scholarly journals Estimation of the Continuous Walking Angle of Knee and Ankle (Talocrural Joint, Subtalar Joint) of a Lower-Limb Exoskeleton Robot Using a Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2807
Author(s):  
Taehoon Lee ◽  
Inwoo Kim ◽  
Soo-Hong Lee
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Lower Limb ◽  
Subtalar Joint ◽  
Walking Speed ◽  
Knee Angle ◽  
Talocrural Joint ◽  
Angle Estimation ◽  
Lower Limb Exoskeleton ◽  
Exoskeleton Robot

A lower-limb exoskeleton robot identifies the wearer′s walking intention and assists the walking movement through mechanical force; thus, it is important to be able to identify the wearer′s movement in real-time. Measurement of the angle of the knee and ankle can be difficult in the case of patients who cannot move the lower-limb joint properly. Therefore, in this study, the knee angle as well as the angles of the talocrural and subtalar joints of the ankle were estimated during walking by applying the neural network to two inertial measurement unit (IMU) sensors attached to the thigh and shank. First, for angle estimation, the gyroscope and accelerometer data of the IMU sensor were obtained while walking at a treadmill speed of 1 to 2.5 km/h while wearing an exoskeleton robot. The weights according to each walking speed were calculated using a neural network algorithm programmed in MATLAB software. Second, an appropriate weight was selected according to the walking speed through the IMU data, and the knee angle and the angles of the talocrural and subtalar joints of the ankle were estimated in real-time during walking through a feedforward neural network using the IMU data received in real-time. We confirmed that the angle estimation error was accurately estimated as 1.69° ± 1.43 (mean absolute error (MAE) ± standard deviation (SD)) for the knee joint, 1.29° ± 1.01 for the talocrural joint, and 0.82° ± 0.69 for the subtalar joint. Therefore, the proposed algorithm has potential for gait rehabilitation as it addresses the difficulty of estimating angles of lower extremity patients using torque and EMG sensors.

scholarly journals Design of a 2DoF Ankle Exoskeleton with a Polycentric Structure and a Bi-Directional Tendon-Driven Actuator Controlled Using a PID Neural Network

Actuators ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 9
Author(s):  
Taehoon Lee ◽  
Inwoo Kim ◽  
Yoon Su Baek
Keyword(s):  
Neural Network ◽  
Degrees Of Freedom ◽  
Plantar Flexion ◽  
Length Change ◽  
Talocrural Joint ◽  
Lower Limb Exoskeleton ◽  
Exoskeleton Robot ◽  
The Difference ◽  
Ankle Exoskeleton ◽  
The Stability

Lower limb exoskeleton robots help with walking movements through mechanical force, by identifying the wearer’s walking intention. When the exoskeleton robot is lightweight and comfortable to wear, the stability of walking increases, and energy can be used efficiently. However, because it is difficult to implement the complex anatomical movements of the human body, most are designed simply. Due to this, misalignment between the human and robot movement causes the wearer to feel uncomfortable, and the stability of walking is reduced. In this paper, we developed a two degrees of freedom (2DoF) ankle exoskeleton robot with a subtalar joint and a talocrural joint, applying a four-bar linkage to realize the anatomical movement of a simple 1DoF structure mainly used for ankles. However, bidirectional tendon-driven actuators (BTDAs) do not consider the difference in a length change of both cables due to dorsiflexion (DF) and plantar flexion (PF) during walking, causing misalignment. To solve this problem, a BTDA was developed by considering the length change of both cables. Cable-driven actuators and exoskeleton robot systems create uncertainty. Accordingly, adaptive control was performed with a proportional-integral-differential neural network (PIDNN) controller to minimize system uncertainty.

scholarly journals The Impact of Load Style Variation on Gait Recognition Based on sEMG Images Using a Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8365
Author(s):  
Xianfu Zhang ◽  
Yuping Hu ◽  
Ruimin Luo ◽  
Chao Li ◽  
Zhichuan Tang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Lower Limb ◽  
Gait Recognition ◽  
Training Set ◽  
Lower Limb Exoskeleton ◽  
The Impact ◽  
Semg Signals

Surface electromyogram (sEMG) signals are widely employed as a neural control source for lower-limb exoskeletons, in which gait recognition based on sEMG is particularly important. Many scholars have taken measures to improve the accuracy of gait recognition, but several real-time limitations affect its applicability, of which variation in the load styles is obvious. The purposes of this study are to (1) investigate the impact of different load styles on gait recognition; (2) study whether good gait recognition performance can be obtained when a convolutional neural network (CNN) is used to deal with the sEMG image from sparse multichannel sEMG (SMC-sEMG); and (3) explore whether the control system of the lower-limb exoskeleton trained by sEMG from part of the load styles still works efficiently in a real-time environment where multiload styles are required. In addition, we discuss an effective method to improve gait recognition at the levels of the load styles. In our experiment, fifteen able-bodied male graduate students with load (20% of body weight) and using three load styles (SBP = backpack, SCS = cross shoulder, SSS = straight shoulder) were asked to walk uniformly on a treadmill. Each subject performed 50 continuous gait cycles under three speeds (V3 = 3 km/h, V5 = 5 km/h, and V7 = 7 km/h). A CNN was employed to deal with sEMG images from sEMG signals for gait recognition, and back propagation neural networks (BPNNs) and support vector machines (SVMs) were used for comparison by dealing with the same sEMG signal. The results indicated that (1) different load styles had remarkable impact on the gait recognition at three speeds under three load styles (p < 0.001); (2) the performance of gait recognition from the CNN was better than that from the SVM and BPNN at each speed (84.83%, 81.63%, and 83.76% at V3; 93.40%, 88.48%, and 92.36% at V5; and 90.1%, 86.32%, and 85.42% at V7, respectively); and (3) when all the data from three load styles were pooled as testing sets at each speed, more load styles were included in the training set, better performance was obtained, and the statistical analysis suggested that the kinds of load styles included in training set had a significant effect on gait recognition (p = 0.002), from which it can be concluded that the control system of a lower-limb exoskeleton trained by sEMG using only some load styles is not sufficient in a real-time environment.

Design of M-G modal space sliding mode control for lower limb exoskeleton robot driven by electrical actuators

Mechatronics ◽  
2021 ◽  
Vol 78 ◽  
pp. 102610
Author(s):  
Jinsong Zhao ◽  
Tao Yang ◽  
Zhilei Ma ◽  
Chifu Yang ◽  
Zhipeng Wang ◽  
...  
Keyword(s):  
Sliding Mode Control ◽  
Lower Limb ◽  
Sliding Mode ◽  
Lower Limb Exoskeleton ◽  
Mode Control ◽  
Exoskeleton Robot ◽  
Modal Space

Trajectory following control of lower limb exoskeleton robot based on Udwadia–Kalaba theory

2021 ◽  
pp. 107754632110317
Author(s):  
Jin Tian ◽  
Liang Yuan ◽  
Wendong Xiao ◽  
Teng Ran ◽  
Li He
Keyword(s):  
Lower Limb ◽  
Control Method ◽  
Uniform Boundedness ◽  
Adaptive Robust Control ◽  
Structural Vibration ◽  
Control Approach ◽  
Lower Limb Exoskeleton ◽  
Exoskeleton Robot ◽  
Constraint Problem ◽  
Trajectory Following

The main objective of this article is to solve the trajectory following problem for lower limb exoskeleton robot by using a novel adaptive robust control method. The uncertainties are considered in lower limb exoskeleton robot system which include initial condition offset, joint resistance, structural vibration, and environmental interferences. They are time-varying and have unknown boundaries. We express the trajectory following problem as a servo constraint problem. In contrast to conventional control methods, Udwadia–Kalaba theory does not make any linearization or approximations. Udwadia–Kalaba theory is adopted to derive the closed-form constrained equation of motion and design the proposed control. We also put forward an adaptive law as a performance index whose type is leakage. The proposed control approach ensures the uniform boundedness and uniform ultimate boundedness of the lower limb exoskeleton robot which are demonstrated via the Lyapunov method. Finally, simulation results have shown the tracking effect of the approach presented in this article.

Adaptive Learning Inertia Control of Lower Limb Exoskeleton Robot.

2021 ◽  
Author(s):  
Muhammad Arsalan ◽  
Muhammad Tufail ◽  
SG Khan ◽  
Syed Humayoon Shah
Keyword(s):  
Adaptive Learning ◽  
Lower Limb ◽  
Lower Limb Exoskeleton ◽  
Exoskeleton Robot ◽  
Inertia Control
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