Recognition of Multiple Human Body Postures Based on Six-axis Sensor

DOI

10.2991/eame-18.2018.50How to use a DOI?

Keywords

wearable sensors; recognition of human postures; decision tree

Abstract

In the existing methods of recognition of multiple human body postures, recognition of human body postures based on wearable sensors has recently become a research hotspot because of its advantages such as simple information acquisition, low cost, and fast transmission. Based on the monitoring data collected by the six-axis sensor, this paper performs Kalman filtering on the data, and then selects a Gradient Boosting Decision Tree model from the classification algorithms in machine learning to classify and recognize various human behavior postures. And it could be benefit to key crowds such as the elderly.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Wei Li
AU  - Dongxiu Ou
PY  - 2018/06
DA  - 2018/06
TI  - Recognition of Multiple Human Body Postures Based on Six-axis Sensor
BT  - Proceedings of the 2018 3rd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2018)
PB  - Atlantis Press
SP  - 239
EP  - 243
SN  - 2352-5401
UR  - https://doi.org/10.2991/eame-18.2018.50
DO  - 10.2991/eame-18.2018.50
ID  - Li2018/06
ER  -