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).
Giansanti
Daniele
D
Dipartimento di Tecnologie e Salute, Istituto Superiore di Sanità, Rome, Italy. dgiansa@iss.it
Maccioni
Giovanni
G
Cesinaro
Stefano
S
Benvenuti
Francesco
F
Macellari
Velio
V
D000054:Acceleration; D000058:Accidental Falls / Q000706:statistics & numerical data*; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001185:Artificial Intelligence; D001415:Back / Q000502:physiology; D001696:Biomechanical Phenomena / Q000379:methods; D005069:Evaluation Studies as Topic; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D004856:Postural Balance; D011187:Posture / Q000502:physiology*; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D012399:Rotation; D014159:Transducers