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IMU Consensus Exception Detection with Dynamic Time Warping-A Comparative Approach.

Chan-Yun Yang1, Pei-Yu Chen2, Te-Jen Wen3

  • 1Department of Electrical Engineering, National Taipei University, New Taipei City 23741, Taiwan. cyyang@mail.ntpu.edu.tw.

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|May 17, 2019
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Summary

Dynamic Time Warping (DTW) effectively detects motion abnormalities using inertial measurement units (IMUs). This algorithm excels at capturing time-varying patterns for remote patient monitoring, outperforming artificial neural networks.

Keywords:
dynamic time warpingremote abnormality detectionthe inertial measurement unit

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Remote motion abnormality detection is crucial for patient monitoring.
  • Traditional fixed-interval sampling struggles with dynamic motion patterns from inertial measurement units (IMUs).
  • Dynamic Time Warping (DTW) offers a nonlinear pattern-matching solution for time-varying signals.

Purpose of the Study:

  • To evaluate the efficacy of the Dynamic Time Warping (DTW) algorithm for motion-sensitive microelectronic systems.
  • To assess DTW's capability in remote motion abnormality detection using IMU data.
  • To compare DTW's performance against artificial neural network (ANN) frameworks.

Main Methods:

  • Implemented a DTW algorithm for analyzing IMU motion signal sequences.
  • Utilized DFT interpolation and convolutional preprocessors to standardize sequence lengths for ANN comparison.
  • Compared DTW's direct template-matching approach with two ANN frameworks.

Main Results:

  • The DTW algorithm demonstrated superior performance in detecting time-varying and speed-varying motion abnormalities.
  • DTW reliably captured consensus exceptions in motion patterns.
  • ANN frameworks, even with preprocessors, did not match DTW's effectiveness for this specific application.

Conclusions:

  • The proposed DTW algorithm significantly enhances the viability of remote motion abnormality detection systems.
  • DTW is a practical and reliable method for identifying exceptional motions in patient monitoring.
  • DTW's nonlinear pattern-matching is well-suited for the complexities of IMU-derived motion data.