Falling in elderly is a worldwide major problem because it can lead to severe injuries, and even sudden death. Fall risk prediction would provide rapid intervention, as well as reducing the over burden of healthcare systems. Such prediction is currently performed by means of clinical scales. Among them, the Tinetti Scale is one of the better established and mostly used in clinical practice. In this work, we proposed an automatic method to assess the Tinetti scores using a wearable accelerometer. The balance and gait characteristics of 13 elderly subjects have been scored by an expert clinician while performing 8 different motor tasks according to the Tinetti Scale protocol. Two statistical analysis were selected. First, a linear regression study was performed between the Tinetti scores and 8 features (one feature for each task). Second, the generalization quality of the regression model was assessed using a Leave-One SubjectOut approach. The multiple linear regression provided a high correlation between the Tinetti scores and the features proposed (adj. R(2) = 0.948; p = 0.003). Moreover, six of the eight features added statistically significantly to the prediction of the scores (p <; 0.05). When testing the generalization capability of the model, a moderate linear correlation was obtained (R(2) = 0.67; p <; 0.05). The results suggested that the automatic method might be a promising tool to assess the falling risk of older individuals.

Automatic vs. clinical assessment of fall risk in older individuals: a proof of concept / M.W. Rivolta, M. Aktaruzzaman, G. Rizzo, C.L. Lafortuna, M. Ferrarin, G. Bovi, D.R. Bonardi, R. Sassi (PROCEEDINGS OF THE ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY). - In: Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE[s.l] : IEEE, 2015 Aug. - ISBN 9781424492718. - pp. 6935-6938 (( Intervento presentato al 37. convegno EMBC tenutosi a Milano nel 2015.

Automatic vs. clinical assessment of fall risk in older individuals: a proof of concept

M.W. Rivolta
Primo
;
M. Aktaruzzaman
Secondo
;
M. Ferrarin;D.R. Bonardi
Penultimo
;
R. Sassi
Ultimo
2015-08

Abstract

Falling in elderly is a worldwide major problem because it can lead to severe injuries, and even sudden death. Fall risk prediction would provide rapid intervention, as well as reducing the over burden of healthcare systems. Such prediction is currently performed by means of clinical scales. Among them, the Tinetti Scale is one of the better established and mostly used in clinical practice. In this work, we proposed an automatic method to assess the Tinetti scores using a wearable accelerometer. The balance and gait characteristics of 13 elderly subjects have been scored by an expert clinician while performing 8 different motor tasks according to the Tinetti Scale protocol. Two statistical analysis were selected. First, a linear regression study was performed between the Tinetti scores and 8 features (one feature for each task). Second, the generalization quality of the regression model was assessed using a Leave-One SubjectOut approach. The multiple linear regression provided a high correlation between the Tinetti scores and the features proposed (adj. R(2) = 0.948; p = 0.003). Moreover, six of the eight features added statistically significantly to the prediction of the scores (p <; 0.05). When testing the generalization capability of the model, a moderate linear correlation was obtained (R(2) = 0.67; p <; 0.05). The results suggested that the automatic method might be a promising tool to assess the falling risk of older individuals.
Settore INF/01 - Informatica
Settore ING-INF/06 - Bioingegneria Elettronica e Informatica
IEEE
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/356207
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