Complex activity recognition using time series pattern dictionary learned from ubiquitous sensors

Sensor-based human activity recognition has become an important research field within pervasive and ubiquitous computing. Techniques for recognizing atomic activities such as gestures or actions are mature for now, but complex activity recognition still remains a challenging issue. In this paper, we address the problem of complex activity recognition using time series extracted from multiple sensors. We first build a dictionary of time series patterns, called shapelets, to represent atomic activities, then present three shapelet-based models to recognize sequential, concurrent, and generic complex activities. We use the datasets collected from three different labs to evaluate our shapelet-based approach and the results show that our approach can handle complex activity recognition effectively. Our experimental results also show that the shapelet-based approach outperforms other competing approaches in terms of recognition accuracy and system usage.