To reduce energy consumption while a human is walking with
different loads, an active energy storage mechanism and a gait cycle
prediction method are proposed, and then a wearable ankle assistance robot
is developed. A motor, a clutch, and elastic rods are placed strategically in
the active energy storage mechanism to achieve energy storage and release.
During the period when the ankle does not generate torque, the clutch is
closed, and the elastic rods are driven by the motor to produce deformation
for energy storage. When the ankle generates torque, the motor is stopped
and the clutch is opened, and then energy is released. Assisted force is
transmitted to the human heel by a flexible transmission device to achieve
walking assistance. The deformation length of the elastic rods can be
changed to achieve assisted force adjustment for different loads. Based on
the hip angular displacement and heel pressure, the gait cycle can be
obtained with the proposed prediction method, and then assistance control
can be achieved. Consequently, the development of a wearable ankle
assistance robot is realized, a walking assistance experiment with different
loads is completed, and the net metabolic cost is used to indicate the
energy consumption. The experimental results show that the net metabolic
cost of the participants is reduced by averages of 5.30 %, 5.67 %, and
4.84 % with 0, 4, and 8 kg loads respectively. The reduced net
metabolic costs are compared with other research results; the reduced net
metabolic costs are close to the others, but the motor power in this work is
lower.
National Natural Science Foundation of ChinaU1813222Introduction
With progress in robotic research, robots are playing an increasingly
important role in military, industry, medicine, and daily life; robotics
provides new solutions to traditional problems (Qiao et al., 2022; Qi et
al., 2021). Walking with loads often occurs in the military (Mala et al.,
2015; Jones, 2005) and in daily life (Saelens et al., 2003). Compared with
walking without any load, walking with a load requires the consumption of
more energy (Browning et al., 2007; Hogan et al., 1998). Exoskeletons, such
as the Berkeley lower extremity exoskeleton (BLEEX) (Zoss et al., 2006) and
the Hybrid Assistive Limb (HAL) (Tsukahara et al., 2014), can be used to assist
humans in walking while carrying loads. However, this kind of exoskeleton
with several joints requires a complex control method and a greater energy
cost. In recent years, researchers have begun to focus on assisting single
or double lower limb joints with wearable devices (Kang et al., 2022; Han et al., 2020; Wang et al., 2020b).
In the process of walking, the ankle provides approximately 35 % of the
mechanical work and accounts for 19 % of the metabolic cost (Gordon et
al., 2006; Sawicki and Ferris, 2009), so providing assisted force for the
ankle and reducing the activity of the leg muscles can reduce the energy
consumption of the human body (Wang et al., 2020a; Xie et al., 2021). In
view of such an important effect on the ankle, ankle assistance robots have
attracted the attention of researchers, and several excellent studies have
been proposed. The remainder of this article is organized as follows.
Section 2 introduces the related work of ankle assistance robot. Section 3
presents the design and working principle of the ankle assistance robot.
Section 4 introduces the control method of the robot. Section 5 carries out
experiments to verify the effectiveness of the ankle assistance robot.
Section 6 concludes this article.
Related work
According to the operation mode, ankle assistance robots can be divided into
two categories: passive ankle assistance robots and active ankle assistance
robots. Passive ankle assistance robots have no power source or control
systems. They use components such as springs or artificial muscles to assist
ankle movement in the process of walking. In the flexible exoskeleton
developed by Park et al. (2014), four pneumatic artificial muscles
were used to simulate the structure of human muscles and ligaments to
provide power for the ankle. The passive ankle exoskeleton designed by
Collins et al. (2015) and Diller et al. (2016) had a light elastic
element that was parallel to the calf muscle, and a ratchet pawl mechanism
was used to achieve energy storage and release. As a result, the muscle
burden and energy consumption are reduced. The spring rate of the
exoskeleton was 180 Nm rad-1, and the metabolic cost of walking flat on the
treadmill at a speed of 1.25 m s-1 was reduced by 7.26±2.6 %. The
passive ankle assistance robot developed by Yandell et al. (2019)
was suitable for daily wear. It included a clutch on the sole and a linear
spring on the back of the shank. During walking, energy could be stored and
released in combination with the clutch and spring. The robot could adapt to
different walking speeds and did not limit the non-sagittal movement of
joints, and the average soleus activity was reduced by 5 %–17 % when the
robot was worn. Owing to the lack of a power source and control system, the
mechanical structure of the passive ankle assistance robot was simple, and
the weight was light.
In contrast to passive ankle assistance robots, active ankle assistance
robots usually include a power source, one or more active actuators, and
sensing and control systems that monitor human gait information in real time
and control the assisted force in the process of walking. Galle et al. (2014) used pneumatic muscles as an actuator of the ankle-foot exoskeleton;
information on foot switches is used to impose a specific timing and
duration in which the pneumatic muscles are inflated. Under the condition of
walking on an inclined treadmill (15 %) at a speed of 1.1 m s-1, the net
metabolic cost is reduced by 8.0±6.2 % when unloaded. When a load
of 22.5 kg is added, the net metabolic cost is reduced by 10.1±6.8 %. Asbeck et al. (2015) developed a flexible exoskeleton for
walking assistance. The gait cycle was obtained by judging the heel landing
time using the plantar pressure sensor, and the motor displacement curve was
generated according to the gait cycle. Then the motor (power: 200 W) drove
the pulley cable to assist the ankle. Four subjects wearing the system and
carrying 23 kg on a treadmill at a speed of 1.25 m s-1 reduced the average
optimal metabolic cost by 6.4 % and increased the metabolism by 9.3 %.
Two kinds of light ankle assistance robots named Alpha and Beta were
proposed by Witte et al. (2015). These robots had a power source and
a controller placed outside the body, and the tension was transmitted
through the Bowden cable. The assisted torque was calculated with the ankle
angle and gait phase information. Alpha used the tension sensor to measure
the tension of the Bowden cable, and Beta used four strain gauges to
directly measure the torque of the ankle mechanism. In the walking trials,
the peak average measured torque generated by Alpha and Beta was 80 and
87 Nm respectively. Mooney and Herr (2016) and Mooney et al. (2014)
built an autonomous exoskeleton that used a DC motor (power: 200 W) to drive
the rope wound on a winch to provide mechanical power for an ankle, with an
inertial measurement unit (IMU) located in the front of the lower leg to
judge the gait phase of a human. The net metabolic cost of walking flat on a
treadmill at a speed of 1.4 m s-1 with the exoskeleton was an 11 % ± 4 % reduction compared to the cost of walking without the exoskeleton
under the condition of no load. For the subjects walking with a 23 kg load,
the net metabolic cost reduction was approximately 8 % ± 3 %. Wang et al. (2020) also used a flexible cable to transmit the assisted
force, and the ankle movements of plantar flexion and dorsal flexion were
driven by one motor. When a subject was wearing the exoskeleton with the
power on, the muscle activity of the soleus was reduced by 5.2 % compared
to the state without the exoskeleton. Shao et al. (2021) proposed a
load-adaptive actuator with variable stiffness to actuate ankle exoskeleton;
the ankle consumption can be reduced 0.23, -3.36, -0.94, and 1.63 J, with
0, 4, 12, and 20 kg loads respectively. Xie et at. (2021) designed
and manufactured a lightweight flexible and wearable ankle-assisted robot. The Bowen cable is used as the transmission medium of power which is generated by a
motor (150 W), and the best net metabolic cost of eight experimenters wearing
this system for walking flat at a speed of 1 m s-1 on a treadmill was reduced
by 6.94 %, 5.23 %, 3.64 %, with 10 %, 20 %, and 30 % of the body
mass loads respectively.
The passive ankle assistance robot stores energy through the change of the
ankle movement angular displacement during walking. This type of robot has
the advantages of having a simple structure and being lightweight, but the assisted
force provided by the robot is usually small and uncontrollable. The active
ankle assist robot has an actuator that can provide controllable assisted
force, but when a large assisted force is required (such as when the human
body is loaded), a higher power actuator and a larger capacity battery are
required. In this study, an ankle assistance method with active energy
storage is proposed, and an ankle assistance robot is developed that can
provide a large assisted force with a low-power motor and a simple control
method. The improvement achieved in this work will contribute to progressing
the development of ankle assistance robots and their applications to persons
who walk with loads.
Mechanism of wearable ankle assistance robotActive energy storage method
In the process of walking, the torque of an ankle is concentrated at the end
of the supporting phase, with a short duration and a large peak value. When
people walk with a weight, the ankle load is further increased. The energy
storage and release of a passive ankle assist robot are realized by the
change of the ankle angular displacement during the support phase. Owing to
the lack of a power supply and motor, the structure of a passive ankle
assistance robot is simple, and the assisted force does not need to be
controlled in real time, but the assisted force provided by the robot is
small and unadjustable. An active ankle assistance robot has an actuator and
a control system, and the assisted force is controllable. However, the load
of a user will increase with a high-power motor and a high-capacity battery.
Combining the advantages of passive and active ankle assist robots, an
active energy storage method for ankle assistance is proposed. This method
is shown in Fig. 1.
Active energy storage method.
According to the proposed method, an active energy storage mechanism is
designed. An electromagnetic clutch is driven by a motor with a synchronous
belt, the output of the electromagnetic clutch is connected with a reel, and
a Bowden cable is used to achieve flexible transmission and is wound on the
reel. One end of the elastic rod is fixed on the reel, and the other end
passes through the hole on the adjusting mount. The adjusting mount is
connected to the adjusting handle through threads. The adjusting mount can
be moved along the guide bar to adjust the deformation length of the elastic
rod (L) by rotating the adjusting handle. Then the assisted force can be
changed.
The proposed active energy storage method works in two stages. When there is
no torque generated by the ankle, the electromagnetic clutch is closed. The
motor drives the elastic rod between the adjusting mount and the reel to
generate deformation, and then the energy storage is achieved. When the
ankle generates torque, the electromagnetic clutch is opened. Then the reel
is driven to rotate by the elastic rod in the energy storage mechanism, and
the assisted force on the user's heel is generated through the flexible
transmission device, thereby providing the assisted force for the ankle. The
output force (F′) is determined by multiple parameters such as the angular
displacement (φ), the deformation length of the elastic rod (L), the
diameter of the elastic rod (d), the radius of the reel (R), the elastic
modulus of the elastic rod (E), and the number of elastic rods (N). The
proposed active energy storage method has the following advantages:
During walking, the ankle torque generation time is short. The active
energy storage method can store energy in the ankle's non-force-generation
stage in a gait cycle. Then the energy storage time is long. Therefore, a
small power motor can be used with a large reduction ratio gearbox to
provide larger assisted torque for the user.
The deformation length of the elastic rod in the energy storage
mechanism can be adjusted according to the load of the user. Therefore, the
stored energy can be changed, and appropriate assisted forces can be provided
for different loads.
According to the user gait cycle, the controller only needs to control
the motor to rotate at a certain angle and control the electromagnetic
clutch to open or close. The controller does not need to control the
assisted force in real time, so the control system is easy to develop.
Sketch of ankle assistance robot
Based on the proposed active energy storage method, a wearable ankle
assistance robot is designed. A sketch of the robot is shown in Fig. 2. A
battery, motor, controller, and active energy storage mechanism are set at
the human waist. A Bowden cable is arranged along the outside of the thigh.
Two IMUs are arranged on the outside of each thigh, and pressure sensors are
arranged on the heels. The pressure sensors are combined with the two kinds
of sensing data to obtain the user's gait cycle information. One end of the
Bowden cable is connected to the energy storage mechanism, and the other end
is connected to the heel of the human body to achieve flexible transmission,
and the assisted force is transferred to the heel by the Bowden cable.
Because of the transmission loss and hysteresis of the Bowden cable (Veneman
et al., 2006), the assisted force (Fx) is less than the output force
(F′). The relationship between the assisted force (Fx) and the output
force (F′) can be calculated with
F′Fx=efαx,
where f is the friction coefficient, and αx is the wrap angle
between two ends.
Sketch of wearable ankle assistance robot.
Figure 3 shows the assisted force acting on the human heel. The changes in
the steel wire length of the Bowden cable and the assisted torque for the
ankle can be estimated with
l02=a2+b2-2abcosα0Δlt=l0-a2+b2-2abcos(α(t))Txt=Fx(t)⋅a⋅sin(β(t)),
where l0 is the length of the steel wire, α0 is the initial
angle between line a and line b, Δl is the change of the steel wire
length, and Tx is the assisted torque for the ankle. The change of the
steel wire length (Δl) is used to calculate the angular displacement
(φ) of the active energy storage mechanism, and the assisted torque
(Tx) is used to estimate the torque for the ankle that is generated by
the assisted force of the robot.
Assisted force acting on the human heel.
Mechanical analysis of elastic rod
The assisted force is generated by the deformation of the elastic rod. An
appropriate assisted force can be provided by designing the diameter and
length of the elastic rod appropriately. The cross-section of the elastic
rod is set to be circular and distributed along the reel uniformly. Figure 4
shows the deformation effect of the elastic rod during torque (with two
elastic rods).
Elastic rod deformation.
When the reel is driven by the motor to rotate from the balance position to
a certain angle, the contact point of the elastic rod and the reel is
transferred from point Q0 to point Q1 in the circumferential
direction. The relationship between the displacement (w1) and the
rotation angle (φ) can be expressed by
w1=2Rsin(φ2),
where R is the rotation radius of the elastic rod end.
Owing to the structural characteristics of the energy storage mechanism, the
elastic rod only has bending deformation and no torsional deformation, so
the deformation of the elastic rod can be approximated as the bending of a
cantilever beam. With the rotation of the reel, a concentrated load opposite
to the rotation direction is applied to the elastic rod by the adjusting
mount. The cantilever beam bending model is shown in Fig. 5.
Cantilever bending model.
Because the deformation of the elastic rod is approximated as the bending of
the cantilever beam, the deflection value (w2) can be calculated by
w2=FL33EI,
where F is the concentrated load on the free end of the elastic rod, L is the
effective bending length of the elastic rod, E is the elastic modulus of the
elastic rod, and I is the section moment of inertia of the elastic rod.
According to Figs. 4 and 5, w1 is equal to w2. Then the force F
acting on the end of the elastic rod can be obtained from Eqs. () and (). The force F can be described as
F=6EIRsin(φ2)L3.
The moment of inertia of a circular section can be calculated with
I=πd464,
where d is the diameter of the elastic rod. Fc that generates torque on
the reel is the component of the force F in the vertical radius, which can be
calculated with
Fc=3πERd4sin(φ2)cos(φ2)32L3.
The torque generate by one elastic rod can be calculated with
T=FcR.
Energy storage capacity analysis
Based on the working principle of the energy storage mechanism, the energy
storage capacity of the energy storage mechanism is analyzed, which can
provide a basis for the energy storage control and assisted force adjustment
of the robot.
By comparing the performance parameters such as the elastic modulus and
tensile strength, beryllium copper is selected as the material of the
elastic rod. The elastic modulus (E) and yield strength of beryllium copper
(σs) are 128 GPa and 1035 MPa respectively. During energy
storage, the elastic rod is bent and deformed, and the maximum bending
stress can be calculated with
σmax=MWZ,
where Wz is the bending section coefficient of the circular section rod,
and M is the bending moment of the elastic rod at the adjusting mount.
Wz and M can be calculated with
WZ=πd332M=FL.
Then the maximum bending stress is
σmax=32Mπd3=32FLπd3.
Considering the safety of the energy storage mechanism, it is necessary to
ensure that the elastic rod meets the requirements of the allowable load
stress during use, and Eq. () should be satisfied in the design.
σmax≤[σ]
The allowable tensile stress of an elastic rod is the yield strength
σs divided by the safety factor n, then
32FLπd3≤σsn.
With Eqs. () and (), the minimum length of the selected elastic rod
can be calculated with
L≥3nERdsin(φ2)σs.
It can be known from Eq. () that after the parameters of the elastic
rod are determined, the minimum selected length is only related to the
rotation angle (φ) of the reel. A circular section elastic rod with
a diameter of 2 mm is selected, and the safety factor n is set to 1.5.
Considering factors such as the volume and the installation of the active
energy storage mechanism, the distance R from the contact point of the
elastic rod to the center of the reel is set to 15 mm. The rotation angle of
the reel is calculated based on the structure of the flexible transmission
device and the range of the ankle movement. Then, with Eq. (), the
minimum length of the elastic rod can be calculated, which should not be
less than 64.75 mm. The adjustment range of the elastic rod is set to 65–80 mm.
According to the force requirements of the ankle joint and the structure of
the energy storage mechanism, the number of elastic rods (N) is set to 10.
The torque generated by all elastic rods can be calculated with
T′=TN.
The winding radius r of the wire is 12.5 mm (the r is shown in Fig. 6), and
the output force (F′) generated by the torque T′ acting on the Bowdoin line can
be calculated with Eq. ():
F′=T′r.
According to the adjustment range of the elastic rod and the rotation angle
of the reel, the relationship between the maximum output force and the
deformation length of the elastic rod can be obtained from Eqs. () and
(). The result is shown in Fig. 10. The output force of the designed
active energy storage mechanism decreases monotonically with the increase in
the deformation length of the elastic rod, an approximately linear
relationship, so it is convenient for the adjustment of the output force.
Reel construction.
Control method of the robotMoment of assistance
To realize the power-assisted function, it is necessary to obtain the
moments when the ankle generates force and when the robot provides assisted
force. Much information can be used for robot decision making (Qiao et
al., 2022; Qi et al., 2021), but it is difficult to obtain the moment of assistance
directly, so a method is proposed to detect the gait cycle by combining the
information on the hip joint angle and the plantar pressure. Then the moment of assistance can be calculated.
An IMU is placed on the outside of the thigh to obtain the hip angle
information during walking. When the human body is standing, the thigh is
perpendicular to the ground, and the hip joint angle at this time is defined
as 0∘. The angle is positive when flexed, and the angle is
negative when extended. The human gait cycle is shown in Fig. 7. According
to the literature (Dai and Tang, 1982), the minimum value of the hip angle
appears at 53.8 % of the gait cycle during walking, and then the angle
increases until 84.6 % of the gait cycle.
Gait cycle analysis.
During walking, the gait cycles are different, but the ratios between the
hip flexion times and the gait cycles are approximately equal. Therefore,
the hip flexion time and the ratio can be used to predict the gait cycle.
The ankle does positive work between 40 % and 60 % of the gait cycle
(Collins et al., 2005), and the peak value of the ankle torque during
walking is at about 50.1 % of the gait cycle (Tang and Dai, 1986), so the
starting time of the assistance should between 40 % and 50.1 % of the
gait cycle. In this study, the starting time of the assistance is set at
43 % of the gait cycle (during the experiment, testers feel comfortable at
this moment). Based on the characteristics of human walking, the pressure
sensor is used to obtain the heel landing time as the start time of a gait
cycle. The IMU is used to obtain the angular displacement of the hip. After
the hip movement peak time is detected, the hip flexion time is obtained by
subtracting the trough time. At the same time, the gait cycle time is
obtained by using the difference between the two peak times. Then the ratio
is obtained with the flexion time divided by the cycle time. The predicted
gait cycle can be obtained using the flexion time of the previous gait cycle
divided by the average ratio of the previous three gait cycles. The starting
time of the power assistance can be calculated with
pk-1=13∑i=k-3k-1tpeaki-ttroughi-1tpeaki-tpeaki-1Tprek=tpeakk-1-ttroughk-1pk-1tassistk=tzerok+0.43Tprek,
where k is the current gait number, k-1 is the last gait number, p is the
predicted ratio, Tpre is the predicted gait cycle, tpeak is the peak
time of the hip, ttrough is the trough time of the hip, tzero is the
start time of the gait, and tassist is the moment when the robot begins
to provide assisted force. The proposed assistance moment calculation method
has the following advantages:
The gait cycle is predicted using the hip angular displacement, whose
shape is similar to a sinusoidal curve, with only one maximum and one
minimum value in a gait cycle, so the prediction result is more stable and
accurate.
Using the pressure information of both heels and the angular
displacement of the hips, the human states of walking and standing can be
estimated accurately, so the incorrect operation of the robot can be
avoided.
Control method
The robot control system is made up primarily of a controller, an IMU, a
pressure sensor, a DC motor, and an electromagnetic clutch. The robot
controlling principle is shown in Fig. 8. First, the gait information is
collected by the IMU and the pressure sensor and is then transmitted to the
controller. Second, the assistance time is calculated by the controller with
the heel pressure information and the hip joint angular displacement. Third,
the control command is sent to the DC servo motor driver and the
electromagnetic clutch to coordinate the motor and clutch.
Diagram of the robot control system.
The same control method is applied to the left leg and the right leg of the
user. The start time of the energy storage is when the leg is in the swing
phase and the angular displacement of the hip joint is 0∘. After
the energy storage start time is obtained, the electromagnetic clutch is
closed by the controller. At the same time, the motor is controlled to
rotate for energy storage, and the motor stops after rotating to the preset
angle.
After the start time of the gait cycle is obtained according to the heel
pressure information, the starting time of assistance is calculated with Eq. (). When
the power assistance time occurs, the electromagnetic clutch is opened, and
the energy storage mechanism drives the human ankle movement through the
flexible transmission mechanism to achieve walking assistance.
ExperimentTest of energy storage mechanism output force
An experimental platform is built to obtain the maximum output force of the
energy storage mechanism. This platform is shown in Fig. 9. The energy
storage mechanism is connected to the dynamic torque sensor, and a servo
motor is used to drive the energy storage mechanism to rotate. The maximum
output force is calculated with the test torque divided by the winding
radius r of the wire. The result is shown in Fig. 10. The test results and
calculation results have similar trend. But the tested output forces are
slightly greater than calculated values, with an approximately constant
difference. This may be for several reasons, such as the difference
between the calculated parameters and the actual parameters of elastic rods
and other parts, the axiality error between the torque sensor and the energy
storage mechanism, and friction. The output force supplied by the robot
should reference the test result. With the output force, the assisted force
for the user can be calculated with Eq. ().
Experimental platform for the output force test.
Maximum output force test result.
Ankle assistance robot.
Walking assistance experiment
The prototype of the wearable ankle assistance robot is shown in Fig. 11.
According to the maximum torque required by the energy storage mechanism, a
90 W DC servo motor (RE35 Maxon) and an electromagnetic clutch (HB-YC-42,
Shenzhen Haibosi Technology Co., Ltd) are selected, and the reduction ratio
of the gearbox is 26:1. An NI myRIO-1900 (from NI Inc.) is used as the
controller for the robot. The active energy storage mechanism and the
controller are placed at the user's back and waist, and the mobile power
supply is arranged behind the controller. The IMUs (BWT901CL, WitMotion
ShenZhen Co., Ltd) are fixed in place by a flexible bandage. The pressure
sensors (FSR 402, SparkFun Electronics Inc.) are placed in the heels of the
shoes. The cardiopulmonary function tester (K4b2, Cosmed Inc.) is used to
measure the metabolic cost of the user. The walking frequency of a healthy
person is about 0.8 Hz; when loaded with weight, the walking frequency maybe
lower. The control cycle was set to be 40 ms, which means the control
frequency is 25 Hz.
During the experiment, participants walk on a treadmill, and the metabolic
costs of the participants in three states are measured: the resting state,
walking without the assisted robot, and walking with the assisted robot. The
net metabolic cost is obtained by subtracting the resting state metabolic
cost from the walking metabolic cost, which is used to indicate the energy
expenditure during walking. The assistance effect of the robot is evaluated
by comparing the net metabolic costs of wearing and not wearing the robot.
The data unit of the cardiopulmonary function tester is calories per second (cal s-1) in this
study, and the unit of the metabolic cost is watts per kilogram (W kg-1); the
value conversion can be realized by the following equation:
MC=4.184DMb,
where MC is the value (in W kg-1) of the metabolic
cost, D is the value (in cal s-1) collected by the cardiopulmonary
function tester, and Mb is the body weight (in kg) of the
participant.
Walking on the treadmill with robot and load.
Net metabolic cost without robot assistance and with robot
assistance under different loads. (a) Load: 0 kg. (b) Load: 4 kg. (c) Load: 8 kg.
Three participants participate in the experiment, and the information on
the participants is shown in Table 1. Different assisted forces are supplied
to participants with different loads. Because the accurate calculation of
the assisted force is difficult, the output force of the energy storage
mechanism is taken as a reference. The load and the assisted force are shown
in Table 2.
In each experiment, participants walk for 6 min at a speed of 4.5 km h-1
(Fig. 12), and the net metabolic cost data of the last 2 min were
analyzed. The net metabolic costs of the three participants are shown in
Fig. 13.
It can be observed from Fig. 13 that the average net metabolic cost is
reduced by 5.30 % when the participants have no load, and the maximum
reduction is 5.53 %. When a 4 kg load is carried by the participants, the
average net metabolic cost is reduced by 5.67 %, and the maximum reduction
is 6.11 %. When an 8 kg load is carried by the participants, the average
net metabolic cost is reduced by 4.84 %, and the maximum reduction is
5.61 %.
The test result shows that the net metabolic costs of all the participants
under different loads are reduced. This means that the developed wearable
ankle assistance robot can reduce the energy consumption of the human body
by providing assisted force for the ankle. The net metabolic cost reductions
when the participants are under loads are close to the reductions in the
case without loads. This indicates that the robot's function of adjusting
the assisted force for different loads is effective.
Although the expected functions are realized, there are still some problems
that need to be improved. For example, the weight of the developed robot is
about 5.3 kg, and participants feel heavier after wearing it. Some energy is
consumed by the friction that is generated in the process of the Bowden
cable working. The robot is inconvenient to wear and take off. About
2 min is needed to wear it or take it off by a participant without the help of others.
These problems impact the assistance effect of the robot and will be
improved in future study.
Conclusions
Based on the proposed active energy storage ankle assistance method, an
active energy storage mechanism is designed and developed that achieves the
function of energy storage using a small power motor with a larger reduction
ratio gearbox. By adjusting the deformation length of the elastic rods, the
energy storage capacity of the energy storage mechanism can be changed,
thereby providing an adjustable assisted force for the user.
With the information on the IMU, the information on the pressure sensor,
and human gait cycle characteristics, the moment of assisting the ankle can
be obtained. According to the assistance time and the start time of the
energy storage, the movement of the motor and the electromagnetic clutch can
be controlled accurately with the controller. Then the functions of storage
and release of energy can be realized, and the assisted force can be
provided by the robot for the user.
A wearable ankle assistance robot with the function of active energy storage
is developed, and a walking assistance experiment is carried out. The
experimental results show that the net metabolic cost of the participants is
reduced by averages of 5.30 %, 5.67 %, and 4.84 % with 0, 4, and
8 kg loads respectively. The results show that with different loads, the
robot can reduce participant energy consumption, and the assisted walking
function is achieved.
Data availability
All data included in this study are available upon request by contact with the corresponding author.
Author contributions
Conceptualization, JL and DY; investigation, KY; methodology, JL; project administration, JL; resources, DY; software, KY;
validation, JL; writing (original draft preparation), KY;
writing (review and editing), JL and KY. All authors have read and
agreed to the published version of the manuscript.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.
Acknowledgements
The authors would like to thank Shijie Guo for his valuable insight and advice. The authors would also like to thank Jingyuan Qi, Liang Zhao, and Lei Zhao for their help with the testing of human subjects.
Financial support
This research has been supported by the National Natural Science Foundation of China (grant no. U1813222).
Review statement
This paper was edited by Zi Bin and reviewed by six anonymous referees.
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