Human Activities and Postural Transitions Classification using Support Vector Machine and K-Nearest Neighbor Methods

Abstract

Nowadays, the gyroscope and accelerometer sensors are available on almost all smartphone devices. One of the sensor uses is to sensor is to determine body's position. It is related to human activity that could be related to someone's fitness level, and monitoring it is one of the main focus smart city system. By using classification method, we can determine body's position. The experiment was conducted using k-nearest neighbor (KNN) with n-neighbors 3, 5, 7, and 9, and support vector machine (SVM) which kernels were polynomial, radial basis function (RBF), and sigmoid methods. The results of the K-NN method for all n-neighbor variations were 85.3% - 85.7% for 10 folds of cross validation. While for SVM, only the RBF kernel had a good result with 86.0% for 10 folds of cross-validation. So it can be concluded that K-NN and SVM with kernel RBF had good result.