Sensor-Based Activity Recognition with Improved GP-based Classifier

Compared to conventional activity recognition methods using feature extraction followed by classification, the Genetic Programming (GP) based classification applied to raw sensor data can avoid the time-consuming and knowledge-dependent feature extraction procedure. However, the traditional GP-based classifier using accuracy as fitness function is sensitive to the choice of threshold values. Furthermore, sensor data of the same activity might demonstrate remarkable distinction when the signal is collected in the changing environment, which will lead to inconsistency between training and testing data and consequently degrade the generalization power of the trained classifier. Moreover, the GP-based classifier cannot well distinguish less separable activities in the presence of multiple activities. Our work aims to address these issues by improving the GP-based classifier via: (1) using the area under the receiver operating characteristic curve (AUC) as fitness function, (2) using an online local time series normalization procedure to pre-smooth undesirable features, and (3) using a binary tree based classification framework to force GP to learn key discriminating features that can better distinguish less separable activities. We test the proposed method on a sensor data set collected from a smartphone, consisting of four common human activities, sitting, standing, walking and running. The proposed GP-based classifier achieves the outstanding performance on recognizing each of four activities in terms of both high true positive and low false alarm rates, which much improves over the traditional GP-based classifier and several of its variants.