Data driven recognition of interleaved and concurrent human activities with nonlinear characteristics

Abstract

Sensor-based human activity recognition gained a lot of research interest within the field of pervasive computing due to its wide range of application domains. Recognition of complex human activities is a challenging task due to the tendency of humans to perform activities in an interleaved and concurrent scenario. In this paper, we address the problem of complex activities recognition using a combination of the discriminative features called Strong Jumping Emerging Patterns (SJEPs) and the fuzzy sets theory. The proposed approach is designed to fit the challenges of multi-label classification, nonlinear separation, and recognition of multiple overlaps of interleaved and concurrent activities. Besides the need for a training dataset of complex activities that is difficult to obtain. The proposed approach uses a training dataset of simple activities to extract two sets of SJEPs for linear and nonlinear activities. Then, a novel SJEP-based recognition approach is presented to recognize simple and complex activities. We evaluate our approach using two datasets collected from two different labs. Experimental results show the efficiency of our approach in recognizing simple and complex human activities, besides the superiority of our approach against other competing approaches with regard to recognition accuracy.