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