Time Domain Feature Extraction and SVM Processing for Activity Recognition Using Smartphone Signals
Keywords:
Smartphone, Accelerometer, Activity recognition, Time domain feature, SVMAbstract
Automatic classification of human movement is a feature that is desired for a multitude of applications and mobile phone technology continuously evolves and incorporates more and more sensors to enable advanced applications. Combining these two concepts we can deal with “an activity recognition via smartphone sensors” problem where sensors of these devices play a core role when we deal with personalized activity tracking systems. In this paper we give an overview of the recent work in the field of activity recognition from mobile devices that can be attached to different parts of the body (pocket, wrist, and forearm). We focus on the technique of feature extraction from raw acceleration signal sequences of smartphone (mean, standard deviation, minimal and maximal signal values, correlation, median crossing). Further processing of these data allowed to classify the activity performed by the user. The core classification stage of the current approach was based on the method of “learning with the teacher” where the features of signal sequences were analyzed using the support vector machines (SVM) learning method.
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