Human activity recognition method based on molecular attributes

Acceleration sensor is extensively used in the field of human activity recognition, since it provides better recognition rate of human activity. Based on the principle of molecular attribute, a simple and adaptive activity recognition method is proposed using the acceleration data flow, which constitutes a serial activity, when the acceleration data are treated as the material flow with certain molecular structure. Then five molecular attributes including relative molecular mass, density, internal forces in a molecule, molecule stability, and attraction between molecules are introduced to recognize six human activities, since the closer molecular attribute means the more similar activity. Based on the calculated molecular attributes, a reliability-based voting method for human activity recognition is developed. Since each activity has respective motion cycle, a sliding window with variable sizes is put forward to enhance the recognition rate. Furthermore, adaptive incremental learning is designed to adapt to the different users. The long-time experimental results show that the proposed method is rather accurate and robust for different crowds. The average recognition rate achieves 97.2% for six human activities including walking, jogging, running, going upstairs, going downstairs, and sitting down.