Human Motion Intention Recognition via sEMG and Joint Kinematics Fusion Using MPSO-SVM for Intelligent Transportation Systems

For intelligent transportation systems (ITS), understanding pedestrian motion intention is crucial for enhancing traffic safety, enabling human-centered mobility services, and facilitating adaptive vehicle-pedestrian interactions. This paper proposes a pedestrian gait recognition method based on a modified particle swarm optimization-support vector machine (MPSO-SVM), utilizing fused surface electromyography (sEMG) signals and ankle joint angles. Seven lower-limb gait features were extracted from these signals to characterize walking patterns. The MPSO algorithm optimizes the support vector machine (SVM) parameters to improve classification performance. Experimental results based on data collected from healthy subjects demonstrate a recognition accuracy exceeding 92.5% across four gait phases. The proposed method offers significantly enhanced accuracy and robustness compared to traditional classifiers. These results suggest that the method is suitable for deployment in intelligent traffic control systems, autonomous vehicle navigation, and urban pedestrian behavior prediction.