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%.

人体行走是一种复杂的活动，精确检测步态阶段对于控制下肢辅助装置如外骨骼和假肢至关重要。本研究提出了几种基于下肢肌电数据的机器学习模型，其中包括高斯朴素贝叶斯、决策树、随机森林、线性判别分析以及深度卷积神经网络。传统机器学习模型通过手工特征或使用主成分分析对其降维处理，而深度卷积神经网络模型则利用卷积层从原始数据中提取特征。结果显示传统机器学习模型的平均准确率为75%，深度学习模型的准确率为79%。在训练的深度学习模型中，经过50次试验，最高准确率达到89.5%。

传统机器学习与深度学习技术在步态相位检测中的比较