A Comparative Study on Human Activity Recognition Using Inertial Sensors in a Smartphone

TLDR

Activity recognition bridges low‑level sensor data to high‑level applications in ambient‑assisted living, and smartphone‑embedded inertial sensors are widely used because they are convenient, low‑cost, and non‑intrusive. The study aims to evaluate the effectiveness of smartphone triaxial accelerometer and gyroscope data, alone or combined, for human activity recognition and to develop a feature‑selection method that enhances generalization while reducing power consumption. The authors employ both accelerometer and gyroscope streams, propose a novel discriminant‑feature selection algorithm, and construct an online recognizer that operates efficiently on smartphone hardware. Experiments on a public dataset demonstrate that fusing accelerometer and gyroscope data improves recognition accuracy over single‑sensor use, that the proposed selector outperforms three alternatives across four metrics, and that it yields significant time savings, enabling power‑efficient real‑world deployment.

Abstract

Activity recognition plays an essential role in bridging the gap between the low-level sensor data and the high-level applications in ambient-assisted living systems. With the aim to obtain satisfactory recognition rate and adapt to various application scenarios, a variety of sensors have been exploited, among which, smartphone-embedded inertial sensors are widely applied due to its convenience, low cost, and intrusiveness. In this paper, we explore the power of triaxial accelerometer and gyroscope built-in a smartphone in recognizing human physical activities in situations, where they are used simultaneously or separately. A novel feature selection approach is then proposed in order to select a subset of discriminant features, construct an online activity recognizer with better generalization ability, and reduce the smartphone power consumption. Experimental results on a publicly available data set show that the fusion of both accelerometer and gyroscope data contributes to obtain better recognition performance than that of using single source data, and that the proposed feature selector outperforms three other comparative approaches in terms of four performance measures. In addition, great improvement in time performance can be achieved with an effective feature selector, indicating the way of power saving and its applicability to real-world activity recognition.