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In this study, a gait phase classification method based on SVM multiclassclassification is introduced, with a focus on the precise identification of thestance and swing phases, which are further subdivided into seven phases. Datafrom individual IMU sensors, such as Shank Acceleration X, Y, Z, Shank Gyro X,and Knee Angles, are used as features in this classification model. Thesuggested technique successfully classifies the various gait phases with asignificant accuracy of about 90.3%. Gait phase classification is crucial,especially in the domains of exoskeletons and prosthetics, where accurateidentification of gait phases enables seamless integration with assistiveequipment, improving mobility, stability, and energy economy. This studyextends the study of gait and offers an effective method for correctlyidentifying gait phases from Shank IMU sensor data, with potential applicationsin biomechanical research, exoskeletons, rehabilitation, and prosthetics.

SVM based Multiclass Classifier for Gait phase Classification using Shank IMU Sensor