Wearable Computing for Internet of Things: A Discriminant Approach for Human Activity Recognition

Abstract

With the rapid development of the wireless sensor network and the continuous improvement of its key technologies, the concept of Internet of Things has been encouraged and extended due to its wide applications in scenarios, such as smart homes and healthcare. Under the background, human activity recognition has drawn great attention in recent years. In this paper, we present a discriminant approach to recognize daily human activities recorded through accelerometer sensor. In the proposed approach, we first use S transform (ST) to extract features, and then introduce a supervised regularization-based robust subspace (SRRS) learning method to learn low-dimensional intrinsic feature representation from the original feature subspace. Particularly, ST has been described as a joint time-frequency representation, which is insensitive to noise. SRRS can learn more robust and discriminative features to reinforce the descriptions of samples while removing noise and redundancy. Experiments are conducted on three publicly available datasets, i.e., wireless sensor data mining, SCUT-NAA, and mHealth demonstrating the superior performance of our proposed scheme compared with state-of-the-art methods.