SVM versus MAP on accelerometer data to distinguish among locomotor activities executed at different speeds

Two approaches to the classi\fcation of dierent locomotor activities performed. at various speeds are here presented and compared: a maximum a posteriori. (MAP) Bayes' classi\fcation scheme and a Support Vector Machine (SVM). are applied on a 2D projection of 16 features extracted from accelerometer. data. The locomotor activities (level walking, stair climbing, and stair de-. scending) were recorded by a single-axis accelerometer placed on the shank. (preferred leg), performed in a natural indoor-outdoor scenario by 10 healthy. young adults (age 25-35 yrs.). From each segmented activity epoch, sixteen. features were chosen in the frequency and time domain. Dimension reduc-. tion was then performed through 2D Sammon's mapping. An Arti\fcial Neural. Network (ANN) was then trained to mimic Sammon's mapping on the whole. dataset. In the Bayes' approach, the two features were then fed to a Bayes'. classi\fer that incorporates an update rule, while, in the SVM scheme, the. ANN was considered as the kernel function of the classi\fer. Bayes' approach. performed slightly better than SVM on both the training set (91.4% vs. 90.7%). and the testing set (84.2% vs. 76.0%), favoring the proposed Bayes' scheme. as more suitable than the proposed SVM in distinguishing among the dierent. monitored activities.