Human activity recognition based on multienvironment sensor data

Abstract

With the development of artificial intelligence and the broad application of sensors, human activity recognition (HAR) technologies based on noninvasive environmental sensors have received extensive attention and have shown great application value. Owing to the initiative of human activities and machine learning-based methods relying on domain knowledge, obtaining a uniform model to understand the daily behaviors of different residents is difficult. From the perspective of data feature constraints to recognition methods, we constructed a methodology for single user's daily behavior recognition that can adaptively constrain the sensor noise during human activities in multitenant smart home scenarios. We propose a sensor data contribution significance analysis (CSA) method based on the sensor status frequency-inverse type frequency for HAR. This method is employed to measure the contribution of a particular type of sensor to a certain type of behavior recognition. We then build a spatial distance matrix based on the layout of environmental sensors for context-awareness and reducing data noise. Finally, we propose a HAR algorithm based on wide time-domain convolutional neural network and multienvironment sensor data (HAR_WCNN) for daily behavior recognition. Comparative experiment results on the CASAS dataset show that the proposed HAR_WCNN outperforms the compared state-of-the-art methods in terms of HAR accuracy and time consumption.