Improving Human Activity Recognition Algorithms using Wireless Body Sensors and SVM

The accurate individuals' activities description is an important assignments in human computer interface. Many applications can be developed based on correct and fast activity recognition, such as healthcare monitoring and fall detection applications. Human Activity Recognition (HAR) is an active research field, where some key factors still challenging and need to be enhanced for faster and accurate recognition. This paper presents a novel approach aims to improve the activity recognition time by aggregating the raw data from inertial sensors into one time series used as input, then various features are extracted from the resulted signal by dimensionality reduction procedure, Those features are used as input data for the classification algorithm without affecting the information associated with activity in the raw data. The results of the novel method showed an improvement in recognition time while retaining the same level of accuracy.