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Modern prostheses can be controlled by using gait analysis data from Inertial Measurement Units compared to traditional prostheses. This article aims to classify foot movements for the robotic ankle system in lower limb prostheses to recognize motion intent and adapt to abnormal walking conditions. The statistical features are extracted from IMU data from 11 volunteers aged 20-34 and then the features are classified using machine learning. In this study, the classification accuracies of Naïve Bayes Classifier, Linear Discriminant Analysis, K-Nearest Neighbour Classifier and Support Vector Machines and Artificial Neural Networks in classifying foot movements are examined separately for the raw data and the processed data such as Euler angles and quaternions which estimate with Madwick Filter. Gait analysis data were obtained by using the Inemo inertial module LSM9DS1 work on an NRF52 including 9 DOF, triaxial gyroscope, triaxial accelerometer, and triaxial magnetometer in the Biomechanics Laboratory of the Department of Mechanical Engineering, Middle East Technical University from eleven subjects and achieved an highest classification accuracy rate of 90.9% on test data, 97.3% for training data.

Today, Inertial Measurement Units is used for control in lower extremity prosthesis studies. In this article, an application related to the analysis and classification of foot movements such as dorsiflexion, plantarflexion, inversion and eversion is presented. This study aims to perform the classification of foot movements to recognize the movement pattern and to adapt to abnormal walking conditions for the robotic foot system. Nine parameters are measured with motion data from the IMU sensor connected to the metatarsal of the foot from eleven volunteers aged 20-34 years. Size is reduced by extracting statistical properties such as sum, mean, standard deviation, covariance, skewness and kurtosis from these parameters. Classification process is performed with classifiers such as Decision Tree, Linear Discriminant Analysis, Naïve Bayes Classifier, K-Nearest Neighbor and Support Vector Machine separately for each person. The classification accuracies obtained for 11 volunteers are averaged and the highest accuracy is obtained with 97.2% for KNN.