SVM based Multiclass Classifier for Gait phase Classification using
  Shank IMU Sensor
Aswadh Khumar G S Barath Kumar JK
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.