggested by this experiment.
Then, the asymmetry of the gait of the subject should
be considered. Even with normal assistance, the motionof the gait exhibits some asymmetry. One reason for thistrend is the compensation by the subject. In this experiment,
the unexpected torque was applied only to the left leg to
avoid the effect of the dominant foot. This possibly changed

1497     the instantaneous adaptivity of humans, in that it resembles
the motion of a patient with limited joint motion.

  




          Fig. 12.

ACKNOWLEDGMENT
This work was supported by JSPS KAKENHI Grant
Number 26750121.
R EFERENCES

Trajectory of the CoP of stance leg

the gait asymmetry. As a result, the knee flexion and hip
abduction angle of the swing phase were affected. Thischange suggests that the subject moved his leg outward
relative to an average gait. This motion seems to be effective
for preventing falls in the case of tripping because it expands
the support polygon. However, the effect of asymmetry is
not so severe as to affect the subject’s walking because
he can continue with his gait. Although the results of thisexperiment perhaps place the risk of the subject fall at rather
less than the actual value, the experiment clarifies the strategy
applied by the subject to avoid a fall.
It is impossible to evaluate whether this motion is common
for other people because of the limitation on the number ofsubjects and their individual attributes. Therefore, a morethorough inspection is required to reveal and classify theentire fall avoidance motion. However, the results of thisstudy suggest that the recovery strategy applied by a user
upon tripping differs from that applied upon falling or
slipping.

[1] Y. Akiyama, Y. Yamada, K. Ito, S. Oda, S. Okamoto, and S. Hara Test
Method for Contact Safety Assessment of a Wearable Robot -Analysis
of Load Caused by a Misalignment of the Knee Joint-, Proceedingsof The 21st IEEE International Symposium on Robot and Human
Interactive Communication, pp. 539-544, 2012.
[2] F. Yang, F. C. Anderson, and Y. Pai, Predicted threshold against backward balance loss following a slip in gait, Journal of Biomechanics,
vol. 41, pp. 1823-1831, 2008.
[3] X. Zhou, L. F. Draganich, and F. Amirouche, A dynamic model for
simulating a trip and fall during gait, Medical Engineering & Physics,
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[4] C. J. Arellano, D. P. O’Connor, C. Layne, and M. J. Kurz, The
independent effect of added mass on the stability of the sagittal planeleg kinematics during steady-state human walking, The Journal of
Experimental Biology, vol. 212, pp. 1965-1970, 2009.
[5] J. Meuleman, W. Terpstra, EH. van Asseldonk, and H. van der Kooij,
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[10] R. C. Browning, J. R. Modica, R. Kram, and A. Goswami, The Effectsof Adding Mass to the Legs on the Energetics and Biomechanics ofWalking, Medicine & Science in Sports & Exercise, vol. 33, no. 3, pp.
515-525, 2007.

V. CONCLUSIONS
An experiment was performed to analyze the reaction
motion when an unexpected torque was applied by a physicalassistant robot. In the experiment, an unexpected assist
torque was applied to the subject, in addition to the assist
torque. A physical assistant robot, the L-MALO, which
was attached to each leg independently, was used in thisexperiment. The application of a sudden abnormal torquethat prevents the flexion of the knee joint drastically changed
the gait of the subject.
Because of the additional torque, the subject increased his
hip abduction instead of reducing the flexion of his hip andknee joints. Although this gait also changed the trajectoryof the CoP and decreased the stability margin, the risk ofthe subject fall did not seem to increase because it was easy
for him to support himself with his trunk by stepping onto
the leg being swung. In addition, a different gait strategy
was adopted to reduce the effect of the limitation of joint
motion when the subject was tripped. This strategy points to1498