Identifying multiuser activity with overlapping acoustic data for mobile decision making in smart home environments

Smart services, one of the most intriguing areas of current Internet of Things(IoT) research, require improvement in terms of recognizing user activities. Sound is a useful medium for making decisions based on activity recognition in the smart home environment, which includes mobile devices such as sensors and actuators. Instead of visual sensors to recognize human activity, acoustic sensor data is acquired in an unobtrusive manner for greater privacy. However, multiuser activity provides a formidable challenge for acoustic data-based activity recognition systems because of the difficulty of identifying multiple sources of activity from among a variety of sounds. In our study, we propose a statistical method to detect the interval of interference, which is also known as the unexpected mesa, distinguishing activities based on the pre- and post-mesa intervals. The results suggest that the proposed method outperforms previously presented classification algorithms in terms of the accuracy of multiuser activity recognition. Future studies may utilize this method for improvement of existing smart home systems.