Comparison of Feature Extraction Techniques for Ambient Sensor-based In-home Activity Recognition

Abstract

Ambient sensor-based in-home activity recognition plays a crucial role in the design and development of a smart home to better and actively respond to population aging. From the perspective of machine learning, how to extract features from sensor data largely determines the power of a data-driven human activity recognizer. However, few studies systematically investigate how to encode streaming sensor events. To this end, we herein conduct a comparison of different feature extraction techniques for activity recognition. Specifically, we explore two types of feature representations (i.e., statistical features and structural features) and evaluate their single use and joint use. Besides, we experimentally analyze the impact of window size on prediction accuracy. Finally, we perform experiments on three public datasets with 15 different feature encodings and 6 classifiers. Results show that the joint use of different features generally obtains enhanced accuracy and that the interval 60s of window size achieves a better accuracy-speed tradeoff.