From User-independent to Personal Human Activity Recognition Models Exploiting the Sensors of a Smartphone
This addresses the need for personalized activity recognition for smartphone users, though it is incremental as it builds on existing sensor-based methods.
The study tackled the problem of human activity recognition by developing a method to unobtrusively create user-dependent models using smartphone sensors, resulting in improved detection accuracy over traditional user-independent models in nine out of ten cases.
In this study, a novel method to obtain user-dependent human activity recognition models unobtrusively by exploiting the sensors of a smartphone is presented. The recognition consists of two models: sensor fusion-based user-independent model for data labeling and single sensor-based user-dependent model for final recognition. The functioning of the presented method is tested with human activity data set, including data from accelerometer and magnetometer, and with two classifiers. Comparison of the detection accuracies of the proposed method to traditional user-independent model shows that the presented method has potential, in nine cases out of ten it is better than the traditional method, but more experiments using different sensor combinations should be made to show the full potential of the method.