Abstract: This research presents a Human Lower Limb Activity Recognition (HLLAR) system that identifies specific activities andpredicts the angles of the knees simultaneously, based on the EMG signals. The HLLAR systems streamlines the researchon 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 NeuralNetwork (GRNN) as feature extraction, activity recognition, and knee angle prediction respectively. Electromyographysignal-based automatic lower limb activity detection is crucial to rehabilitation and human movement analysis. Yet severalof 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 electromyography signals using HILLAR model. The model is validated on two datasets, comprising 26 subjects performing threeclasses 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 generalizedregression neural network predicted angles of the knee joint with a root mean squared error of 1.25%. Robustness isdemonstrated 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 errorrates by 18% and decreasing processing time to 0.98 s.

Key words: Electromyography, Lower limb motion recognition, Knee joint angle prediction, Convolutional neuralnetwork, Wavelet transform')">Electromyography, Lower limb motion recognition, Knee joint angle prediction, Convolutional neuralnetwork, Wavelet transform