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Validation of Foot Placement Locations from Ankle Data of a Kinect v2 Sensor

The Kinect v2 sensor may be a cheap and easy to use sensor to quantify gait in clinical settings, especially when applied in set-ups integrating multiple Kinect sensors to increase the measurement volume. Reliable estimates of foot placement locations are required to quantify spatial gait parameters...

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Autores principales: Geerse, Daphne, Coolen, Bert, Kolijn, Detmar, Roerdink, Melvyn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677405/
https://www.ncbi.nlm.nih.gov/pubmed/28994731
http://dx.doi.org/10.3390/s17102301
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author Geerse, Daphne
Coolen, Bert
Kolijn, Detmar
Roerdink, Melvyn
author_facet Geerse, Daphne
Coolen, Bert
Kolijn, Detmar
Roerdink, Melvyn
author_sort Geerse, Daphne
collection PubMed
description The Kinect v2 sensor may be a cheap and easy to use sensor to quantify gait in clinical settings, especially when applied in set-ups integrating multiple Kinect sensors to increase the measurement volume. Reliable estimates of foot placement locations are required to quantify spatial gait parameters. This study aimed to systematically evaluate the effects of distance from the sensor, side and step length on estimates of foot placement locations based on Kinect’s ankle body points. Subjects (n = 12) performed stepping trials at imposed foot placement locations distanced 2 m or 3 m from the Kinect sensor (distance), for left and right foot placement locations (side), and for five imposed step lengths. Body points’ time series of the lower extremities were recorded with a Kinect v2 sensor, placed frontoparallelly on the left side, and a gold-standard motion-registration system. Foot placement locations, step lengths, and stepping accuracies were compared between systems using repeated-measures ANOVAs, agreement statistics and two one-sided t-tests to test equivalence. For the right side at the 2 m distance from the sensor we found significant between-systems differences in foot placement locations and step lengths, and evidence for nonequivalence. This distance by side effect was likely caused by differences in body orientation relative to the Kinect sensor. It can be reduced by using Kinect’s higher-dimensional depth data to estimate foot placement locations directly from the foot’s point cloud and/or by using smaller inter-sensor distances in the case of a multi-Kinect v2 set-up to estimate foot placement locations at greater distances from the sensor.
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spelling pubmed-56774052017-11-17 Validation of Foot Placement Locations from Ankle Data of a Kinect v2 Sensor Geerse, Daphne Coolen, Bert Kolijn, Detmar Roerdink, Melvyn Sensors (Basel) Article The Kinect v2 sensor may be a cheap and easy to use sensor to quantify gait in clinical settings, especially when applied in set-ups integrating multiple Kinect sensors to increase the measurement volume. Reliable estimates of foot placement locations are required to quantify spatial gait parameters. This study aimed to systematically evaluate the effects of distance from the sensor, side and step length on estimates of foot placement locations based on Kinect’s ankle body points. Subjects (n = 12) performed stepping trials at imposed foot placement locations distanced 2 m or 3 m from the Kinect sensor (distance), for left and right foot placement locations (side), and for five imposed step lengths. Body points’ time series of the lower extremities were recorded with a Kinect v2 sensor, placed frontoparallelly on the left side, and a gold-standard motion-registration system. Foot placement locations, step lengths, and stepping accuracies were compared between systems using repeated-measures ANOVAs, agreement statistics and two one-sided t-tests to test equivalence. For the right side at the 2 m distance from the sensor we found significant between-systems differences in foot placement locations and step lengths, and evidence for nonequivalence. This distance by side effect was likely caused by differences in body orientation relative to the Kinect sensor. It can be reduced by using Kinect’s higher-dimensional depth data to estimate foot placement locations directly from the foot’s point cloud and/or by using smaller inter-sensor distances in the case of a multi-Kinect v2 set-up to estimate foot placement locations at greater distances from the sensor. MDPI 2017-10-10 /pmc/articles/PMC5677405/ /pubmed/28994731 http://dx.doi.org/10.3390/s17102301 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Geerse, Daphne
Coolen, Bert
Kolijn, Detmar
Roerdink, Melvyn
Validation of Foot Placement Locations from Ankle Data of a Kinect v2 Sensor
title Validation of Foot Placement Locations from Ankle Data of a Kinect v2 Sensor
title_full Validation of Foot Placement Locations from Ankle Data of a Kinect v2 Sensor
title_fullStr Validation of Foot Placement Locations from Ankle Data of a Kinect v2 Sensor
title_full_unstemmed Validation of Foot Placement Locations from Ankle Data of a Kinect v2 Sensor
title_short Validation of Foot Placement Locations from Ankle Data of a Kinect v2 Sensor
title_sort validation of foot placement locations from ankle data of a kinect v2 sensor
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677405/
https://www.ncbi.nlm.nih.gov/pubmed/28994731
http://dx.doi.org/10.3390/s17102301
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