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

• Published in: PloS one 2018-02, Vol.13 (2), p.e0192868-e0192868
• Main Authors: Bargiotas, Ioannis, Audiffren, Julien, Vayatis, Nicolas, Vidal, Pierre-Paul, Buffat, Stephane, Yelnik, Alain P, Ricard, Damien
• Format: Article
• Language: English
• Subjects: Accidental falls; Analysis; Artificial Intelligence; Balance; Biology and Life Sciences; Classification; Computer and Information Sciences; Computer Science; Diagnosis; Elderly; Falls; Force plates; Geriatrics; Geriatry and gerontology; Health risk assessment; Human health and pathology; Hypotheses; Life Sciences; Mathematics; Medical examination; Medicine and Health Sciences; Methods; Older people; Order parameters; People and Places; Physical Sciences; Physiological aspects; Posture; Principal components analysis; Research and Analysis Methods; Researchers; Risk factors; Signal and Image Processing; Splitting; Statistics
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0192868

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 blocks, 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.