Comprehensive evaluation of human activity classification based on inertia measurement unit with air pressure sensor

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

This paper focuses on accuracy improvement of human activities detection and classification by using single Inertia Measurement Unit sensor (IMU sensor: an acceleration sensor, a gyro sensor, a magnetometer, and an air pressure sensor) which is a type of the wearable sensors. Generally, performance of classification model is determined by these methodologies; number and type of sensors, coordinate transformation, time window, time-frequency domain analysis, and machine learning algorithms. The contributions of this paper are summarized in the following three points. Firstly, a pressure sensor is additionally utilized to improve the accuracy of human activities estimation. This information is effective to estimate up/down motion by stair and elevator. Secondly, comprehensive evaluation of the combinations using different methodologies is conducted to find an optimal classification model. Thirdly, ensemble learning is performed to improve estimation accuracy. It shows superior performance with over 95 % accuracy of human activity estimation.

本文言語English
ホスト出版物のタイトル2017 24th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2017
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1-6
ページ数6
ISBN(電子版)9781509065462
DOI
出版ステータスPublished - 2017 12 14
イベント24th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2017 - Auckland, New Zealand
継続期間: 2017 11 212017 11 23

出版物シリーズ

名前2017 24th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2017
2017-December

Other

Other24th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2017
CountryNew Zealand
CityAuckland
Period17/11/2117/11/23

ASJC Scopus subject areas

  • Mechanical Engineering
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Control and Optimization

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