Enhanced Annotation Framework for Activity Recognition Through Change Point Detection

The task of Activity Recognition (AR) on ubiquitous sensor data is traditionally performed on annotated datasets where manually identified change points denoting the start and the end of the activities are present. However, the majority of the real-world smart home applications generate un-annotated data streams, where such change points are not known in prior. In this paper, we address this problem by proposing a real-time annotation framework for Activity Recognition based on Change Point Detection (CPD). First, we investigate the components of feature extraction, data augmentation, noise handling, and classification to propose an optimal AR framework for the chosen datasets. We then propose S-CPD, a novel transfer learning based CPD algorithm, which uses similarities of the output probability distributions in order to generate a change point index (CPI) corresponding to each of the sensor readings in the data stream. Based on this calculated CPI, we segment the data stream and allows us to perform enhanced annotations. To test the efficiency of this proposed annotation framework, we perform extensive experimentation on 4 real-world smart home datasets. Our proposed solutions outperform the existing state-of-the-art AR and annotation frameworks on these datasets by around 1.6% and 14% respectively, while providing comparable performance with that of the state-of-the-art CPD algorithm. In particular, we achieve an average AR and annotation accuracy of 96.64 % and 94.15% respectively, with an average sensor distance error of 1.1 across the 4 datasets.