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%.

翻译：人类行走是一项复杂的活动，需要身体不同系统之间高度协同与交互。实时准确检测步态相位对于控制外骨骼和假肢等下肢体辅助设备至关重要。步态相位的检测方法多种多样，包括使用摄像头、深度传感器以及设备本体或人体表面附着的传感器。肌电图（EMG）作为一种输入方法，因其在神经肌肉活动与肌肉运动之间的高精度和极短延迟而备受关注。本研究基于下肢肌电图数据提出了几种机器学习模型用于人类行走分析，具体包括高斯朴素贝叶斯（NB）、决策树（DT）、随机森林（RF）、线性判别分析（LDA）和深度卷积神经网络（DCNN）。传统机器学习模型基于手工提取特征或其经主成分分析（PCA）降维后的特征训练，而DCNN模型则利用卷积层从原始数据中自动提取特征。实验结果表明：传统机器学习模型的平均准确率最高可达75%，深度学习模型为79%，其中在50次训练试验中，深度学习模型取得的最高准确率为89.5%。