how to validate the reliability and accuracy of information

He performed this work without any financial compensation or any other benefits. The specific role of this author is articulated in the ‘author contributions’ section.Ĭompeting interests: The authors have the following competing interests: François Rioux is employed by Frisoft Inc. provided support in the form of a salary for author FR, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedĭata Availability: Authors may be contacted at The authors received no specific funding for this work. Received: Accepted: ApPublished: June 8, 2015Ĭopyright: © 2015 Riou et al. PLoS ONE 10(6):Īcademic Editor: François Hug, The University of Queensland, AUSTRALIA

The results obtained from the classification model were accurate and reproducible, and highlight the potential use of this approach in research to quantify the time spent performing different activities.Ĭitation: Riou M-È, Rioux F, Lamothe G, Doucet É (2015) Validation and Reliability of a Classification Method to Measure the Time Spent Performing Different Activities. Results were highly reproducible in both environments. No significant differences were noted between performances obtained with six or eight features.

The sensitivity in CE and UE was 94% and 95% for lying down, 88% and 80% for dynamic standing, 97% and 89% for sitting, 96% and 78% for walking and 90% and 64% for running, respectively. Results revealed an overall accuracy of 94% in CE and 90% in UE. The reliability of the classification model was tested in both environments using six features. The performance of the classification model was validated against triaxial accelerometers using six (x, y and step counts for arm and thigh) or eight (same as six features plus z axis) features. A classification model trained with activities performed in a specific order during the first day was developed to validate the activities performed in a random order on the second day (CE) and over 24 hours on a separate day (UE).

In CE, participants wore a pair of biaxial and/or triaxial accelerometers while performing pre-determined training activities classified as variants of lying down, dynamic standing, sitting, walking and running on two separate days. The aim of this study was to validate the performance and reliability of results obtained from a classification model that measures time spent performing activities in confined (CE) and unrestricted (UE) environments.

How to validate the reliability and accuracy of information