title: A Non Linear Scoring Approach for Evaluating Balance: Classification of Elderly as Fallers and Non-Fallers.
abstract: Almost one third of population 65 years-old and older faces at least one fall per year. An accurate evaluation of the risk of fall through simple and easy-to-use measurements is an important issue in current clinic. A common way to evaluate balance in posturography is through the recording of the centre-of-pressure (CoP) displacement (statokinesigram) with force platforms. A variety of indices have been proposed to differentiate fallers from non fallers. However, no agreement has been reached whether these analyses alone can explain sufficiently the complex synergies of postural control. In this work, we study the statokinesigrams of 84 elderly subjects (80.3+- 6.4 years old), which had no impairment related to balance control. Each subject was recorded 25 seconds with eyes open and 25 seconds with eyes closed and information pertaining to the presence of problems of balance, such as fall, in the last six months, was collected. Five descriptors of the statokinesigrams were computed for each record, and a Ranking Forest algorithm was used to combine those features in order to evaluate each subject's balance with a score. A classical train-test split approach was used to evaluate the performance of the method through ROC analysis. ROC analysis showed that the performance of each descriptor separately was close to a random classifier (AUC between 0.49 and 0.54). On the other hand, the score obtained by our method reached an AUC of 0.75 on the test set, consistent over multiple train-test split. This non linear multi-dimensional approach seems appropriate in evaluating complex postural control.
authors.affiliation: CMLA, ENS Cachan, CNRS, Université Paris-Saclay, Cachan, France.|COGNAG-G UMR 8257 CNRS, SSA, Université Paris Descartes, Paris, France.
authors.forename: Julien
authors.initials: J
authors.lastname: Audiffren
authors.affiliation: CMLA, ENS Cachan, CNRS, Université Paris-Saclay, Cachan, France.|COGNAG-G UMR 8257 CNRS, SSA, Université Paris Descartes, Paris, France.
authors.forename: Ioannis
authors.initials: I
authors.lastname: Bargiotas
authors.affiliation: CMLA, ENS Cachan, CNRS, Université Paris-Saclay, Cachan, France.|COGNAG-G UMR 8257 CNRS, SSA, Université Paris Descartes, Paris, France.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Vayatis
authors.affiliation: COGNAG-G UMR 8257 CNRS, SSA, Université Paris Descartes, Paris, France.
authors.forename: Pierre-Paul
authors.initials: PP
authors.lastname: Vidal
authors.affiliation: COGNAG-G UMR 8257 CNRS, SSA, Université Paris Descartes, Paris, France.|Service de neurologie, HIA Percy, Service de Santé des Armées, Clamart, France.|Ecole du Val-de-Grâce, Service de Santé des Armées, Paris, France.
authors.forename: Damien
authors.initials: D
authors.lastname: Ricard
mesh_terms: D000058:Accidental Falls / Q000517:prevention & control* / Q000706:statistics & numerical data*; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D005602:France; D015577:Geriatric Assessment / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008962:Models, Theoretical; D004856:Postural Balance / Q000502:physiology*; D011187:Posture / Q000502:physiology; D012372:ROC Curve; D012307:Risk Factors; D011795:Surveys and Questionnaires