Human activity recognition method based on molecular attributes

Hengnian Qi, Kai Fang, Xiaoping Wu, Lili Xu and Qing Lang

International Journal of Distributed Sensor Networks, 2019, vol. 15, issue 4, 1550147719842729

Abstract: 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.

Keywords: Activity recognition; acceleration sensor; molecular feature; variable sliding window; incremental learning (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/1550147719842729 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:15:y:2019:i:4:p:1550147719842729

DOI: 10.1177/1550147719842729

Access Statistics for this article

More articles in International Journal of Distributed Sensor Networks
Bibliographic data for series maintained by SAGE Publications ().