Forecasting Model of Combining Mini Batch K Means and Kohonen Maps to Cluster and Evaluate Gait Kinematics Data

Abstract

When people are getting old, some gait abnormalities may have happened in their walking patterns. It means, there may be slight differences in their physical performance. Due to the complexity of that evaluation, a machine learning algorithm can be used to cluster the gait patterns. Kohonen Maps (KM) and mini-batch k-means (MBKM) have been combined to cluster the gait parameters according to the age groups to identify the principal gait characteristics which are affected to the walking pattern. Dataset is consisting of 180 gait data based on the data which have been gained through the inertial measurement unit (IMU). When analysing the results, the proposed algorithm is showing low computational cost and time which is more efficient. As well the results have been proved that the cadence is the most important and affected gait parameter when caused to a walking pattern of a person when he or she is getting older. These results provide clues for the health professionals to identify and evaluate the difficulties of walking patterns of patients according to age.