scholarly journals Lower Limb Motion Recognition Method Based on Improved Wavelet Packet Transform and Unscented Kalman Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Xin Shi ◽  
Pengjie Qin ◽  
Jiaqing Zhu ◽  
Shuyuan Xu ◽  
Weiren Shi
Keyword(s):  
Neural Network ◽  
Lower Extremity ◽  
Lower Limb ◽  
Wavelet Packet ◽  
Sliding Window ◽  
Recognition Algorithm ◽  
Lower Limbs ◽  
Exoskeleton Robot ◽  
Motion Intention ◽  
Semg Signals

Exoskeleton robot is a typical application to assist the motion of lower limbs. To make the lower extremity exoskeleton more flexible, it is necessary to identify various motion intentions of the lower limbs of the human body. Although more sEMG sensors can be used to identify more lower limb motion intention, with the increase in the number of sensors, more and more data need to be processed. In the process of human motion, the collected sEMG signal is easy to be interfered with noise. To improve the practicality of the lower extremity exoskeleton robot, this paper proposed a wavelet packet transform- (WPT-) based sliding window difference average filtering feature extract algorithm and the unscented Kalman neural network (UKFNN) recognition algorithm. We established an sEMG energy feature model, using a sliding window difference average filtering method to suppress noise interference and extracted stable feature values and using UKF filtering to optimize the neural network weights to improve the adaptability and accuracy of the recognition model. In this paper, we collected the sEMG signals of three muscles to identify six lower limb motion intentions. The average accuracy of 94.83% is proposed in this paper. Experiments show that the algorithm improves the accuracy and anti-interference of motion intention recognition of lower limb sEMG signals. The algorithm is superior to the backpropagation neural network (BPNN) recognition algorithm in the lower limb motion intention recognition and proves the effectiveness, novelty, and reliability of the method in this paper.

scholarly journals Development of human lower limbs exoskeleton robot for medical rehabilitation

2021 ◽  
pp. 91-97
Author(s):  
E. A. Kotov ◽  
◽  
A. D. Druk ◽  
D. N. Klypin ◽  
◽  
...  
Keyword(s):  
Control System ◽  
Experimental Research ◽  
Lower Limb ◽  
Lower Limbs ◽  
Medical Rehabilitation ◽  
Lower Limb Exoskeleton ◽  
Exoskeleton Robot ◽  
Controlled Motion ◽  
And Control

The article deals with the solution of the problem of optimizing the characteristics of controlled motion of human lower limb exoskeleton robot for improving medical rehabilitation. The aim of the work is to develop a rehabilitation device capable of providing controlled motion in two planes, as well as maintaining balance without loss of mobility. The design and control system of a rehabilitation trainer designed for performing mechanotherapy of the lower limbs of patients with locomotive disorders are proposed and characterized. The developed system has a number of significant differences from analogues and can be recommended for experimental research on patients with impaired locomotive functions

scholarly journals Isolated Arteritis of Both Lower Limbs

2018 ◽  
Vol 119 (1) ◽  
pp. 70-75
Author(s):  
Miroslav Průcha ◽  
Alena Šnajdrová ◽  
Pavel Zdráhal
Keyword(s):  
Autoimmune Disease ◽  
Lower Extremity ◽  
Lower Limb ◽  
Imaging Techniques ◽  
Rare Condition ◽  
Rapid Diagnosis ◽  
Vascular Imaging ◽  
Lower Limbs ◽  
Systemic Autoimmune Disease ◽  
Successful Therapy

Isolated arteritis of the lower limb vessels is an extremely rare condition. The use of modern vascular imaging techniques substantially facilitates and accelerates the diagnostics. In the isolated lower limb arteritis, it is always necessary to exclude Takayasu’s and giant-cell arteritis. We present the case of a female patient with an isolated lower extremity arteritis without any other symptoms of systemic vascular damage or systemic autoimmune disease. Immunosuppressive therapy is obligatory in this case. Interdisciplinary co-operation is required for rapid diagnosis and successful therapy. Our patient has consented to the publication of this report.

scholarly journals Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 672 ◽  
Author(s):  
Lin Chen ◽  
Jianting Fu ◽  
Yuheng Wu ◽  
Haochen Li ◽  
Bin Zheng
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Surface Electromyography ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Hand Gesture ◽  
Proposed Model ◽  
Electromyography Signals ◽  
Motion Intention ◽  
Semg Signals

By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of parameters. Therefore, we designed a compact Convolution Neural Network (CNN) model, which not only improves the classification accuracy but also reduces the number of parameters in the model. Our proposed model was validated on the Ninapro DB5 Dataset and the Myo Dataset. The classification accuracy of gesture recognition achieved good results.

scholarly journals Fast Wearable Sensor–Based Foot–Ground Contact Phase Classification Using a Convolutional Neural Network with Sliding-Window Label Overlapping

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4996 ◽  
Author(s):  
Haneul Jeon ◽  
Sang Lae Kim ◽  
Soyeon Kim ◽  
Donghun Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Lower Limb ◽  
Sliding Window ◽  
Swing Phase ◽  
Training Dataset ◽  
Test Accuracy ◽  
Ground Contact ◽  
Contact Phase ◽  
Walking Gait

Classification of foot–ground contact phases, as well as the swing phase is essential in biomechanics domains where lower-limb motion analysis is required; this analysis is used for lower-limb rehabilitation, walking gait analysis and improvement, and exoskeleton motion capture. In this study, sliding-window label overlapping of time-series wearable motion data in training dataset acquisition is proposed to accurately detect foot–ground contact phases, which are composed of 3 sub-phases as well as the swing phase, at a frequency of 100 Hz with a convolutional neural network (CNN) architecture. We not only succeeded in developing a real-time CNN model for learning and obtaining a test accuracy of 99.8% or higher, but also confirmed that its validation accuracy was close to 85%.

Push recovery control for the underactuated lower extremity exoskeleton based on improved capture point concept

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiaqi Zhang ◽  
Ming Cong ◽  
Dong Liu ◽  
Yu Du ◽  
Hongjiang Ma
Keyword(s):  
Lower Extremity ◽  
Ankle Joint ◽  
Lower Limb ◽  
Biped Robot ◽  
Lower Limbs ◽  
Simple Method ◽  
Content Type ◽  
Torsion Spring ◽  
Balance Ability ◽  
Capture Point

Purpose The purpose of this paper is to use a simple method to enhance the ability of lower limb exoskeletons to restore balance under large interference conditions and to solve the problem that biped robot stability criterion cannot be fully applied to the underactuated lower limb exoskeletons. Design/methodology/approach The method used in this paper is to construct an underactuated lower extremity exoskeleton ankle joint with a torsion spring. Based on the constructed exoskeleton, the linear inverted torsion spring pendulum model is proposed, and the traditional capture point (CP) concept is optimized. Findings The underactuated exoskeleton ankle joint with torsion springs, combined with the improved CP concept, can effectively reduce the forward stepping distance under the same interference condition, which is equivalent to enhancing the balance ability of the lower extremity exoskeleton. Originality/value The contribution of this paper is to enhance the balance ability of the exoskeleton of the lower limbs under large interference conditions. The torsion spring is used as the exoskeleton ankle joint, and the traditional CP concept is optimized according to the constructed exoskeleton.

Lower Limb Flexibility in Children With Duchenne Muscular Dystrophy: Effects on Functional Performance

2019 ◽  
Vol 31 (1) ◽  
pp. 42-46
Author(s):  
Lütfiye Akkurt ◽  
İpek Alemdaroğlu Gürbüz ◽  
Ayşe Karaduman ◽  
Öznur Tunca Yilmaz
Keyword(s):  
Duchenne Muscular Dystrophy ◽  
Muscular Dystrophy ◽  
Lower Extremity ◽  
Lower Limb ◽  
Functional Performance ◽  
Lower Limbs ◽  
Performance Tests ◽  
Gastrocnemius Muscles ◽  
Minute Walk ◽  
Walk Tests

Objective: To investigate the effects of lower limb flexibility on the functional performance of children with Duchenne muscular dystrophy. Methods: Thirty children, whose functional levels were at 1 or 2 according to the Brooke Lower Extremity Functional Classification Scale, were included in this study. The flexibilities of the hamstrings, hip flexors, tensor fascia latae, and gastrocnemius muscles were evaluated in the children’s dominant lower limbs. The children’s functional performance was assessed using 6-minute walk tests and timed performance tests. The correlations between the flexibilities of the lower limb muscles and the performance tests were examined. Results: The flexibilities of the lower extremity muscles were found to be correlated to the 6-minute walk tests and the timed performance tests. The flexibility of the hamstrings (r = −.825), the gastrocnemius muscles (r = .545), the hip flexors (r = .481), and the tensor fascia latae (r = .445) were found to be correlated with functional performance as measured by the 6-minute walk tests (P < .05). Discussion: The results of the current study indicate that the flexibility of the lower limbs has an effect on functional performance in the early stages of Duchenne muscular dystrophy. More research is needed to determine the functional effects of flexibility on performance by adding long-term flexibility exercises to the physiotherapy programs of children with Duchenne muscular dystrophy.

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.

Research on human gait prediction and recognition algorithm of lower limb-assisted exoskeleton robot

2021 ◽  
Author(s):  
Tao Qin ◽  
Yong Yang ◽  
Bin Wen ◽  
Zhengxiang Chen ◽  
Zhong Bao ◽  
...  
Keyword(s):  
Lower Limb ◽  
Recognition Algorithm ◽  
Human Gait ◽  
Exoskeleton Robot
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