Wearable sensor-based human activity recognition from environmental background sounds

Understanding individual's activities, social interaction, and group dynamics of a certain society is one of fundamental problems that the social and community intelligence (SCI) research faces. Environmental background sound is a rich information source for identifying individual and social behaviors. Therefore, many power-aware wearable devices with sound recognition function are widely used to trace and understand human activities. The design of these sound recognition algorithms has two major challenges: limited computation resources and a strict power consumption requirement. In this paper, a new method for recognizing environmental background sounds with a power-aware wearable sensor is presented. By employing a novel low calculation one-dimensional (1-D) Haar-like sound feature with hidden Markov model (HMM) classification, this method can achieve high recognition accuracy while still meeting the wearable sensor's power requirement. Our experimental results indicate an average recognition accuracy of 96.9 % has been achieved when testing with 22 typical environmental sounds related to personal and social activities. It outperforms other commonly used sound recognition algorithms in terms of both accuracy and power consumption. This is very helpful and promising for future integration with other sensor(s) to provide more trustworthy activity recognition results for the SCI system.