Demographic characteristics of the patients enrolled.

<p>Demographic characteristics of the patients enrolled.</p

Example of a decision tree build by the Ranking Forest algorithm.

<p>Example of a decision tree build by the Ranking Forest algorithm.</p

Examples of statokinesigrams from faller and non faller individuals, during the open eyes part of the experiment.

<p>Interestingly, fallers and non fallers do not always have visually distinctive statokinesigrams. A tight faller statokinesigram in a) seems pretty close to the non-faller’s statokinesigram in b). Similarly, a wider non-faller’s statokinesigram in c) seems close to a faller’s statokinesigram in d). In those examples, simple indices alone such as velocity of sway area or acceleration alone would probably fail to discriminate fallers from non fallers.</p

Mean and standard deviation for fallers and non-fallers, AUC, p-value for the Wilcoxon rank-sum test for each of the descriptors.

<p>Mean and standard deviation for fallers and non-fallers, AUC, p-value for the Wilcoxon rank-sum test for each of the descriptors.</p

Mean and standard deviation of the feature importance (Feat. Imp.) as derived from the Ranking Forest algorithm.

<p>Mean and standard deviation of the feature importance (Feat. Imp.) as derived from the Ranking Forest algorithm.</p

ROC curves obtained by the method for different block lengths.

<p>These curves are the average curves from the aformentioned 30 repetitions of the algorithm.</p

On the importance of local dynamics in statokinesigram: A multivariate approach for postural control evaluation in elderly

<div><p>The fact that almost one third of population >65 years-old has at least one fall per year, makes the risk-of-fall assessment through easy-to-use measurements an important issue in current clinical practice. A common way to evaluate posture is through the recording of the center-of-pressure (CoP) displacement (statokinesigram) with force platforms. Most of the previous studies, assuming homogeneous statokinesigrams in quiet standing, used global parameters in order to characterize the statokinesigrams. However the latter analysis provides little information about local characteristics of statokinesigrams. In this study, we propose a multidimensional scoring approach which locally characterizes statokinesigrams on small time-periods, or <i>blocks</i>, while highlighting those which are more indicative to the general individual’s class (faller/non-faller). Moreover, this information can be used to provide a global score in order to evaluate the postural control and classify fallers/non-fallers. We evaluate our approach using the statokinesigram of 126 community-dwelling elderly (78.5 ± 7.7 years). Participants were recorded with eyes open and eyes closed (25 seconds each acquisition) and information about previous falls was collected. The performance of our findings are assessed using the receiver operating characteristics (ROC) analysis and the area under the curve (AUC). The results show that global scores provided by splitting statokinesigrams in smaller blocks and analyzing them locally, classify fallers/non-fallers more effectively (AUC = 0.77 ± 0.09 instead of AUC = 0.63 ± 0.12 for global analysis when splitting is not used). These promising results indicate that such methodology might provide supplementary information about the risk of fall of an individual and be of major usefulness in assessment of balance-related diseases such as Parkinson’s disease.</p></div

Demographic characteristics of the participants.

<p>Fallers are patient who declared at least one fall in the six previous months. No statistically significant difference was found between the two population regarding age, weight, height and body mass index (BMI).</p