Name
Title | ||
|---|---|---|
Evaluation of the Tinetti score and fall risk assessment via accelerometry-based movement analysis. |
Abstract | ||
|---|---|---|
•The Tinetti score is a clinical scale measuring the risk of fall in the elderly.•Signals from a single wearable accelerometer were used to estimate the Tinetti score.•Nine features were automatically selected and employed in a neural network.•Model automatically assigned the fall risk without the intervention of a practitioner.•The study fosters new technologies to monitor the risk of fall at home. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.artmed.2018.08.005 | Artificial Intelligence in Medicine |
Keywords | Field | DocType |
Fall risk,Tinetti clinical scale,Mobile-health,Healthy ageing,Artificial neural network | Movement analysis,Rehabilitation,Gait,Linear model,Computer science,Accelerometer,Fall risk,Tinetti test,Artificial intelligence,Physical medicine and rehabilitation,Statistical hypothesis testing,Machine learning | Journal |
Volume | ISSN | Citations |
95 | 0933-3657 | 0 |
PageRank | References | Authors |
0.34 | 5 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Massimo W Rivolta | 1 | 0 | 4.73 |
| M Aktaruzzaman | 2 | 13 | 1.90 |
| Giovanna Rizzo | 3 | 9 | 4.32 |
| Claudio L. Lafortuna | 4 | 1 | 1.45 |
| Maurizio Ferrarin | 5 | 1 | 4.50 |
| Gabriele Bovi | 6 | 0 | 0.34 |
| Daniela R Bonardi | 7 | 0 | 0.34 |
| Andrea Caspani | 8 | 0 | 0.34 |
| Roberto Sassi | 9 | 139 | 19.26 |