The problem of a correct fall risk assessment is becoming more and more critical with the ageing of the population. In spite of the available approaches allowing a quantitative analysis of the human movement control system’s performance, the clinical assessment and diagnostic approach to fall risk assessment still relies mostly on non-quantitative exams, such as clinical scales. This work documents our current effort to develop a novel method to assess balance control abilities through a system implementing an automatic evaluation of exercises drawn from balance assessment scales. Our aim is to overcome the classical limits characterizing these scales i.e. limited granularity and inter-/intra-examiner reliability, to obtain objective scores and more detailed information allowing to predict fall risk. We used Microsoft Kinect to record subjects' movements while performing challenging exercises drawn from clinical balance scales. We then computed a set of parameters quantifying the execution of the exercises and fed them to a supervised classifier to perform a classification based on the clinical score. We obtained a good accuracy (~82%) and especially a high sensitivity (~83%).

Affordable, automatic quantitative fall risk assessment based on clinical balance scales and Kinect data

COLAGIORGIO, PAOLO;BEJOR, MAURIZIO;RICEVUTI, GIOVANNI;BUIZZA, ANGELO;RAMAT, STEFANO
2014

Abstract

The problem of a correct fall risk assessment is becoming more and more critical with the ageing of the population. In spite of the available approaches allowing a quantitative analysis of the human movement control system’s performance, the clinical assessment and diagnostic approach to fall risk assessment still relies mostly on non-quantitative exams, such as clinical scales. This work documents our current effort to develop a novel method to assess balance control abilities through a system implementing an automatic evaluation of exercises drawn from balance assessment scales. Our aim is to overcome the classical limits characterizing these scales i.e. limited granularity and inter-/intra-examiner reliability, to obtain objective scores and more detailed information allowing to predict fall risk. We used Microsoft Kinect to record subjects' movements while performing challenging exercises drawn from clinical balance scales. We then computed a set of parameters quantifying the execution of the exercises and fed them to a supervised classifier to perform a classification based on the clinical score. We obtained a good accuracy (~82%) and especially a high sensitivity (~83%).
978-142447929-0
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11571/1095786
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