Assessment of fall-risk by means of a neural network based on parameters assessed by a wearable device during posturography.

We have investigated the use of an Artificial Neural Network (ANN) for the assessment of fall-risk (FR) in patients with different neural pathologies. The assessment integrates a clinical tool based on a wearable device (WD) with accelerometers (ACCs) and rate gyroscopes (GYROs) properly suited to identify trunk kinematic parameters that can be measured during a posturography test with different constraints. Our ANN--a Multi Layer Perceptron Neural Network with four layers and 272 neurones--shows to be able to classify patients in three well-known fall-risk levels. The training of the neural network was carried on three groups of 30 subjects with different Fall-Risk Tinetti scores. The validation of our neural network was carried out on three groups of 100 subjects with different Fall-Risk Tinetti scores and this validation demonstrated that the neural network had high specificity (> or =0.88); sensitivity (> or =0.87); area under Receiver-Operator Characteristic Curves (>0.854).