Cargando…

A step in the right direction: an open-design pedometer algorithm for dogs

BACKGROUND: Accelerometer-based technologies could be useful in providing objective measures of canine ambulation, but most are either not tailored to the idiosyncrasies of canine gait, or, use un-validated or closed source approaches. The aim of this paper was to validate algorithms which could be...

Descripción completa

Detalles Bibliográficos
Autores principales: Ladha, C., Belshaw, Z., O’Sullivan, J., Asher, L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5861607/
https://www.ncbi.nlm.nih.gov/pubmed/29558919
http://dx.doi.org/10.1186/s12917-018-1422-3
_version_ 1783308119786389504
author Ladha, C.
Belshaw, Z.
O’Sullivan, J.
Asher, L.
author_facet Ladha, C.
Belshaw, Z.
O’Sullivan, J.
Asher, L.
author_sort Ladha, C.
collection PubMed
description BACKGROUND: Accelerometer-based technologies could be useful in providing objective measures of canine ambulation, but most are either not tailored to the idiosyncrasies of canine gait, or, use un-validated or closed source approaches. The aim of this paper was to validate algorithms which could be applied to accelerometer data for i) counting the number of steps and ii) distance travelled by a dog. To count steps, an approach based on partitioning acceleration was used. This was applied to accelerometer data from 13 dogs which were walked a set distance and filmed. Each footfall captured on video was annotated. In a second experiment, an approach based on signal features was used to estimate distance travelled. This was applied to accelerometer data from 10 dogs with osteoarthritis during normal walks with their owners where GPS (Global Positioning System) was also captured. Pearson’s correlations and Bland Altman statistics were used to compare i) the number of steps measured on video footage and predicted by the algorithm and ii) the distance travelled estimated by GPS and predicted by the algorithm. RESULTS: Both step count and distance travelled could be estimated accurately by the algorithms presented in this paper: 4695 steps were annotated from the video and the pedometer was able to detect 91%. GPS logged a total of 20,184 m meters across all dogs; the mean difference between the predicted and GPS estimated walk length was 211 m and the mean similarity was 79%. CONCLUSIONS: The algorithms described show promise in detecting number of steps and distance travelled from an accelerometer. The approach for detecting steps might be advantageous to methods which estimate gross activity because these include energy output from stationary activities. The approach for estimating distance might be suited to replacing GPS in indoor environments or others with limited satellite signal. The algorithms also allow for temporal and spatial components of ambulation to be calculated. Temporal and spatial aspects of dog ambulation are clinical indicators which could be used for diagnosis or monitoring of certain diseases, or used to provide information in support of canine weight-loss programmes.
format Online
Article
Text
id pubmed-5861607
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-58616072018-03-26 A step in the right direction: an open-design pedometer algorithm for dogs Ladha, C. Belshaw, Z. O’Sullivan, J. Asher, L. BMC Vet Res Methodology Article BACKGROUND: Accelerometer-based technologies could be useful in providing objective measures of canine ambulation, but most are either not tailored to the idiosyncrasies of canine gait, or, use un-validated or closed source approaches. The aim of this paper was to validate algorithms which could be applied to accelerometer data for i) counting the number of steps and ii) distance travelled by a dog. To count steps, an approach based on partitioning acceleration was used. This was applied to accelerometer data from 13 dogs which were walked a set distance and filmed. Each footfall captured on video was annotated. In a second experiment, an approach based on signal features was used to estimate distance travelled. This was applied to accelerometer data from 10 dogs with osteoarthritis during normal walks with their owners where GPS (Global Positioning System) was also captured. Pearson’s correlations and Bland Altman statistics were used to compare i) the number of steps measured on video footage and predicted by the algorithm and ii) the distance travelled estimated by GPS and predicted by the algorithm. RESULTS: Both step count and distance travelled could be estimated accurately by the algorithms presented in this paper: 4695 steps were annotated from the video and the pedometer was able to detect 91%. GPS logged a total of 20,184 m meters across all dogs; the mean difference between the predicted and GPS estimated walk length was 211 m and the mean similarity was 79%. CONCLUSIONS: The algorithms described show promise in detecting number of steps and distance travelled from an accelerometer. The approach for detecting steps might be advantageous to methods which estimate gross activity because these include energy output from stationary activities. The approach for estimating distance might be suited to replacing GPS in indoor environments or others with limited satellite signal. The algorithms also allow for temporal and spatial components of ambulation to be calculated. Temporal and spatial aspects of dog ambulation are clinical indicators which could be used for diagnosis or monitoring of certain diseases, or used to provide information in support of canine weight-loss programmes. BioMed Central 2018-03-20 /pmc/articles/PMC5861607/ /pubmed/29558919 http://dx.doi.org/10.1186/s12917-018-1422-3 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Ladha, C.
Belshaw, Z.
O’Sullivan, J.
Asher, L.
A step in the right direction: an open-design pedometer algorithm for dogs
title A step in the right direction: an open-design pedometer algorithm for dogs
title_full A step in the right direction: an open-design pedometer algorithm for dogs
title_fullStr A step in the right direction: an open-design pedometer algorithm for dogs
title_full_unstemmed A step in the right direction: an open-design pedometer algorithm for dogs
title_short A step in the right direction: an open-design pedometer algorithm for dogs
title_sort step in the right direction: an open-design pedometer algorithm for dogs
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5861607/
https://www.ncbi.nlm.nih.gov/pubmed/29558919
http://dx.doi.org/10.1186/s12917-018-1422-3
work_keys_str_mv AT ladhac astepintherightdirectionanopendesignpedometeralgorithmfordogs
AT belshawz astepintherightdirectionanopendesignpedometeralgorithmfordogs
AT osullivanj astepintherightdirectionanopendesignpedometeralgorithmfordogs
AT asherl astepintherightdirectionanopendesignpedometeralgorithmfordogs
AT ladhac stepintherightdirectionanopendesignpedometeralgorithmfordogs
AT belshawz stepintherightdirectionanopendesignpedometeralgorithmfordogs
AT osullivanj stepintherightdirectionanopendesignpedometeralgorithmfordogs
AT asherl stepintherightdirectionanopendesignpedometeralgorithmfordogs