Comparison of gait phase detection using traditional machine learning and deep learning techniques
This work addresses the problem of real-time gait phase detection for controlling lower-limb assistive devices like exoskeletons and prostheses, but is incremental as it compares existing methods on a specific dataset.
This study compared traditional machine learning and deep learning techniques for detecting gait phases from lower-limb EMG data, finding that deep convolutional neural networks achieved up to 89.5% accuracy, outperforming traditional models which reached up to 75% accuracy.
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%.