Modeling of Noisy Acceleration Signals From Quasi-Periodic Movements for Drift-Free Position Estimation

IEEE J Biomed Health Inform. 2019 Jul;23(4):1558-1565. doi: 10.1109/JBHI.2018.2868370. Epub 2018 Sep 3.


Objective: We present a novel approach to drift-free position estimation from noisy acceleration signals, which often arise from quasi-periodic small-amplitude body movements. In contrast to the existing methods, this data-driven strategy is designed to properly describe time-variant harmonic structures in single-channel acceleration signals for low signal-to-noise ratios.

Methods: It comprises three processing steps: 1) short-time modeling of acceleration dynamics (instantaneous harmonic amplitudes and phases) in the analysis frame, 2) analytical integration that yields short-time position, and 3) overlap-add recombination for full-length position synthesis.

Results: The comparative results, obtained from the medio-lateral X-acceleration components from 30-s chair stand test recordings, suggest that the proposed method outperforms two state-of-the-art reference methods in terms of Euclidean error, root mean square error, correlation coefficient, and harmonic-to-noise ratio.

Conclusion: A major benefit of the method is that acceleration signal components unrelated to movement are suppressed in the whole analysis bandwidth, which allows for position estimation completely free of low-frequency artifacts.

Significance: We believe that the method can be useful in frailty assessment in elderly population, as well as in clinical applications related to gait analysis in aging and rehabilitation.

MeSH terms

  • Accelerometry
  • Adult
  • Algorithms
  • Artifacts
  • Exercise Test
  • Female
  • Humans
  • Male
  • Movement / physiology*
  • Posture / physiology*
  • Signal Processing, Computer-Assisted*
  • Young Adult