Abstract

This paper evaluates a new and adaptive real-time cadence detection algorithm (CDA) for unconstrained sensor placement during walking and running. Conventional correlation procedures, dependent on sensor position and orientation, may alternately detect either steps or strides and consequently suffer from false negatives or positives. To overcome this limitation, the CDA validates correlation peaks as strides using the Sylvester's criterion (SC). This paper compares the CDA with conventional correlation methods.22 volunteers completed 7 different circuits (approx. 140 m) at three gaits-speeds: walking (1.5 m s- 1), running (3.4 m s- 1), and sprinting (5.2 and 5.7 m s- 1), disturbed by various gait-related activities. The algorithm was simultaneously evaluated for 10 different sensor positions. Reference strides were obtained from a foot sensor using a dedicated offline algorithm.The described algorithm resulted in consistent numbers of true positives (85.6-100.0%) and false positives (0.0-2.9%) and showed to be consistently accurate for cadence feedback across all circuits, subjects and sensors (mean ± SD: 98.9 ± 0.2%), compared to conventional cross-correlation (87.3 ± 13.5%), biased (73.0 ± 16.2) and unbiased (82.2 ± 20.6) autocorrelation procedures.This study shows that the SC significantly improves cadence detection, resulting in robust results for various gaits, subjects and sensor positions.

LanguageEnglish
Pages49-58
Number of pages10
JournalMedical Engineering and Physics
Volume52
Issue numberFebruary
Early online date17 Jan 2018
DOIs
StatePublished - Feb 2018

Cite this

@article{271b0260f3594de298a961315e741725,
title = "An adaptive, real-time cadence algorithm for unconstrained sensor placement",
abstract = "This paper evaluates a new and adaptive real-time cadence detection algorithm (CDA) for unconstrained sensor placement during walking and running. Conventional correlation procedures, dependent on sensor position and orientation, may alternately detect either steps or strides and consequently suffer from false negatives or positives. To overcome this limitation, the CDA validates correlation peaks as strides using the Sylvester's criterion (SC). This paper compares the CDA with conventional correlation methods.22 volunteers completed 7 different circuits (approx. 140 m) at three gaits-speeds: walking (1.5 m s- 1), running (3.4 m s- 1), and sprinting (5.2 and 5.7 m s- 1), disturbed by various gait-related activities. The algorithm was simultaneously evaluated for 10 different sensor positions. Reference strides were obtained from a foot sensor using a dedicated offline algorithm.The described algorithm resulted in consistent numbers of true positives (85.6-100.0\{%}) and false positives (0.0-2.9\{%}) and showed to be consistently accurate for cadence feedback across all circuits, subjects and sensors (mean ± SD: 98.9 ± 0.2\{%}), compared to conventional cross-correlation (87.3 ± 13.5\{%}), biased (73.0 ± 16.2) and unbiased (82.2 ± 20.6) autocorrelation procedures.This study shows that the SC significantly improves cadence detection, resulting in robust results for various gaits, subjects and sensor positions.",
keywords = "Accelerometer, Autocorrelation, Cadence, Cross-correlation, Gait cycle detection, Running, Stride frequency, Sylvester's criterion",
author = "{van Oeveren}, {B. T.} and {de Ruiter}, {C. J.} and Beek, {P. J.} and Rispens, {S. M.} and {van Die\{"e}n}, {J. H.}",
note = "Copyright \{circledC} 2018 IPEM. Published by Elsevier Ltd. All rights reserved.",
year = "2018",
month = "2",
doi = "10.1016/j.medengphy.2017.12.007",
language = "English",
volume = "52",
pages = "49--58",
journal = "Medical Engineering and Physics",
issn = "1350-4533",
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}

An adaptive, real-time cadence algorithm for unconstrained sensor placement. / van Oeveren, B. T.; de Ruiter, C. J.; Beek, P. J.; Rispens, S. M.; van Dieën, J. H.

In: Medical Engineering and Physics, Vol. 52, No. February, 02.2018, p. 49-58.

Research output: Contribution to journalArticle

TY - JOUR

T1 - An adaptive, real-time cadence algorithm for unconstrained sensor placement

AU - van Oeveren,B. T.

AU - de Ruiter,C. J.

AU - Beek,P. J.

AU - Rispens,S. M.

AU - van Dieën,J. H.

N1 - Copyright © 2018 IPEM. Published by Elsevier Ltd. All rights reserved.

PY - 2018/2

Y1 - 2018/2

N2 - This paper evaluates a new and adaptive real-time cadence detection algorithm (CDA) for unconstrained sensor placement during walking and running. Conventional correlation procedures, dependent on sensor position and orientation, may alternately detect either steps or strides and consequently suffer from false negatives or positives. To overcome this limitation, the CDA validates correlation peaks as strides using the Sylvester's criterion (SC). This paper compares the CDA with conventional correlation methods.22 volunteers completed 7 different circuits (approx. 140 m) at three gaits-speeds: walking (1.5 m s- 1), running (3.4 m s- 1), and sprinting (5.2 and 5.7 m s- 1), disturbed by various gait-related activities. The algorithm was simultaneously evaluated for 10 different sensor positions. Reference strides were obtained from a foot sensor using a dedicated offline algorithm.The described algorithm resulted in consistent numbers of true positives (85.6-100.0%) and false positives (0.0-2.9%) and showed to be consistently accurate for cadence feedback across all circuits, subjects and sensors (mean ± SD: 98.9 ± 0.2%), compared to conventional cross-correlation (87.3 ± 13.5%), biased (73.0 ± 16.2) and unbiased (82.2 ± 20.6) autocorrelation procedures.This study shows that the SC significantly improves cadence detection, resulting in robust results for various gaits, subjects and sensor positions.

AB - This paper evaluates a new and adaptive real-time cadence detection algorithm (CDA) for unconstrained sensor placement during walking and running. Conventional correlation procedures, dependent on sensor position and orientation, may alternately detect either steps or strides and consequently suffer from false negatives or positives. To overcome this limitation, the CDA validates correlation peaks as strides using the Sylvester's criterion (SC). This paper compares the CDA with conventional correlation methods.22 volunteers completed 7 different circuits (approx. 140 m) at three gaits-speeds: walking (1.5 m s- 1), running (3.4 m s- 1), and sprinting (5.2 and 5.7 m s- 1), disturbed by various gait-related activities. The algorithm was simultaneously evaluated for 10 different sensor positions. Reference strides were obtained from a foot sensor using a dedicated offline algorithm.The described algorithm resulted in consistent numbers of true positives (85.6-100.0%) and false positives (0.0-2.9%) and showed to be consistently accurate for cadence feedback across all circuits, subjects and sensors (mean ± SD: 98.9 ± 0.2%), compared to conventional cross-correlation (87.3 ± 13.5%), biased (73.0 ± 16.2) and unbiased (82.2 ± 20.6) autocorrelation procedures.This study shows that the SC significantly improves cadence detection, resulting in robust results for various gaits, subjects and sensor positions.

KW - Accelerometer

KW - Autocorrelation

KW - Cadence

KW - Cross-correlation

KW - Gait cycle detection

KW - Running

KW - Stride frequency

KW - Sylvester's criterion

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U2 - 10.1016/j.medengphy.2017.12.007

DO - 10.1016/j.medengphy.2017.12.007

M3 - Article

VL - 52

SP - 49

EP - 58

JO - Medical Engineering and Physics

T2 - Medical Engineering and Physics

JF - Medical Engineering and Physics

SN - 1350-4533

IS - February

ER -