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Intelligent Activity Awareness Systems - New Classification Method and Comparison Study

Research in mobile activity awareness has attracted much attention lately due to the lack of mechanisms to guide individuals to engage in health-promoting life styles, including diet and exercise. In fact, several studies have successfully proposed activity classifiers in order to develop intelligent activity awareness systems using off-the-shelf mobile devices such as smart phones. However, most exist- ing research applies customized sensors (i.e., with a much higher data sampling rate) from multiple sensor technologies (such as GPS, cellular or WiFi radio, accelerometer, digital compass, and others). They are not practical and therefore this research uses only an ordinary accelerometer sensor from a commercial phone device. It first applies the Gabor filter to this activity recognition field and then it studies the effectiveness of this new method against several existing classifiers, including nearest neighbors, support vector machines, and linear discriminant analysis. Results from this work show that with appropriate data sampling windows and feature vectors, a classification accuracy of over 90% is a realistic goal for the Gabor and most existing classifiers.