Acoustic Sensor Based Recognition of Human Activity in Everyday Life for Smart Home Services

Abstract

A novel activity recognition method is proposed based on acoustic information acquired from microphones in an unobtrusive and privacy-preserving manner. Behavior detection mechanisms may be useful in context-aware domains in everyday life, but they may be inaccurate, and privacy violation is a concern. For example, vision-based behavior detection using cameras is difficult to apply in a private space such as a home, and inaccuracies in identifying user behaviors reduce acceptance of the technology. In addition, activity recognition using wearable sensors is very uncomfortable and costly to apply for commercial purposes. In this study, an acoustic information-based behavior detection algorithm is proposed for use in private spaces. This system classifies human activities using acoustic information. It combines strategies of elimination and similarity and establishes new rules. The performance of the proposed algorithm was compared with that of commonly used classification algorithms such as case-based reasoning, k-nearest neighbors, support vector machine, and multiple regression