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

Estimation of effects of recovery step length
on severity of injuries caused by the trip and fall
Shuto Yamada1 , Yasuhiro Akiyama1 , Shogo Okamoto1 , and Yoji Yamada1
Abstract— Forward fall is a major risk for the elderly. In
the case of a trip and fall, it is known that the length of therecovery step affects the ability to avoid the fall. However, theeffects of recovery step length on the severity of injuries areunclear. Therefore, this study was conducted with the objectiveof investigating the effects of the recovery step length on thecontact velocity with the ground by estimating the natural fallmotion using a motion simulator. In this study, we developed
a simulator for estimating the fall motion which started fromthe motion obtained in an experiment. In the experiment, therecovery step of the participants was classiﬁed as a short-stepcase or long-step case. The simulation results showed that thecontact velocity had a correlation with the fall velocity of thecenter of mass (r = 0.52) and the simulation duration (r = 0.50) in the short-step case, but no correlation was observed in
the long-step case. It was suggested that a long recovery step
could result in an increase in the types of fall motion and thus
variations in fall injuries.

I. INTRODUCTION
In recent years, the majority of developed countries arefacing the problem of an aging society. In such a society,one of the most signiﬁcant risks that the elderly face ona daily basis is the risk of a fall [1]. Tripping is known
to be a major cause of a fall in the elderly [2]. In a trip
and fall, if the recovery step is delayed, injuries such as
wrist fractures caused by contact with the ground are more
likely to occur. Although it would be ideal to conduct actual
fall experiments to estimate the fall injuries, it is difﬁcult to
conduct such experiments for ethical reason. Thus, there has
been many research on a fall, and they have tried to estimate
the fall motion and fall injuries by partially reproducing or
simulating a fall.
Mitsuoka et al. [3] conducted an actual trip and fallexperiment and recorded the subject’s fall motion; however,the subjects were supported by a harness that stopped theirfall before ground contact occurred. The results of this study
suggested that the recovery step length affected the fallmotion. Lo et al. [4] used a 2D and seven-links model tosimulate a forward fall and estimate the corresponding fallinjury risk. They investigated the inﬂuence of each joint on
the fall motion by simulating various initial joint angles.
In this research, the subjects’ hands contact velocities werereported to be in the range of 2.86 m/s to 3.10 m/s.
Most of studies about the fall have been either partial fallexperiments with adequate safety precautions or measurements of fall avoidance motion in situations that do not leadto fall. Thus, it was difﬁcult to estimate fall injuries directly
1 Depertmentof Mechanical Systems Engineering, Nagoya
University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8603, Japanyasuhiro.akiyama@mae.nagoya-u.ac.jp

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

based on the results of actual fall experiments. In addition,
the simulators used in the previous researches did not fully
mimic the human body. For example, when simulator’s joint
angles were ﬁxed, they could not fully reproduce the fallmotion of a human.
Therefore, we developed a simulator that has a sufﬁcientnumber of joints for estimating a human’s fall motion basedon the data obtained from an actual fall experiment. The
objective of this study was to estimate the natural trip andfall motion using a simulator and investigate the effect of therecovery step length on fall injuries (contact velocity withthe ground). Our hypothesis is that the fall injuries would be
more severe in the case of short step length than those in thecase of a large step length. This hypothesis is based on theresults of Mitsuoka’s research, according to which the step
length of the recovery leg affects the fall motion. In order toassist people with wearable robots, it is necessary to clarify
the motion of wearer, the human, and this research focuseson the fall motion.
II. METHODSWe developed a fall simulator using numerical analysis software (MATLAB, MathWorks Corp.). This simulator
makes use of data obtained from an actual fall experiment[3].
A. Experimental data
The experiment was conducted on seven healthy young
males who had no neurological or musculoskeletal disorders.
Their average age, height, and weight was 21.4 ± 1.2 y,
173.0 ± 3.0 cm, and 61.1 ± 6.9 kg respectively. The experimental condition is presented in Fig. 1. A trip fall wasinduced in the late swing phase of the participants due toobstacles placed in their path. There were some strategies
for avoiding a fall such as the elevating strategy” andlowering strategy” [5]. the elevating strategy is a motion
in which a tripped leg that has collided with an obstacle
and put it forward as a recovery leg and lowering strategy isa motion in which the tripped leg immediately touches theground and the other leg is put forward as a recovery leg.
The most common outcome was an elevating strategy of theswing leg in response to the early swing perturbation and alowering strategy in response to the late swing perturbation[5]. In this experiment, the latter was focused on, and the59 trials of lowering strategy was analyzed. By adjusting thelength of the rope, the limitation of the recovery step wasrelatively large in the Long step case” or Short step case”
in which the length of recovery step was strictly limited. In216

upper-arms, forearms, hands, thighs, shanks, and feet. This
human model comprised shoulder, elbow, hip, knee, andankle joints. The shoulder, lumbar, and hip joints have threedegrees-of-freedom (DOFs) and the other joints have oneDOF. Fig. 2 shows an overview of the human model. At thebeginning of the simulation, the human model is scaled fromthe subject’s height and weight based on anthropometrics[6][7].
For estimating the fall injuries caused by collision withthe ground, contact detection was used at both hands andknees and the ground in order to obtain the contact speed at
the time of contact with the ground.
Fig. 1: Experimental condition

C. Simulation condition
The joint torque is calculated using the inverse kinematics
method with the input of the joint angle, angular velocity,
and angular acceleration obtained from the experiment. In
the experiment, the observed motion of the lower-limb joints
and lumbar under the condition of the support of the harness
probably differed from the actual fall motion because thesubject was supported by the harness in the terminal phaseof the fall. Therefore, in this study, the simulation was started
from the posture and velocity at which the harness started tosupport the subject during the experiment. The lower-limb
joints and lumbar in the sagittal plane were not actuatedwhile estimating the most severe fall condition. The otherjoints were driven by trajectory control, using the same joint
motion as that in the experiment. In this manner, a morenatural fall motion could be reproduced in the simulator. In
this simulator, Simscape Multibody was used as a solver. The
simulation ended when the hands or knees contacted with theground.
III. RESULTS

Fig. 2: 17-link rigid body model

more detail, the length of the rope was adjusted so that therecovery leg was not in front of the support leg in the shortstep case. On the other hand, the length of rope was adjusted
so that the recovery leg was no more than 20 cm in front of
the support leg in the long step case. The experiment wasconducted under the two step conditions. Motion capture system (MAC3D System, Motion Analysis Corp.) comprising
10 cameras and 29 reﬂective markers was used to measure
the fall motion. The participants were made to wear a safety
harness to prevent their heads, shoulders and elbows fromcoming into direct contact with the ground, thus preventing
the occurrence of injuries. The Institutional Review Boardof Nagoya University approved all the experiment protocols,and all the participants provided written consent.
B. Fall motion simulator
The human model used in the simulator comprised 17
links:head, neck, trunk, abdomen, pelvis, (the left and right)

Two of the fall motions estimated using this simulator are
shown in Fig. 3a and Fig 3b as examples. Fig. 3a shows anexample of a hand contact with the ground on the opposite
side of the support leg, which was the most frequently
observed, and Fig. 3b shows an example of a knee contact.
The red area in these ﬁgures indicates the area at which thecontact detection was applied.
In this simulator, a total of 51 fall motions of sevensubjects were estimated. Eight trials, during which the ﬁrst
ground contact of the body did not occur at the hands orknees, were omitted from the analysis. The results of thepart that contacted with the ground were shown in Table I.
The side of the support leg before the contact with the ground
was refered to as the supported side” and the opposite side
was refered to as the opposite side”. The contact of theknee with the ground only occurred on the supported side andoccurred during approximately 8% of all the trials. However,the hand contacted with the ground, on supported side during
12 trials and on the opposite side during 35 trials, whichcorresponds to approximately 23% and 69%, respectively,of the total number of trials. Half of the 12 trials on the sideof the support leg was brought into contact with the same
participant.

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TABLE II: Contact velocity for each condition
Contact velocity [m/s]Contact part
Short step caseLong step case
Supported side hand
2.62 ± 0.82
3.32 ± 0.52
Opposite side hand
3.66 ± 1.25
3.44 ± 0.94
Supported side knee
2.98 ± 0.064.29 ± 0.12
Total
3.41 ± 1.20
3.48 ± 0.84
Notes : Supported side means the side of support leg(a) Hand contact(b) Knee contact

Fig. 3: Simulated fall motion
Fig. 4: Scatter plot of contact velocity against vertical velocity at simulation start (n=51)

TABLE I: Contact parts for each condition
Number of the case [times]
Contact part
Short step caseLong step case
Supported side hand
57
Opposite side hand
1916
Supported side knee
22
Opposite side knee
00Total2625
Notes : Supported side means the side of support legTotal
1235
4051

and the simulation duration was found in the short step case,but no such correlation was found in the long-step case.
IV. DISCUSSION

The results of the contact velocity for each part are
shown in Table II. Although there were some differencesin contact velocity between the short-step case and longstep case depending on the part that contacted with theground, no signiﬁcant difference was observed between thetwo conditions overall as a result of t-test ( p-value = 0.82).
Fig. 4 showed the relationship between the contact velocity
and the center-of-mass (COM) fall velocity at the beginningof the simulation (at the beginning of the support by theharness), and Fig. 5 showed the contact velocity with respect
to the simulation duration, which is the time period of
the simulated fall. In addition, the correlation coefﬁcientsof the contact velocity with the COM fall velocity andthe simulation duration for each condition are shown in
Table III. A positive correlation (r = 0.52) between theCOM fall velocity and contact velocity at the beginningof the simulation was found in the short step case, but nocorrelation was found in the long step case. Similarly, anegative correlation (r = -0.50) between the contact velocity

In the short-step case, a positive correlation was foundbetween the COM fall velocity at the beginning of thesimulation and the contact velocity, which indicates thatthe contact velocity increases as the COM fall velocityincreases. Furthermore, a negative correlation was foundbetween the simulation duration and contact velocity, whichindicates that the shorter the simulation duration is, thelarger contact velocity. These two correlations are intuitively
understandable, but the same results were not obtained in
the long-step case. This may be because it was possible tomitigate the fall impact before the ground contact in thelong-step case as compared to that in the short-step case.
Therefore, there were more types for fall strategies in thelong-step case. This result suggests that the difference in therecovery step length will affect the severity of fall injuriescaused by tripping.
In this study, no torque was applied to the lower limb joints
until the end of the simulation, which is the most severe
condition of a fall. However, in the case of an actual fall, it
is expected that joint torque will be generated in the support
leg which will be used for the recovery motion. Therefore, itis necessary to develop a method for estimating the reaction218[3] Y. Akiyama, K. Mitsuoka, S. Okamoto, and Y. Yamada, “Experimental
analysis of the fall mitigation motion caused by tripping based on themotion observation until shortly before ground contact,” Journal ofBiomechanical Science and Engineering, vol. 14, no. 1, pp. 18–00 510,
2019.
[4] J. Lo and J. A. Ashton-Miller, “Effect of upper and lower extremity
control strategies on predicted injury risk during simulated forward falls:
a study in healthy young adults,” Journal of biomechanical engineering,
vol. 130, no. 4, 2008.
[5] J. J. Eng, D. A. Winter, and A. E. Patla, “Strategies for recovery froma trip in early and late swing during human walking,” Experimental
brain research, vol. 102, no. 2, pp. 339–349, 1994.
[6] P. De Leva, “Adjustments to zatsiorsky-seluyanov’s segment inertia
parameters,” Journal of biomechanics, vol. 29, no. 9, pp. 1223–1230,
1996.
[7] V. Zatsiorsky, “The mass and inertia characteristics of the main segments of the human body,” Biomechanics, pp. 1152–1159, 1983.

Fig. 5: Scatter plot of contact velocity against simulation
duration (n=51)
TABLE III: Correlation coefﬁcients the contact velocity with
the COM fall velocity and the simulation duration for eachcondition
COM fall velocity-Contact velocty(in Fig. 4)
Simulation duration-Contact velocty(in Fig. 5)

Short step caseLong step case

0.52

0.11

-0.50

0.044

motion of the subject in order to estimate the actual fallmotion, and further research is needed in this respect as well.
V. CONCLUSIONSThe objective of this study was to investigate the effectof the recovery step length in the lowering strategy on thecontact velocity with the ground by estimating the natural
fall motion using a simulator. A sufﬁciently human model
was used in the simulator to estimate a more natural fallmotion than that observed in the experiment.
In conclusion, it was found that when the step length of therecovery leg during a fall is not sufﬁciently long, the possible
fall motion is limited and the simulation duration and COM
fall velocity at the start of the simulation affect the impact
velocity. In addition, it was suggested that the faller will beable to react before the ground contact and choose a wider
range of fall motions if the step length of the recovery leg
is long. This may have been the why the contact velocity inthe long step case was not affected by parameters such as
the simulation duration and COM fall velocity.
R EFERENCES[1] B. Johansson, “Fall injuries among elderly persons living at home,”
Scandinavian journal of caring sciences, vol. 12, no. 2, pp. 67–72,
1998.
[2] M. J. Pavol, T. M. Owings, K. T. Foley, and M. D. Grabiner, “Mechanisms leading to a fall from an induced trip in healthy older adults,”
The Journals of Gerontology Series A: Biological Sciences and MedicalSciences, vol. 56, no. 7, pp. M428–M437, 2001.

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