This research presents a Human Lower Limb Activity Recognition(HLLAR)system that identifies specific activities and predicts the angles of the knees simultaneously,based on the EMG signals.The HLLAR systems streamline...This research presents a Human Lower Limb Activity Recognition(HLLAR)system that identifies specific activities and predicts the angles of the knees simultaneously,based on the EMG signals.The HLLAR systems streamlines the research on the lower limb activities.The HILLAR model includes Discrete Hermite Wavelets Transform-based Synchrosqueezing(DHWTS),Deep Two-Layer Multiscale Convolutional Neural Network(DTLMCNN),and Generalized Regression Neural Network(GRNN)as feature extraction,activity recognition,and knee angle prediction respectively.Electromyography signal-based automatic lower limb activity detection is crucial to rehabilitation and human movement analysis.Yet several of these methods face issues in feature extraction in complex data,overlapping signals,extraction of crucial parameters,and adaptation constraints.This research aims classify lower limb activities and predict knee joint angles from electromy-ography signals using HILLAR model.The model is validated on two datasets,comprising 26 subjects performing three classes of activities:walking,standing,and sitting.The proposed model obtained a classification accuracy of 99.95%,along with significant achievements in precision(99.93%),recall(99.91%),and F1-score(99.93%).The generalized regression neural network predicted angles of the knee joint with a root mean squared error of 1.25%.Robustness is demonstrated through consistent results in five-fold cross-validation and statistical significance testing(p-value=0.004,McNemar's test).Additionally,the proposed model showed superior performance over baseline methods by reducing error rates by 18%and decreasing processing time to 0.98 s.展开更多
The purpose of this study was to establish a method for measuring the knee valgus angle from the ana- tomical and mechanical axes on three-dimensional reconstruction imaging models, and to use this method for estimati...The purpose of this study was to establish a method for measuring the knee valgus angle from the ana- tomical and mechanical axes on three-dimensional reconstruction imaging models, and to use this method for estimating an average knee valgus angle value for northern Chinese adults. Computed tomographic angiography data in DICOM format for 128 normal femurs from 64 adult subjects were chosen for analysis. After the femur images were subjected to three-dimensional reconstruction, the deepest point in the intercondylar notch （point A）, the midpoint of the medullary cavity 20 cm above the knee-joint line （point B）, and the landmark of the femoral head rotation center （point C） were identified on each three-dimensional model. The knee valgus angle was defined as the angle enclosed by the distal femoral anatomical axis （line AB） and the femoral mechanical axis （line AC）. The average （mean＋SD） of knee valgus angle for the 128 femurs was 6.20°±1.20° （range, 3.05° to 10.64°）. Significant positive correlations were found between the knee valgus angles of the right and left sides and between the knee valgus angle and age. During total knee arthroplasty, choosing a valgus cut angle of approximately 6° may achieve a good result in reestablishing the natural mechanical alignment of the lower extremity for patients of northern Chinese ethnicity. Larger valgus cut angles should be chosen for older patients.展开更多
文摘This research presents a Human Lower Limb Activity Recognition(HLLAR)system that identifies specific activities and predicts the angles of the knees simultaneously,based on the EMG signals.The HLLAR systems streamlines the research on the lower limb activities.The HILLAR model includes Discrete Hermite Wavelets Transform-based Synchrosqueezing(DHWTS),Deep Two-Layer Multiscale Convolutional Neural Network(DTLMCNN),and Generalized Regression Neural Network(GRNN)as feature extraction,activity recognition,and knee angle prediction respectively.Electromyography signal-based automatic lower limb activity detection is crucial to rehabilitation and human movement analysis.Yet several of these methods face issues in feature extraction in complex data,overlapping signals,extraction of crucial parameters,and adaptation constraints.This research aims classify lower limb activities and predict knee joint angles from electromy-ography signals using HILLAR model.The model is validated on two datasets,comprising 26 subjects performing three classes of activities:walking,standing,and sitting.The proposed model obtained a classification accuracy of 99.95%,along with significant achievements in precision(99.93%),recall(99.91%),and F1-score(99.93%).The generalized regression neural network predicted angles of the knee joint with a root mean squared error of 1.25%.Robustness is demonstrated through consistent results in five-fold cross-validation and statistical significance testing(p-value=0.004,McNemar's test).Additionally,the proposed model showed superior performance over baseline methods by reducing error rates by 18%and decreasing processing time to 0.98 s.
基金supported by the Norman Bethune B Program of Jilin University(No.2012216)the Science and Technology Development Program of Jilin Province(No.20100750),China
文摘The purpose of this study was to establish a method for measuring the knee valgus angle from the ana- tomical and mechanical axes on three-dimensional reconstruction imaging models, and to use this method for estimating an average knee valgus angle value for northern Chinese adults. Computed tomographic angiography data in DICOM format for 128 normal femurs from 64 adult subjects were chosen for analysis. After the femur images were subjected to three-dimensional reconstruction, the deepest point in the intercondylar notch （point A）, the midpoint of the medullary cavity 20 cm above the knee-joint line （point B）, and the landmark of the femoral head rotation center （point C） were identified on each three-dimensional model. The knee valgus angle was defined as the angle enclosed by the distal femoral anatomical axis （line AB） and the femoral mechanical axis （line AC）. The average （mean＋SD） of knee valgus angle for the 128 femurs was 6.20°±1.20° （range, 3.05° to 10.64°）. Significant positive correlations were found between the knee valgus angles of the right and left sides and between the knee valgus angle and age. During total knee arthroplasty, choosing a valgus cut angle of approximately 6° may achieve a good result in reestablishing the natural mechanical alignment of the lower extremity for patients of northern Chinese ethnicity. Larger valgus cut angles should be chosen for older patients.
