scholarly journals Estimation of Knee Joint Extension Force Using Mechanomyography Based on IGWO-SVR Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2972
Author(s):  
Zebin Li ◽  
Lifu Gao ◽  
Wei Lu ◽  
Daqing Wang ◽  
Chenlei Xie ◽  
...  
Keyword(s):  
Knee Joint ◽  
Lower Limb ◽  
Muscle Force ◽  
Muscle Disease ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Support Vector ◽  
Gray Wolf ◽  
Acceleration Sensors ◽  
Extension Force

Muscle force is an important physiological parameter of the human body. Accurate estimation of the muscle force can improve the stability and flexibility of lower limb joint auxiliary equipment. Nevertheless, the existing force estimation methods can neither satisfy the accuracy requirement nor ensure the validity of estimation results. It is a very challenging task that needs to be solved. Among many optimization algorithms, gray wolf optimization (GWO) is widely used to find the optimal parameters of the regression model because of its superior optimization ability. Due to the traditional GWO being prone to fall into local optimum, a new nonlinear convergence factor and a new position update strategy are employed to balance local and global search capability. In this paper, an improved gray wolf optimization (IGWO) algorithm to optimize the support vector regression (SVR) is developed to estimate knee joint extension force accurately and timely. Firstly, mechanomyography (MMG) of the lower limb is measured by acceleration sensors during leg isometric muscle contractions extension training. Secondly, root mean square (RMS), mean absolute value (MAV), zero crossing (ZC), mean power frequency (MPF), and sample entropy (SE) of the MMG are extracted to construct feature sets as candidate data sets for regression analysis. Lastly, the features are fed into IGWO-SVR for further training. Experiments demonstrate that the IGWO-SVR provides the best performance indexes in the estimation of knee joint extension force in terms of RMSE, MAPE, and R compared with the other state-of-art models. These results are expected to become the most effective as guidance for rehabilitation training, muscle disease diagnosis, and health evaluation.

scholarly journals Bicycling Phase Recognition for Lower Limb Amputees Using Support Vector Machine Optimized by Particle Swarm Optimization

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6533
Author(s):  
Xinxin Li ◽  
Zuojun Liu ◽  
Xinzhi Gao ◽  
Jie Zhang
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Knee Joint ◽  
Lower Limb ◽  
Joint Angle ◽  
Support Vector ◽  
Swarm Optimization ◽  
Knee Joint Angle ◽  
Phase Recognition ◽  
Lower Limb Amputees

A novel method for recognizing the phases in bicycling of lower limb amputees using support vector machine (SVM) optimized by particle swarm optimization (PSO) is proposed in this paper. The method is essential for enhanced prosthetic knee joint control for lower limb amputees in carrying out bicycling activity. Some wireless wearable accelerometers and a knee joint angle sensor are installed in the prosthesis to obtain data on the knee joint and ankle joint horizontal, vertical acceleration signal and knee joint angle. In order to overcome the problem of high noise content in the collected data, a soft-hard threshold filter was used to remove the noise caused by the vibration. The filtered information is then used to extract the multi-dimensional feature vector for the training of SVM for performing bicycling phase recognition. The SVM is optimized by PSO to enhance its classification accuracy. The recognition accuracy of the PSO-SVM classification model on testing data is 93%, which is much higher than those of BP, SVM and PSO-BP classification models.

scholarly journals Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques

2022 ◽  
Author(s):  
Chiako Mokri ◽  
Mahdi Bamdad ◽  
Vahid Abolghasemi
Keyword(s):  
Machine Learning ◽  
Lower Limb ◽  
Muscle Force ◽  
Machine Learning Techniques ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Support Vector ◽  
Rehabilitation Robot ◽  
Learning Techniques ◽  
Semg Signals

AbstractThe main objective of this work is to establish a framework for processing and evaluating the lower limb electromyography (EMG) signals ready to be fed to a rehabilitation robot. We design and build a knee rehabilitation robot that works with surface EMG (sEMG) signals. In our device, the muscle forces are estimated from sEMG signals using several machine learning techniques, i.e. support vector machine (SVM), support vector regression (SVR) and random forest (RF). In order to improve the estimation accuracy, we devise genetic algorithm (GA) for parameter optimisation and feature extraction within the proposed methods. At the same time, a load cell and a wearable inertial measurement unit (IMU) are mounted on the robot to measure the muscle force and knee joint angle, respectively. Various performance measures have been employed to assess the performance of the proposed system. Our extensive experiments and comparison with related works revealed a high estimation accuracy of 98.67% for lower limb muscles. The main advantage of the proposed techniques is high estimation accuracy leading to improved performance of the therapy while muscle models become especially sensitive to the tendon stiffness and the slack length.

scholarly journals Dependency of Lower Limb Joint Reaction Forces on Femoral Anteversion

2021 ◽  
Author(s):  
Luca Modenese ◽  
Martina Barzan ◽  
Christopher P Carty
Keyword(s):  
Body Weight ◽  
Knee Joint ◽  
Lower Limb ◽  
Femoral Anteversion ◽  
Accurate Estimation ◽  
Grand Challenge ◽  
Anteversion Angle ◽  
Joint Reaction Forces ◽  
Reaction Forces ◽  
Joint Reaction

AbstractBackgroundMusculoskeletal (MSK) models based on literature data are meant to represent a generic anatomy and are a popular tool employed by biomechanists to estimate the internal loads occurring in the lower limb joints, such as joint reaction forces (JRFs). However, since these models are normally just linearly scaled to an individual’s anthropometry, it is unclear how their estimations would be affected by the personalization of key features of the MSK anatomy, one of which is the femoral anteversion angle.Research QuestionHow are the lower limb JRF magnitudes computed through a generic MSK model affected by changes in the femoral anteversion?MethodsWe developed a bone-deformation tool in MATLAB (https://simtk.org/projects/bone_deformity) and used it to create a set of seven OpenSim models spanning from 2° femoral retroversion to 40° anteversion. We used these models to simulate the gait of an elderly individual with an instrumented prosthesis implanted at their knee joint (5th Grand Challenge dataset) and quantified both the changes in JRFs magnitude due to varying the skeletal anatomy and their accuracy against the correspondent in vivo measurements at the knee joint.ResultsHip and knee JRF magnitudes were affected by the femoral anteversion with variations from the unmodified generic model up to 11.7±5.5% at the hip and 42.6±31.0% at the knee joint. The ankle joint was unaffected by the femoral geometry. The MSK models providing the most accurate knee JRFs (root mean squared error: 0.370±0.069 body weight, coefficient of determination: 0.764±0.104, largest peak error: 0.36±0.16 body weight) were those with the femoral anteversion angle closer to that measured on the segmented bone of the individual.SignificanceFemoral anteversion substantially affects hip and knee JRFs estimated with generic MSK models, suggesting that personalizing key MSK anatomical features might be necessary for accurate estimation of JRFs with these models.

scholarly journals Angle Estimation for Knee Joint Movement Based on PCA-RELM Algorithm

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 130 ◽  
Author(s):  
Yanxia Deng ◽  
Farong Gao ◽  
Huihui Chen
Keyword(s):  
Knee Joint ◽  
Lower Limb ◽  
Joint Angle ◽  
Pearson Correlation ◽  
Lower Limbs ◽  
Human Gait ◽  
Support Vector ◽  
Joint Movement ◽  
Knee Joint Angle ◽  
Semg Signals

Surface electromyogram (sEMG) signals are easy to record and offer valuable motion information, such as symmetric and periodic motion in human gait. Due to these characteristics, sEMG is widely used in human-computer interaction, clinical diagnosis and rehabilitation medicine, sports medicine and other fields. This paper aims to improve the estimation accuracy and real-time performance, in the case of the knee joint angle in the lower limb, using a sEMG signal, in a proposed estimation algorithm of the continuous motion, based on the principal component analysis (PCA) and the regularized extreme learning machine (RELM). First, the sEMG signals, collected during the lower limb motion, are preprocessed, while feature samples are extracted from the acquired and preconditioned sEMG signals. Next, the feature samples dimensions are reduced by the PCA, as well as the knee joint angle system is measured by the three-dimensional motion capture system, are followed by the normalization of the feature variable value. The normalized sEMG feature is used as the input layer, in the RELM model, while the joint angle is used as the output layer. After training, the RELM model estimates the knee joint angle of the lower limbs, while it uses the root mean square error (RMSE), Pearson correlation coefficient and model training time as key performance indicators (KPIs), to be further discussed. The RELM, the traditional BP neural network and the support vector machine (SVM) estimation results are compared. The conclusions prove that the RELM method, not only has ensured the validity of results, but also has greatly reduced the learning train time. The presented work is a valuable point of reference for further study of the motion estimation in lower limb.

scholarly journals Energy absorption at lower limb joints in different foot contact strategies while descending stairs

2021 ◽  
Vol 29 ◽  
pp. 433-440
Author(s):  
Hyeong-Min Jeon ◽  
Ki-Kwang Lee ◽  
Jun-Young Lee ◽  
Ju-Hwan Shin ◽  
Gwang-Moon Eom
Keyword(s):  
Knee Joint ◽  
Potential Energy ◽  
Energy Absorption ◽  
Lower Limb ◽  
Mechanical Energy ◽  
Connective Tissues ◽  
Stair Descent ◽  
Young Subjects ◽  
Power And Energy ◽  
Joint Loads

BACKGROUND: Joint loads in different walking strategies during stair descent have been investigated in terms of the joint moment in association with the risk of osteoarthritis. However, the absorption mechanisms of the potential energy loss are not known. OBJECTIVE: This study aims to compare the mechanical energy absorptions in lower limb joints in different initial foot contact strategies. METHODS: Nineteen young subjects walked down on instrumented stairs with two different strategies, i.e., forefoot and rearfoot strike. Power and energy at lower limb joints during stance phase were compared between strategies. RESULTS: Lower limb joints absorbed 73 ± 11% of the potential energy released by descending stairs and there was no difference between strategies. Rearfoot strategy absorbed less energy than forefoot strategy at the ankle joint in the 1st phase, which was compensated mainly by more energy absorption at the knee in the 2nd phase and less energy generation at the hip joints in the 3rd phase. CONCLUSION: The results suggest that a leg absorbs most of the potential energy while descending stairs irrespective of the walking strategies and that any reduction of energy absorption at one joint is compensated by other joints. Greater energy absorption at the knee joint compared to the other joints suggests high burden of knee joint muscles and connective tissues during stair-descent, which is even more significant for the rearfoot strike strategy.

scholarly journals Dark spot detection for characterization of marine surface slicks using UAVSAR quad-pol data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Vaishali Chaudhary ◽  
Shashi Kumar
Keyword(s):  
Oil Spills ◽  
Marine Ecosystem ◽  
Dark Spot ◽  
Accurate Estimation ◽  
Support Vector ◽  
Potential Hazard ◽  
Significant Drop ◽  
Oil Slick ◽  
Spot Detection ◽  
Marine Surface

AbstractOil spills are a potential hazard, causing the deaths of millions of aquatic animals and this leaves a calamitous effect on the marine ecosystem. This research focuses on evaluating the potential of polarimetric parameters in discriminating the oil slick from water and also possible thicker/thinner zones within the slick. For this purpose, L-band UAVSAR quad-pol data of the Gulf of Mexico region is exploited. A total number of 19 polarimetric parameters are examined to study their behavior and ability in distinguishing oil slick from water and its own less or more oil accumulated zones. The simulation of compact-pol data from UAVSAR quad-pol data is carried out which has shown good performance in detection and discrimination of oil slick from water. To know the extent of separation between oil and water classes, a statistical separability analysis is carried out. The outcomes of each polarimetric parameter from separability analysis are then quantified with the radial basis function (RBF) supervised Support Vector Machine classifier followed with an accurate estimation of the results. Moreover, a comparison of the achieved and estimated accuracy has shown a significant drop in accuracy values. It has been observed that the highest accuracy is given by LHV compact-pol decomposition and coherency matrix with a classification accuracy of ~ 94.09% and ~ 94.60%, respectively. The proposed methodology has performed well in discriminating the oil slick by utilizing UAVSAR dataset for both quad-pol and compact-pol simulation.

scholarly journals Research on a novel data-driven aging estimation method for battery systems in real-world electric vehicles

2021 ◽  
Vol 13 (7) ◽  
pp. 168781402110277
Author(s):  
Yankai Hou ◽  
Zhaosheng Zhang ◽  
Peng Liu ◽  
Chunbao Song ◽  
Zhenpo Wang
Keyword(s):  
Electric Vehicles ◽  
Real World ◽  
Regression Models ◽  
Estimation Method ◽  
Recursive Least Squares ◽  
Data Driven ◽  
Accurate Estimation ◽  
Support Vector ◽  
Battery Degradation ◽  
Operational Data

Accurate estimation of the degree of battery aging is essential to ensure safe operation of electric vehicles. In this paper, using real-world vehicles and their operational data, a battery aging estimation method is proposed based on a dual-polarization equivalent circuit (DPEC) model and multiple data-driven models. The DPEC model and the forgetting factor recursive least-squares method are used to determine the battery system’s ohmic internal resistance, with outliers being filtered using boxplots. Furthermore, eight common data-driven models are used to describe the relationship between battery degradation and the factors influencing this degradation, and these models are analyzed and compared in terms of both estimation accuracy and computational requirements. The results show that the gradient descent tree regression, XGBoost regression, and light GBM regression models are more accurate than the other methods, with root mean square errors of less than 6.9 mΩ. The AdaBoost and random forest regression models are regarded as alternative groups because of their relative instability. The linear regression, support vector machine regression, and k-nearest neighbor regression models are not recommended because of poor accuracy or excessively high computational requirements. This work can serve as a reference for subsequent battery degradation studies based on real-time operational data.

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