Analysis reference

Gait

nimbalwear

  • Inputs: ankle-worn IMUs
  • Event detection: step detection events (pushoff, early-swing, late-swing, heelstrike)
  • Detector parameters:
    • pushoff_threshold: 0.85,
    • pushoff_time: 0.4,
    • swing_phase_time: 0.2,
    • heel_strike_detect_time: 0.5,
    • heel_strike_threshold: -5,
    • foot_down_time: 0.05

  • Feature extraction metrics:

    • step_foot
    • step_start/end/duration
    • pushoff/early-swing/late-swing/heelstrike duration
    • maximum/average step acceleration

  • Reference: https://doi.org/10.1186/s44247-024-00062-3

@article{Beyer2024,
title = {NiMBaLWear analytics pipeline for wearable sensors: a modular,  open-source platform for evaluating multiple domains of health and behaviour},
volume = {2},
ISSN = {2731-684X},
url = {http://dx.doi.org/10.1186/s44247-024-00062-3},
DOI = {10.1186/s44247-024-00062-3},
number = {1},
journal = {BMC Digital Health},
publisher = {Springer Science and Business Media LLC},
author = {Beyer,  Kit B. and Weber,  Kyle S. and Cornish,  Benjamin F. and Vert,  Adam and Thai,  Vanessa and Godkin,  F. Elizabeth and McIlroy,  William E. and Van Ooteghem,  Karen},
year = {2024},
month = feb 
}

paradigma

  • Inputs: wrist-worn IMUs
  • Event detection: arm swing angle time series
  • Detector parameters:
    • yz_columns = ["y", "z"]
  • Feature extraction metrics:
    • peak arm swing velocity
    • arm swing amplitude
  • Reference: https://doi.org/10.1186/s12984-025-01578-z
@article{Post2025,
title = {Quantifying arm swing in Parkinson’s disease: a method accounting for arm activities during free-living gait},
volume = {22},
ISSN = {1743-0003},
url = {http://dx.doi.org/10.1186/s12984-025-01578-z},
DOI = {10.1186/s12984-025-01578-z},
number = {1},
journal = {Journal of NeuroEngineering and Rehabilitation},
publisher = {Springer Science and Business Media LLC},
author = {Post,  Erik and Laarhoven,  Twan van and Raykov,  Yordan P. and Little,  Max A. and Nonnekes,  Jorik and Heskes,  Tom M. and Bloem,  Bastiaan R. and Evers,  Luc J. W.},
year = {2025},
month = feb 
}

ECG

pan-tompkins

  • Inputs: ECG data
  • Event detection: R-peak event times.
  • Detector parameters:
    • ecg_channel: "ecg0"
  • Feature extraction metrics:
    • HRV: Root mean square successive differences
    • HRV: SD R-R intervals (SDNN)
    • HRV: % of R-R intervals greater than 50ms (PNN50)
  • Reference: https://doi.org/10.1109/TBME.1985.325532
@article{Pan1985,
  title = {A Real-Time QRS Detection Algorithm},
  volume = {BME-32},
  ISSN = {0018-9294},
  url = {http://dx.doi.org/10.1109/TBME.1985.325532},
  DOI = {10.1109/tbme.1985.325532},
  number = {3},
  journal = {IEEE Transactions on Biomedical Engineering},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  author = {Pan,  Jiapu and Tompkins,  Willis J.},
  year = {1985},
  month = mar,
  pages = {230–236}
}