A machine learning-based method in body movement tracking with a small number of sensors

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Authors

  • Dang Hoang Minh (Corresponding Author) Military Information Technology Institute, Academy of Military Science and Technology
  • Phung Nhu Hai Military Information Technology Institute, Academy of Military Science and Technology
  • Luu Van Sang Military Information Technology Institute, Academy of Military Science and Technology
  • Vu Hoang Minh Military Information Technology Institute, Academy of Military Science and Technology

DOI:

https://doi.org/10.54939/1859-1043.j.mst.FEE.2022.171-176

Keywords:

Inertial Measurement Unit - IMU; Decision Tree Regression (DTR).

Abstract

Most of the current body sensing devices are composed of inertial measurement units (IMUs). The IMU sensors are placed at a different position on the human body and sense their position, rotation, and tilt angle in space, thereby interpolating the movement of parts and the entire human body. Although IMU sensors have high accuracy and fast processing speed, they suffer from a major limitation of being susceptible to external magnetic field sources. This makes the process of re-interpolating the human body become inaccurate in an environment where many strong magnetic fields exist such as metal frames, computers, etc. In this paper, we propose a model to predict the postures of the upper human body, from 03 stable inputs (head, right hand, left hand), thereby reducing the usage of IMU sensors.

References

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Published

30-12-2022

How to Cite

Đặng Hoàng Minh, Phùng Như Hải, Lưu Văn Sáng, and Vũ Hoàng Minh. “A Machine Learning-Based Method in Body Movement Tracking With a Small Number of Sensors”. Journal of Military Science and Technology, no. FEE, Dec. 2022, pp. 171-6, doi:10.54939/1859-1043.j.mst.FEE.2022.171-176.

Issue

Section

Research Articles