Download Metabolic Energy Consumption in a Box-Lifting Task

Metabolic Energy Consumption in a Box-Lifting Task: A Parametric
Study on the Assistive Torque
Mohammad S. Shourijeh, Moonki Jung, and Michael Damsgaard

Abstract— This study showcases effect of adding assistive
torques to the hip, knee, and ankle joints in the sagittal plane
on the total human body metabolic energy expenditure using
the AnyBody musculoskeletal modeling system. To this goal, a
box-lifting task was targeted and metabolic energy was
computed for several cases including when each of the three
joints was assisted at a time. Simulation results showed that
the hip joint assistance affects the total metabolic energy
consumption more than the knee and ankle joints.
I. INTRODUCTION
M
energy consumption has been shown to be a
serious criterion in human movements. Simulation
studies have used this measure to provide information on
optimality level of human motion [1]. Rate of VO2
consumption, which is deemed to be equivalent to overall rate
of metabolic energy consumption, have been simulated in
musculoskeletal dynamic simulations by means of simple [2]
to complex [3] mathematical models.
Additionally, metabolic energy has been used and
speculated to be important in efficacy of exoskeletons. How
an assistive device contributes to both the local joint-level
and total human body has been a design objective for the
neuro-rehabilitation engineers. Experimental measurement of
VO2 consumption have been used for assessing performance
of human body with and without wearing assistive devices.
However, VO2 rate only reflects the total metabolic energy
rate and is neither informative about effects at the local
muscles or joints nor the contribution of the local elements to
the total metabolic energy consumption. Therefore in design
of assistive devices, if the question is to evaluate efficiency of
the device at the joint level or in some applications,
essentially which joint must be assisted most, computer
simulations have the potential to play an important role.
The goals of this study were 1) to exemplify use of
musculoskeletal modeling for assistive wearables in a boxlifting task and 2) to determine which joint is the most crucial
to be assisted within the desired movement.
ETABOLIC
This work has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No. 680754 (The
MovAiD project, www.movaid.eu).
M. S. Shourijeh is with the AnyBody Technology, Aalborg, Denmark (email: [email protected]).
M. Jung is with the AnyBody Technology, Aalborg, Denmark
([email protected]).
M. Damsgaard are with the AnyBody Technology, Aalborg, Denmark
([email protected]).
II. METHODS
AnyBody modeling software [4] has been used to simulate
the box-lifting task. Metabolic energy rate model was a
modified model of [3]. Ground reaction forces were predicted
by the reaction force prediction tool in AnyBody to keep the
model dynamic equilibrium; for more details, please see [5].
A. Musculoskeletal model (MSM)
The full human body model in the AnyBody Managed Model
Repository (AMMR v1.6.3) was used for this study; see
Figure I. The model includes 63 segments, and 834 muscles.
The model was 75.6 Kg and 1.80 m. Three-element Hill-type
muscle model was chosen for the muscles, which consists of
a contractile element (CE), a parallel elastic element (PE),
and a serial elastic element (SE) [6].
B. Box-lifting movement
The vertical kinematics of the box was prescribed by an
assumed 6th order polynomial with zero velocity and
acceleration for the initial and final points. Following that,
the kinematics of the model was predicted using the center of
mass driver in AnyBody while keeping the center of pressure
within the model feet region in order to maintain model
balance.
C. Metabolic energy
The model for the muscle metabolic power E was defined as
E  H W
(1)
which models the decrease rate in muscle internal energy. In
equation (1), H is the heat rate generated by the muscle
defined as the sum of basal, activation, maintenance, and
shortening/lengthening heat rates. W is the external work
done by the muscle and defined as
(2)
W   fce (l ce , vce , a)vce
where l ce and v ce are length and velocity of the contractile
element (CE) in the muscle model, respectively; f ce is the CE
force function and a is muscle activation.
The total body metabolic energy Etot was defined as
N
tf
i 1
t0
Etot    Ei dt
(2)
where t 0 and t f are initial and final movement time, which
were set to 0 s and 3 s, respectively; i refers to muscle index,
and N is the total number of muscles in the model.
D. Assistive torque
The assistive torque was modeled as a rotational actuator,
torque of which is proportional to the joint angle as
Tassist   K
(3)
where K (Nm/rad) is the pseudo-stiffness of the assistive
device that can be negative, zero (no assist), or positive. In a
parametric study, K was changed between [-300, 300]
Nm/rad for each joint at a time that seemed to be a reasonably
large range for this study.
Fig. I. The musculoskeletal model in AnyBody applied for the boxlifting task. Ground reaction forces and moments are predicted by
AnyBody.
E. Box Interaction with the MSM
A cube (0.4x0.4x0.4 m3) with a mass of 5 Kg, with
Ixx=Iyy=Izz=0.768 Kgm2 was assumed for the box. The
interaction between the hands and the two sides of the box
was modeled as two weld joints. Box bottom was also
assumed to move from 0.1 m to 0.6 m of the floor.
III. RESULTS
Total Metabolic Energy
Rate (W)
Figure II depicts the total metabolic power for the four cases
in this study: unassisted, and optimally-assisted at hip
(K=+100), knee (K=+50), and ankle (K=-100) joints. The
total metabolic energy for these cases were 30.3, 25.6, 29.2,
and 29.7 kJ, respectively.
400
Ankle -100
Knee +50
Hip +100
None
350
300
250
200
150
100
0.0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 3.0
Time (s)
Fig. II. Simulated total metabolic power of the box-lifting task for
unassisted (black), optimally hip-assisted (green), knee-assisted (blue), and
ankle-assisted (red) scenarios.
IV. DISCUSSION
All the three optimal assisted cases led to less metabolic
energy required to perform the task. Out of the three joints,
hip joint was found to be most influential on the design
criterion (i.e. over 31% less metabolic energy). It must be
noted that the hip flexion is negative in the model; therefore,
a positive pseudo-stiffness for the hip joint will lead to a
positive hip extensor torque, which is desired for assistance in
the lifting phase. The optimal joint to assist can be dependent
on the style of lifting (stooped versus squat). It must be
pointed out that in the simulations of this study, the model
used the hip more than the knee for the lifting task. The
absolute angle variations for the hip and the knee joints were
1.58 rad and 0.524 rad, respectively, which showed that the
lifting was more like a stooped style. It is speculated that if a
human subject chooses to perform a squat style lifting, knee
will be the optimal joint for assistance.
The less is the metabolic energy required from the human,
the more comfortable will be the human to perform the task;
however, the required assisted torque from the external
device might be constrained by the motor size, material
properties, etc., which might lead to less assistive torque
generation capability. Therefore, computer modeling of the
assistive devices must be done with consideration of design
and fabrication limitations in order to reach a realistic optimal
design.
V. CONCLUSIONS AND FUTURE WORK
Effect of adding external assistive torques to the hip, knee,
and ankle joints was studied using the AnyBody software.
Total metabolic energy over the period of the box-lifting
motion was computed and utilized as the design variable. As
metabolic energy measure has been broadly used in studies
for design optimization of the exoskeletons, musculoskeletal
modeling environments such as AnyBody that can provide
such information in detail at the joint and muscle levels can
significantly contribute.
Model kinematics was assumed to be the same between
the four cases. However, as the lifting styles might be
subjectively different, studying how a range of lifting styles
from stooped to squat affects the metabolic energy
consumption could be a future research study.
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