Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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Relative Position Estimation using Kalman Filter Based on Inertial Sensor Signals Considering Soft Tissue Artifacts of Human Body Segments

신체 분절의 연조직 변형을 고려한 관성센서신호 기반의 상대위치 추정 칼만필터

  • Lee, Chang June (Mechanical Engineering, Hankyong National Unversity) ;
  • Lee, Jung Keun (School of ICT, Robotics & Mechanical Engineering, Hankyong National Unversity)
  • 이창준 (한경대학교 기계공학과) ;
  • 이정근 (한경대학교 ICT 로봇기계공학부)
  • Received : 2020.07.15
  • Accepted : 2020.07.17
  • Published : 2020.07.31
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Abstract

This paper deals with relative position estimation using a Kalman filter (KF) based on inertial sensors that have been widely used in various biomechanics-related outdoor applications. In previous studies, the relative position is determined using relative orientation and predetermined segment-to-joint (S2J) vectors, which are assumed to be constant. However, because body segments are influenced by soft tissue artifacts (STAs), including the deformation and sliding of the skin over the underlying bone structures, they are not constant, resulting in significant errors during relative position estimation. In this study, relative position estimation was performed using a KF, where the S2J vectors were adopted as time-varying states. The joint constraint and the variations of the S2J vectors were used to develop a measurement model of the proposed KF. Accordingly, the covariance matrix corresponding to the variations of the S2J vectors continuously changed within the ranges of the STA-causing flexion angles. The experimental results of the knee flexion tests showed that the proposed KF decreased the estimation errors in the longitudinal and lateral directions by 8.86 and 17.89 mm, respectively, compared with a conventional approach based on the application of constant S2J vectors.

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References

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