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

研究成果: Conference contribution

1 引用 (Scopus)

抜粋

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.

元の言語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
出版者Institute of Electrical and Electronics Engineers Inc.
ページ1-6
ページ数6
2017-December
ISBN(電子版)9781509065462
DOI
出版物ステータス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

Other

Other24th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2017
New Zealand
Auckland
期間17/11/2117/11/23

ASJC Scopus subject areas

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

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  • これを引用

    Ishikawa, T., Hayami, H., & Murakami, T. (2017). Comprehensive evaluation of human activity classification based on inertia measurement unit with air pressure sensor. : 2017 24th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2017 (巻 2017-December, pp. 1-6). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/M2VIP.2017.8211471