Orthotic bracing are fitted to improve the quality of a deviating gait cycle, nevertheless recentrnstudies show that gait cycle deviations are seen due to bracing [4, 36]. In order to correct therndeviation, adjustments should be made in all stages of the process to which it requires evaluatingrnthe deviation. So the objective of this paper is to develop and analyze the performance of anrnalgorithm for gait classification, to assess the deviation of gait cycle due to Orthotic bracing.rnAcceleration data was taken as an input from Inertial Motion Sensor attached at the foot of arnhealthy subject. The algorithm was developed using Model based approach, by considering fourrnmachine learning classifiers. The performance of the algorithm was analyzed using thernperformance indicators.rnThe performance analysis illustrates that using categorical class type gives a relatively higherrnaccuracy with a maximum accuracy of 86%. The performance related to the classifier on thernother hand illustrates that Tree Bagger Classifier and Support Vector Machine classifier providerna higher accuracy with a balanced true rate. The analysis further indicate that the gait cycle of thernbraced side of an Ankle bracing is relatively expressed using the extracted features, it wasrnexpressed on average 58%. While the effect of the bracing on the unbraced side of the leg arernshown more with Knee bracing.rnKeywords: Gait classification, Feature extraction, Inertial motion sensor, Machine learning,rnOrthotic bracing, Performance analysis, Confusion matrix, hold out cross valdation.