scholarly journals Explosive strength: effect of knee-joint angle on functional, neural, and intrinsic contractile properties

2019 ◽  
Vol 119 (8) ◽  
pp. 1735-1746 ◽  
Author(s):  
Marcel Bahia Lanza ◽  
T. G. Balshaw ◽  
J. P. Folland
Keyword(s):  
Knee Joint ◽  
Joint Angle ◽  
Contractile Properties ◽  
Explosive Strength ◽  
Knee Joint Angle ◽  
Strength Effect

scholarly journals Continuous Estimation of Knee Joint Angle Based on Surface Electromyography Using a Long Short-Term Memory Neural Network and Time-Advanced Feature

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4966
Author(s):  
Xunju Ma ◽  
Yali Liu ◽  
Qiuzhi Song ◽  
Can Wang
Keyword(s):  
Neural Network ◽  
Knee Joint ◽  
Surface Electromyography ◽  
Joint Angle ◽  
Short Term Memory ◽  
Mean Square ◽  
Short Term ◽  
Term Memory ◽  
Knee Joint Angle ◽  
Long Short Term Memory

Continuous joint angle estimation based on a surface electromyography (sEMG) signal can be used to improve the man-machine coordination performance of the exoskeleton. In this study, we proposed a time-advanced feature and utilized long short-term memory (LSTM) with a root mean square (RMS) feature and its time-advanced feature (RMSTAF; collectively referred to as RRTAF) of sEMG to estimate the knee joint angle. To evaluate the effect of joint angle estimation, we used root mean square error (RMSE) and cross-correlation coefficient ρ between the estimated angle and actual angle. We also compared three methods (i.e., LSTM using RMS, BPNN (back propagation neural network) using RRTAF, and BPNN using RMS) with LSTM using RRTAF to highlight its good performance. Five healthy subjects participated in the experiment and their eight muscle (i.e., rectus femoris (RF), biceps femoris (BF), semitendinosus (ST), gracilis (GC), semimembranosus (SM), sartorius (SR), medial gastrocnemius (MG), and tibialis anterior (TA)) sEMG signals were taken as algorithm inputs. Moreover, the knee joint angles were used as target values. The experimental results showed that, compared with LSTM using RMS, BPNN using RRTAF, and BPNN using RMS, the average RMSE values of LSTM using RRTAF were respectively reduced by 8.57%, 46.62%, and 68.69%, whereas the average ρ values were respectively increased by 0.31%, 4.15%, and 18.35%. The results demonstrated that LSTM using RRTAF, which contained the time-advanced feature, had better performance for estimating the knee joint motion.

scholarly journals Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor

2019 ◽  
Vol 6 ◽  
pp. 205566831986854 ◽  
Author(s):  
Rob Argent ◽  
Sean Drummond ◽  
Alexandria Remus ◽  
Martin O’Reilly ◽  
Brian Caulfield
Keyword(s):  
Machine Learning ◽  
Knee Joint ◽  
Inertial Measurement Unit ◽  
Joint Angle ◽  
Machine Learning Algorithms ◽  
Measurement Unit ◽  
Reference Standard ◽  
Inertial Measurement ◽  
Knee Joint Angle ◽  
Single Sensor

Introduction Joint angle measurement is an important objective marker in rehabilitation. Inertial measurement units may provide an accurate and reliable method of joint angle assessment. The objective of this study was to assess whether a single sensor with the application of machine learning algorithms could accurately measure hip and knee joint angle, and investigate the effect of inertial measurement unit orientation algorithms and person-specific variables on accuracy. Methods Fourteen healthy participants completed eight rehabilitation exercises with kinematic data captured by a 3D motion capture system, used as the reference standard, and a wearable inertial measurement unit. Joint angle was calculated from the single inertial measurement unit using four machine learning models, and was compared to the reference standard to evaluate accuracy. Results Average root-mean-squared error for the best performing algorithms across all exercises was 4.81° (SD = 1.89). The use of an inertial measurement unit orientation algorithm as a pre-processing step improved accuracy; however, the addition of person-specific variables increased error with average RMSE 4.99° (SD = 1.83°). Conclusions Hip and knee joint angle can be measured with a good degree of accuracy from a single inertial measurement unit using machine learning. This offers the ability to monitor and record dynamic joint angle with a single sensor outside of the clinic.

BIOMECHANICAL ANALYSIS OF THE STANDING LONG JUMP

2003 ◽  
Vol 15 (05) ◽  
pp. 186-192 ◽  
Author(s):  
WEN-LAN WU ◽  
JIA-HROUNG WU ◽  
HWAI-TING LIN ◽  
GWO-JAW WANG
Keyword(s):  
Knee Joint ◽  
Motion Analysis ◽  
Joint Angle ◽  
Three Dimensional ◽  
The Body ◽  
Initial Angle ◽  
Knee Joint Angle ◽  
Standing Long Jump ◽  
Long Jump ◽  
Arm Motion

The purposes of the present study were to (1) investigate the effects of the arm movement and initial knee joint angle employed in standing long jump by the ground reaction force analysis and three-dimensional motion analysis; and (2) investigate how the jump performance of the female gender related to the body configuration. Thirty-four healthy adult females performed standing long jump on a force platform with full effort. Body segment and joint angles were analyzed by three-dimensional motion analysis system. Using kinetic and kinematic data, the trajectories on mass center of body, knee joint angle, magnitude of peak takeoff force, and impulse generation in preparing phase were calculated. Average standing long jump performances with free arm motion were +1.5 times above performance with restricted arm motion in both knee initial angles. The performances with knee 90° initial flexion were +1.2 times above performance with knee 45° initial flexion in free and restricted arm motions. Judging by trajectories of the center mass of body (COM), free arm motion improves jump distance by anterior displacement of the COM in starting position. The takeoff velocity with 90° knee initial angle was as much as 11% higher than in with 45° knee initial angle. However, the takeoff angles on the COM trajectory showed no significant differences between each other. It was found that starting jump from 90° bend knee relatively extended the time that the force is applied by the leg muscles. To compare the body configurations and the jumping scores, there were no significant correlations between jump scores and anthropometry data. The greater muscle mass or longer leg did not correlated well with the superior jumping performance.

scholarly journals Human muscle length-dependent changes in blood flow

2012 ◽  
Vol 112 (4) ◽  
pp. 560-565 ◽  
Author(s):  
John McDaniel ◽  
Stephen J. Ives ◽  
Russell S. Richardson
Keyword(s):  
Blood Flow ◽  
Knee Joint ◽  
Joint Angle ◽  
Muscle Blood Flow ◽  
Muscle Length ◽  
Limb Blood Flow ◽  
Knee Joint Angle ◽  
Femoral Blood Flow ◽  
Femoral Blood ◽  
Cyclic Changes

Although a multitude of factors that influence skeletal muscle blood flow have been extensively investigated, the influence of muscle length on limb blood flow has received little attention. Thus the purpose of this investigation was to determine if cyclic changes in muscle length influence resting blood flow. Nine healthy men (28 ± 4 yr of age) underwent a passive knee extension protocol during which the subjects' knee joint was passively extended and flexed through 100–180° knee joint angle at a rate of 1 cycle per 30 s. Femoral blood flow, cardiac output (CO), heart rate (HR), stroke volume (SV), and mean arterial pressure (MAP) were continuously recorded during the entire protocol. These measurements revealed that slow passive changes in knee joint angle did not have a significant influence on HR, SV, MAP, or CO; however, net femoral blood flow demonstrated a curvilinear increase with knee joint angle ( r2 = 0.98) such that blood flow increased by ∼90% (125 ml/min) across the 80° range of motion. This net change in blood flow was due to a constant antegrade blood flow across knee joint angle and negative relationship between retrograde blood flow and knee joint angle ( r2 = 0.98). Thus, despite the absence of central hemodynamic changes and local metabolic factors, blood flow to the leg was altered by changes in muscle length. Therefore, when designing research protocols, researchers need to be cognizant of the fact that joint angle, and ultimately muscle length, influence limb blood flow.

Design and Test of a Biomechanical Model for the Estimation of Knee Joint Angle During Indoor Rowing: Implications for FES-Rowing Protocols in Paraplegia

2018 ◽  
Vol 26 (11) ◽  
pp. 2145-2152 ◽  
Author(s):  
Taian Vieira ◽  
Giacinto Luigi Cerone ◽  
Laura Gastaldi ◽  
Stefano Pastorelli ◽  
Liliam F. Oliveira ◽  
...  
Keyword(s):  
Knee Joint ◽  
Joint Angle ◽  
Biomechanical Model ◽  
Knee Joint Angle

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.

Sign in / Sign up

Export Citation Format

Share Document