Name
Abstract | ||
|---|---|---|
A relevant aspect in the field of health monitoring is represented by the evaluation of balance stability in the elderly. The Berg Balance Scale (BBS) represents a golden standard test for clinical assessment of balance stability. Recently, the Wii Balance Board has been successfully validated as an effective tool for the analysis of static balance-related features such as the duration or the speed of assessment of patient's center of pressure. In this paper we propose an innovative unobtrusive approach for automatic evaluation of balance assessment, by analyzing the whole temporal information generated by the balance board. In particular, using Recurrent Neural Networks implemented according to the Reservoir Computing paradigm, we propose to estimate the BBS score of a patient from the temporal data gathered during the execution on the balance board of one simple BBS exercise. The experimental assessment of the proposed approach on real-world data shows promising results. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-44412-3_5 | ADVANCED ANALYSIS AND LEARNING ON TEMPORAL DATA, AALTD 2015 |
Keywords | Field | DocType |
Reservoir computing,Echo state network,Learning with temporal data,Balance assessment | Data mining,Computer science,Recurrent neural network,Temporal database,Balance board,Center of pressure (fluid mechanics),Reservoir computing,Artificial intelligence,Echo state network,Berg Balance Scale,Machine learning | Conference |
Volume | ISSN | Citations |
9785 | 0302-9743 | 2 |
PageRank | References | Authors |
0.37 | 13 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Claudio Gallicchio | 1 | 336 | 32.18 |
| Alessio Micheli | 2 | 713 | 60.24 |
| Luca Pedrelli | 3 | 7 | 0.88 |
| Luigi Fortunati | 4 | 13 | 1.63 |
| F Vozzi | 5 | 26 | 6.84 |
| Oberdan Parodi | 6 | 53 | 13.16 |