%0 Journal Article
%T Human activity recognition method based on molecular attributes
%A Hengnian Qi
%A Kai Fang
%A Lili Xu
%A Qing Lang
%A Xiaoping Wu
%J International Journal of Distributed Sensor Networks
%@ 1550-1477
%D 2019
%R 10.1177/1550147719842729
%X 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
%K Activity recognition
%K acceleration sensor
%K molecular feature
%K variable sliding window
%K incremental learning
%U https://journals.sagepub.com/doi/full/10.1177/1550147719842729