In this study, a gait phase classification method based on SVM multiclass classification is introduced, with a focus on the precise identification of the stance and swing phases, which are further subdivided into seven phases. Data from individual IMU sensors, such as Shank Acceleration X, Y, Z, Shank Gyro X, and Knee Angles, are used as features in this classification model. The suggested technique successfully classifies the various gait phases with a significant accuracy of about 90.3%. Gait phase classification is crucial, especially in the domains of exoskeletons and prosthetics, where accurate identification of gait phases enables seamless integration with assistive equipment, improving mobility, stability, and energy economy. This study extends the study of gait and offers an effective method for correctly identifying gait phases from Shank IMU sensor data, with potential applications in biomechanical research, exoskeletons, rehabilitation, and prosthetics.

翻译：本研究提出一种基于SVM多分类的步态相位分类方法，重点聚焦于支撑相和摆动相的精确识别，并将其进一步细分为七个相位。该分类模型采用单个IMU传感器的数据作为特征，包括胫部加速度X、Y、Z分量、胫部陀螺仪数据及膝关节角度。所提出的技术成功实现了不同步态相位的分类，准确率显著达到约90.3%。步态相位分类至关重要，尤其在假肢和外骨骼领域，精确的步态相位识别能够实现与辅助设备的无缝集成，从而改善运动能力、稳定性和能量效率。本研究拓展了步态分析领域，提供了一种从胫部IMU传感器数据中准确识别步态相位的有效方法，在生物力学研究、外骨骼、康复医学及假肢技术中具有潜在应用价值。