  Human walking is a complex activity with a high level of cooperation and
interaction between different systems in the body. Accurate detection of the
phases of the gait in real-time is crucial to control lower-limb assistive
devices like exoskeletons and prostheses. There are several ways to detect the
walking gait phase, ranging from cameras and depth sensors to the sensors
attached to the device itself or the human body. Electromyography (EMG) is one
of the input methods that has captured lots of attention due to its precision
and time delay between neuromuscular activity and muscle movement. This study
proposes a few Machine Learning (ML) based models on lower-limb EMG data for
human walking. The proposed models are based on Gaussian Naive Bayes (NB),
Decision Tree (DT), Random Forest (RF), Linear Discriminant Analysis (LDA) and
Deep Convolutional Neural Networks (DCNN). The traditional ML models are
trained on hand-crafted features or their reduced components using Principal
Component Analysis (PCA). On the contrary, the DCNN model utilises
convolutional layers to extract features from raw data. The results show up to
75% average accuracy for traditional ML models and 79% for Deep Learning (DL)
model. The highest achieved accuracy in 50 trials of the training DL model is
89.5%.
