A public data set of human balance evaluations

Abstract

The goal of this study was to create a public data set with results of qualitative and quantitative evaluations related to human balance. Subject's balance was evaluated by posturography using a force platform and by the Mini Balance Evaluation Systems Tests. In the posturography test, we evaluated subjects during standing still for 60 s in four different conditions where vision and the standing surface were manipulated: on a rigid surface with eyes open; on a rigid surface with eyes closed; on an unstable surface with eyes open; on an unstable surface with eyes closed. Each condition was performed three times and the order of the conditions was randomized among subjects. In addition, the following tests were employed in order to better characterize each subject: Short Falls Efficacy Scale International; International Physical Activity Questionnaire Short Version; and Trail Making Test. The subjects were also interviewed to collect information about their socio-cultural, demographic, and health characteristics. The data set comprises signals from the force platform (raw data for the force, moments of forces, and centers of pressure) of 163 subjects plus one file with information about the subjects and balance conditions and the results of the other evaluations.

Age-Related Changes in Centre of Pressure Trajectories Analysed with a Novel ‘Return to Central’ Analysis

“…This study uses a previously recorded dataset, in which COP data is collected of 163 participants (Santos & Duarte, 2016a, 2016b, published open access on physionet.org (Goldberger et al, 2000). Ethical approval for collating this dataset was provided by the local ethics committee of the Federal University of ABC (#842529/ 2014) (Santos & Duarte, 2016a, 2016b. The dataset contains information from 116 females and 47 males, with an age between 18 and 86 years.…”

“…The maximum score is 28 points and a score lower than 19.5 indicates an elevated fall risk (Marques et al, 2016). For the full protocol, please see (Santos & Duarte, 2016b).…”

“…Data used in this study is available through the publicly available Human Balance Evaluation Database (Santos & Duarte, 2016a, 2016b; https://doi.org/10. 13026/C2WW2W) which is made available under the ODC Attribution Licence (https://physionet.org/content/ hbedb/view-licence/1.0.0/).…”

Feature selection for the classification of fall-risk in older subjects: a combinational approach using static force-plate measures

“…The aim of this study is to demonstrate an effective feature selection procedure that can allow for more accurate and reliable classification of fall-risk among older subjects using static force-platform measures only. This study will avail of an opensource dataset (https://peerj.com/articles/2648) which has been published specifically for use in such studies (31). A combination of Support vector machines-Recursive feature elimination (SVM-RFE), a Genetic algorithm (GA) and ReliefF feature selection methods will be utilized to find the most relevant, least redundant subset and Singha & Shenoy's (6) self-evaluating feature evaluation (SAFE) heuristic will optimize complementarity downstream from this.…”

“…The performance of the final feature subset will then be tested using a Multi-layer perceptron (MLP), Support vector machines (SVM), Naïve Bayes (NB) and K-Nearest neighbours (K-NN). A full description of the data acquisition protocol can be found at (31). Only the most relevant aspects to this study will be covered here.…”