The estimation of Knee Joint angle based on Generalized Regression Neural Network (GRNN)

2015 ◽  
Author(s):  
Tanvir Anwar ◽  
Yee Mon Aung ◽  
Adel Al Jumaily
Keyword(s):  
Neural Network ◽  
Knee Joint ◽  
Joint Angle ◽  
Generalized Regression Neural Network ◽  
Knee Joint Angle ◽  
Generalized Regression

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 Estimation of Knee Joint Angle Using Textile Capacitive Sensor and Artificial Neural Network Implementing with Three Shoe Types at Two Gait Speeds: A Preliminary Investigation

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5484
Author(s):  
Vantha Chhoeum ◽  
Young Kim ◽  
Se-Dong Min
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Knee Joint ◽  
Plantar Pressure ◽  
Joint Angle ◽  
Capacitive Sensor ◽  
Structure Design ◽  
Knee Joint Angle ◽  
Ann Models ◽  
Artificial Neural

The lower limb joints might be affected by different shoe types and gait speeds. Monitoring joint angles might require skill and proper technique to obtain accurate data for analysis. We aimed to estimate the knee joint angle using a textile capacitive sensor and artificial neural network (ANN) implementing with three shoe types at two gait speeds. We developed a textile capacitive sensor with a simple structure design and less costly placing in insole shoes to measure the foot plantar pressure for building the deep learning models. The smartphone was used to video during walking at each condition, and Kinovea was applied to calibrate the knee joint angle. Six ANN models were created; three shoe-based ANN models, two speed-based ANN models, and one ANN model that used datasets from all experiment conditions to build a model. All ANN models at comfortable and fast gait provided a high correlation efficiency (0.75 to 0.97) with a mean relative error lower than 15% implement for three testing shoes. And compare the ANN with A convolution neural network contributes a similar result in predict the knee joint angle. A textile capacitive sensor is reliable for measuring foot plantar pressure, which could be used with the ANN algorithm to predict the knee joint angle even using high heel shoes.

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