Human activity recognition from environmental background sounds for Wireless Sensor Networks

Sound feature extraction Mel Frequency Cepstral Coefficients (MFCC) and Vector Quantization (VQ) classification Linde-Buzo-Gray algorithm (LBG) algorithms are applied for recognizing the background sounds in the human daily activities. Applying these algorithms to twenty typical daily activity sounds, average recognition accuracy of 93.8% can be achieved. In these algorithms, how three parameters (i.e., Mel filters number, frame-to-frame overlap and LBG codebook cluster number) affect system's calculation burden and accuracy is also investigated. By adjusting these three parameters to an optimized combination, the multiplication and addition calculation burden can be reduced by 87.0% and 87.1% individually while maintaining the system's average accuracy rate at 92.5%. This is promising for future integration with other sensor (s) to fulfill daily activity recognition by using power aware Wireless Sensor Networks (WSN) systems.