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Medical & Biological Engineering & Computing

, Volume 55, Issue 10, pp 1773–1785 | Cite as

A wrist sensor and algorithm to determine instantaneous walking cadence and speed in daily life walking

  • Benedikt Fasel
  • Cyntia Duc
  • Farzin Dadashi
  • Flavien Bardyn
  • Martin Savary
  • Pierre-André Farine
  • Kamiar AminianEmail author
Original Article

Abstract

In daily life, a person’s gait—an important marker for his/her health status—is usually assessed using inertial sensors fixed to lower limbs or trunk. Such sensor locations are not well suited for continuous and long duration measurements. A better location would be the wrist but with the drawback of the presence of perturbative movements independent of walking. The aim of this study was to devise and validate an algorithm able to accurately estimate walking cadence and speed for daily life walking in various environments based on acceleration measured at the wrist. To this end, a cadence likelihood measure was designed, automatically filtering out perturbative movements and amplifying the periodic wrist movement characteristic of walking. Speed was estimated using a piecewise linear model. The algorithm was validated for outdoor walking in various and challenging environments (e.g., trail, uphill, downhill). Cadence and speed were successfully estimated for all conditions. Overall median (interquartile range) relative errors were −0.13% (−1.72 2.04%) for instantaneous cadence and −0.67% (−6.52 6.23%) for instantaneous speed. The performance was comparable to existing algorithms for trunk- or lower limb-fixed sensors. The algorithm’s low complexity would also allow a real-time implementation in a watch.

Keywords

Inertial sensor Wrist Walking Cadence Speed 

Notes

Acknowledgements

This study was financed by the CTI Grant No 14787.1 PFNM-NM. The authors would like to thank all subjects that agreed walking in various meteorological conditions ranging from cold to hot and from sun to light rain.

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Copyright information

© International Federation for Medical and Biological Engineering 2017

Authors and Affiliations

  1. 1.Laboratory of Movement Analysis and MeasurementEcole Polytechnique Fédérale de LausanneLausanneSwitzerland
  2. 2.Electronics and Signal Processing LaboratoryEcole Polytechnique Fédérale de LausanneNeuchâtelSwitzerland

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