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Citation |
Rivolta MW, Aktaruzzaman M, Rizzo G, Lafortuna CL, Ferrarin M, Bovi G, Bonardi DR, Caspani A, Sassi R. Artif. Intell. Med. 2019; 95: 38-47. |
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Affiliation |
Dipartimento di Informatica, Università degli Studi di Milano, Crema (CR) 26013, Italy. Electronic address: roberto.sassi@unimi.it. |
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Copyright |
(Copyright © 2019, Elsevier Publishing) |
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DOI |
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PMID |
30195985 |
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Abstract |
Gait and balance disorders are among the main predisposing factors of falls in elderly. Clinical scales are widely employed to assess the risk of falling, but they require trained personnel. We investigate the use of objective measures obtained from a wearable accelerometer to evaluate the fall risk, determined by the Tinetti clinical scale. Seventy-nine patients and eleven volunteers were enrolled in two rehabilitation centers and underwent a full Tinetti test, while wearing a triaxial accelerometer at the chest. Tinetti scores were assessed by expert physicians and those subjects with a score ≤18 were considered at high risk. First, we analyzed 21 accelerometer features by means of statistical tests and correlation analysis. Second, one regression and one classification problem were designed and solved using a linear model (LM) and an artificial neural network (ANN) to predict the Tinetti outcome. Pearson's correlation between the Tinetti score and a subset of 9 features (mainly related with standing and walking) was 0.71. The misclassification error of high risk patient was 0.21 and 0.11, for LM and ANN, respectively. The work might foster the development of a new generation of applications meant to monitor the time evolution of the fall risk using low cost devices at home. Language: en |
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Keywords |
Artificial neural network; Fall risk; Healthy ageing; Mobile-health; Tinetti clinical scale |