scholarly journals Prediction of Limb Joint Angles Based on Multi-Source Signals by GS-GRNN for Exoskeleton Wearer

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1104
Author(s):  
Hualong Xie ◽  
Guanchao Li ◽  
Xiaofei Zhao ◽  
Fei Li
Keyword(s):  
Neural Network ◽  
Lower Limb ◽  
Prediction Accuracy ◽  
Joint Angle ◽  
Golden Section ◽  
Human Movement ◽  
Lower Limbs ◽  
General Regression Neural Network ◽  
Joint Angles ◽  
Prediction Result

To enable exoskeleton wearers to walk on level ground, estimation of lower limb movement is particularly indispensable. In fact, it allows the exoskeleton to follow the human movement in real time. In this paper, the general regression neural network optimized by golden section algorithm (GS-GRNN) is used to realize prediction of the human lower limb joint angle. The human body hip joint angle and the surface electromyographic (sEMG) signals of the thigh muscles are taken as the inputs of a neural network to predict joint angles of lower limbs. To improve the prediction accuracy in different gait phases, the plantar pressure signals are also added into the input. After that, the error between the prediction result and the actual data decreases significantly. Finally, compared with the prediction result of the BP neural network, GRNN shows splendid prediction performance for its less processing time and higher prediction accuracy.

scholarly journals Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7199
Author(s):  
Jianyu Yang ◽  
Guanchao Li ◽  
Xiaofei Zhao ◽  
Hualong Xie
Keyword(s):  
Neural Network ◽  
Ankle Joint ◽  
Lower Limb ◽  
Plantar Pressure ◽  
Joint Angle ◽  
Golden Section ◽  
Prediction Method ◽  
Maximum Error ◽  
Generalized Regression Neural Network ◽  
Joint Angles

In the current study, our research group proposed an asymmetric lower extremity exoskeleton to enable above-knee amputees to walk with a load. Due to the absence of shank and foot, the knee and ankle joint at the amputation side of the exoskeleton lack tracking targets, so it is difficult to realize the function of assisted walking when going up and downstairs. Currently, the use of lower-limb electromyography to predict the angles of lower limb joints has achieved remarkable results. However, the prediction effect was poor when only using electromyography from the thigh. Therefore, this paper introduces hip-angle and plantar pressure signals for improving prediction effect and puts forward a joint prediction method of knee- and ankle-joint angles by electromyography of the thigh, hip-joint angle, and plantar pressure signals. The generalized regression neural network optimized by the golden section method is used to predict the joint angles. Finally, the parameters (the maximum error, the Root-Mean-Square error (RMSE), and correlation coefficient (γ)) were calculated to verify the feasibility of the prediction method.

scholarly journals Estimation of Continuous Joint Angles of Upper Limb Based on sEMG by Using GA-Elman Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Junhong Wang ◽  
Qiqi Hao ◽  
Xugang Xi ◽  
Jiuwen Cao ◽  
Anke Xue ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Elbow Joint ◽  
Joint Angle ◽  
Wavelet Packet ◽  
Upper Limbs ◽  
Joint Angles ◽  
Semg Signal ◽  
Elman Neural Network ◽  
Shoulder Movement

The estimation of continuous and simultaneous multijoint angle based on surface electromyography (sEMG) signal is of considerable significance in rehabilitation practice. However, there are few studies on the continuous joint angle of multiple joints at present. In this paper, the wavelet packet energy entropy (WPEE) of the special subspace was investigated as a feature of the sEMG signal. An Elman neural network optimized by genetic algorithm (GA) was established to estimate the joint angle of shoulder and elbow. First, the accuracy of the method is verified by estimating the angle of the shoulder joint. Then, this method was used to simultaneously and continuously estimate the shoulder and elbow joint angle. Six subjects flexed and extended the upper limbs according to the intended movements of the experiment. The results show that this method can obtain a decent performance with a RMSE of 3.4717 and R2 of 0.8283 in shoulder movement and with a RMSE of 4.1582 and R2 of 0.8114 in continuous synchronous movement of the shoulder and elbow.

Joint trajectory generator for powered orthosis based on gait modelling using PCA and FFT

Robotica ◽  
2017 ◽  
Vol 36 (3) ◽  
pp. 395-407 ◽  
Author(s):  
Nicholas B. Melo ◽  
Carlos E. T. Dórea ◽  
Pablo J. Alsina ◽  
Márcio V. Araújo
Keyword(s):  
Principal Component Analysis ◽  
Fourier Series ◽  
Energy Consumption ◽  
Lower Limb ◽  
Joint Angle ◽  
Principal Component ◽  
Orthotic Devices ◽  
Joint Angles ◽  
Anthropometric Features ◽  
Trajectory Generator

SUMMARYIn this work, we propose a method able to find user-oriented gait trajectories that can be used in powered lower limb orthosis applications. Most research related to active orthotic devices focuses on solving hardware issues. However, the problem of generating a set of joint trajectories that are user-oriented still persists. The proposed method uses principal component analysis to extract shared features from a gait dataset, taking into consideration gait-related variables such as joint angle information and the user's anthropometric features, used directly in an orthosis application. The trajectories of joint angles used by the model are represented by a given number of harmonics according to their respective Fourier series analyses. This representation allows better performance of the model, whose capability to generate gait information is validated through experiments using a real active orthotic device, analysing both joint motor energy consumption and user metabolic effort.

scholarly journals Error-state Kalman filter for lower-limb kinematic estimation: Evaluation on a 3-body model

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249577
Author(s):  
Michael V. Potter ◽  
Stephen M. Cain ◽  
Lauro V. Ojeda ◽  
Reed D. Gurchiek ◽  
Ryan S. McGinnis ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Lower Limb ◽  
Injury Risk ◽  
Human Subjects ◽  
Human Movement ◽  
Lower Limbs ◽  
Direct Evaluation ◽  
Body Model ◽  
Optical Motion ◽  
Error State

Human lower-limb kinematic measurements are critical for many applications including gait analysis, enhancing athletic performance, reducing or monitoring injury risk, augmenting warfighter performance, and monitoring elderly fall risk, among others. We present a new method to estimate lower-limb kinematics using an error-state Kalman filter that utilizes an array of body-worn inertial measurement units (IMUs) and four kinematic constraints. We evaluate the method on a simplified 3-body model of the lower limbs (pelvis and two legs) during walking using data from simulation and experiment. Evaluation on this 3-body model permits direct evaluation of the ErKF method without several confounding error sources from human subjects (e.g., soft tissue artefacts and determination of anatomical frames). RMS differences for the three estimated hip joint angles all remain below 0.2 degrees compared to simulation and 1.4 degrees compared to experimental optical motion capture (MOCAP). RMS differences for stride length and step width remain within 1% and 4%, respectively compared to simulation and 7% and 5%, respectively compared to experiment (MOCAP). The results are particularly important because they foretell future success in advancing this approach to more complex models for human movement. In particular, our future work aims to extend this approach to a 7-body model of the human lower limbs composed of the pelvis, thighs, shanks, and feet.

scholarly journals Joint Angle, Range of Motion, Force, and Moment Assessment: Responses of the Lower Limb to Ankle Plantarflexion and Dorsiflexion

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ukadike Chris Ugbolue ◽  
Chloe Robson ◽  
Emma Donald ◽  
Kerry L. Speirs ◽  
Frédéric Dutheil ◽  
...  
Keyword(s):  
Range Of Motion ◽  
Lower Limb ◽  
Joint Angle ◽  
Joint Angles ◽  
Hip Joints ◽  
Exercise Interventions ◽  
Biomechanical Assessment ◽  
Lower Limb Exercise ◽  
Males And Females ◽  
Foot Position

There is limited research on the biomechanical assessment of the lower limb joints in relation to dynamic movements that occur at the hip, knee, and ankle joints when performing dorsiflexion (DF) and plantarflexion (PF) among males and females. This study investigated the differences in joint angles (including range of motion (ROM)) and forces (including moments) between the left and right limbs at the ankle, knee, and hip joints during dynamic DF and PF movements in both males and females. Using a general linear model employing multivariate analysis in relation to the joint angle, ROM, force, and moment datasets, the results revealed significant main effects for gender, sidedness, phases, and foot position with respect to joint angles. Weak correlations were observed between measured biomechanical variables. These results provide insightful information for clinicians and biomechanists that relate to lower limb exercise interventions and modelling efficacy standpoints.

Sensitivity of the Toe Height to Multijoint Angular Changes in the Lower Limbs During Unobstructed and Obstructed Gait

2020 ◽  
pp. 1-9
Author(s):  
Chuyi Cui ◽  
Brittney Muir ◽  
Shirley Rietdyk ◽  
Jeffrey Haddad ◽  
Richard van Emmerik ◽  
...  
Keyword(s):  
Young Adults ◽  
Lower Limb ◽  
Sagittal Plane ◽  
Frontal Plane ◽  
Singular Value ◽  
Lower Limbs ◽  
Joint Angles ◽  
Adult Lifespan ◽  
Main Contributor ◽  
Bilateral Lower Limb

Tripping while walking is a main contributor to falls across the adult lifespan. Trip risk is proportional to variability in toe clearance. To determine the sources of this variability, the authors computed for 10 young adults the sensitivity of toe clearance to 10 bilateral lower limb joint angles during unobstructed and obstructed walking when the lead and the trail limb crossed the obstacle. The authors computed a novel measure—singular value of the appropriate Jacobian—as the combined toe clearance sensitivity to 4 groups of angles: all sagittal and all frontal plane angles and all swing and all stance limb angles. Toe clearance was most sensitive to the stance hip ab/adduction for unobstructed gait. For obstructed gait, sensitivity to other joints increased and matched the sensitivity to stance hip ab/adduction. Combined sensitivities revealed critical information that was not evident in the sensitivities to individual angles. The combined sensitivity to stance limb angles was 84% higher than swing limb angles. The combined sensitivity to the sagittal plane angles was lower than the sensitivity to the frontal plane angles during unobstructed gait, and this relation was reversed during obstacle crossing. The results highlight the importance of the stance limb joints and indicate that frontal plane angles should not be ignored.

scholarly journals The control algorithm of the lower limb exoskeleton synchronous gait

2018 ◽  
Vol 161 ◽  
pp. 03010
Author(s):  
Vladimir Antipov ◽  
Alexey Postolny ◽  
Andrey Yatsun ◽  
Sergey Jatsun
Keyword(s):  
Lower Limb ◽  
Power Distribution ◽  
Control Algorithm ◽  
Center Of Mass ◽  
Human Movement ◽  
Lower Limbs ◽  
Centre Of Mass ◽  
Lower Limb Exoskeleton ◽  
Synchronous Motion

In this article a study of algorithms for human movement in the lower limbs exoskeleton is presented. Human-machine system is considered, the classification of the existing exoskeletons by type of power distribution, the features of stable motion of the mechanism are presented. The law of the necessary trajectory of the center of mass of the exoskeleton is shown in the sagittal and frontal planes to maintain stability. The synchronous motion scheme of the centre of mass and the foot is described.

scholarly journals A Mechanical Sensor Designed for Dynamic Joint Angle Measurement

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Congo Tak-Shing Ching ◽  
Su-Yu Liao ◽  
Teng-Yun Cheng ◽  
Chih-Hsiu Cheng ◽  
Tai-Ping Sun ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Linear Correlation ◽  
Lower Limb ◽  
Joint Angle ◽  
Intraclass Correlation ◽  
Reliability And Validity ◽  
Angle Measurement ◽  
Joint Angles ◽  
Rehabilitation Programs ◽  
Analysis System

Background. The measurement of the functional range of motion (FROM) of lower limb joints is an essential parameter for gait analysis especially in evaluating rehabilitation programs.Aim. To develop a simple, reliable, and affordable mechanical goniometer (MGR) for gait analysis, with six-degree freedom to dynamically assess lower limb joint angles.Design. Randomized control trials, in which a new MGR was developed for the measurements of FROM of lower limb joints.Setting. Reliability of the designed MGR was evaluated and validated by a motion analysis system (MAS).Population. Thirty healthy subjects participated in this study.Methods. Reliability and validity of the new MGR were tested by intraclass correlation coefficient (ICC), Bland-Altman plots, and linear correlation analysis.Results. The MGR has good inter- and intrarater reliability and validity withICC≥0.93(for both). Moreover, measurements made by MGR and MAS were comparable and repeatable with each other, as confirmed by Bland-Altman plots. Furthermore, a very high degree of linear correlation (R≥0.92for all joint angle measurements) was found between the lower limb joint angles measured by MGR and MAS.Conclusion. A simple, reliable, and affordable MGR has been designed and developed to aid clinical assessment and treatment evaluation of gait disorders.

scholarly journals EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3622
Author(s):  
Jordan Coker ◽  
Howard Chen ◽  
Mark C. Schall Schall ◽  
Sean Gallagher ◽  
Michael Zabala
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Knee Flexion ◽  
Prediction Accuracy ◽  
Joint Angles ◽  
The Future ◽  
Artificial Neural ◽  
Secondary Hypothesis ◽  
Artificial Neural Network Ann ◽  
Knee Muscle

Electromyography (EMG) is commonly used to measure electrical activity of the skeletal muscles. As exoskeleton technology advances, these signals may be used to predict human intent for control purposes. This study used an artificial neural network trained and tested with knee flexion angles and knee muscle EMG signals to predict knee flexion angles during gait at 50, 100, 150, and 200 ms into the future. The hypothesis of this study was that the algorithm’s prediction accuracy would only be affected by time into the future, not subject, gender or side, and that as time into the future increased, the prediction accuracy would decrease. A secondary hypothesis was that as the number of algorithm training trials increased, the prediction accuracy of the artificial neural network (ANN) would increase. The results of this study indicate that only time into the future affected the accuracy of knee flexion angle prediction (p < 0.001), whereby greater time resulted in reduced accuracy (0.68 to 4.62 degrees root mean square error (RMSE) from 50 to 200 ms). Additionally, increased number of training trials resulted in increased angle prediction accuracy.

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