Name
Abstract | ||
|---|---|---|
In this paper we propose a novel energy efficient approach for the recognition of human activities using smartphones as wearable sensing devices, targeting assisted living applications such as remote patient activity monitoring for the disabled and the elderly. The method exploits fixed-point arithmetic to propose a modified multiclass Support Vector Machine (SVM) learning algorithm, allowing to better preserve the smartphone battery lifetime with respect to the conventional floating-point based formulation while maintaining comparable system accuracy levels. Experiments show comparative results between this approach and the traditional SVM in terms of recognition performance and battery consumption, highlighting the advantages of the proposed method. |
Year | Venue | Keywords |
|---|---|---|
2013 | JOURNAL OF UNIVERSAL COMPUTER SCIENCE | Activity Recognition,Remote Monitoring,SVM,Smartphones,Energy Efficiency,Fixed-Point Arithmetic,Assisted Healthcare |
Field | DocType | Volume |
Data mining,Activity recognition,Fixed-point arithmetic,Computer science,Efficient energy use,Support vector machine,Exploit,Artificial intelligence,Computer hardware,Battery (electricity),Machine learning,Wearable sensing | Journal | 19 |
Issue | ISSN | Citations |
9 | 0948-695X | 37 |
PageRank | References | Authors |
1.12 | 39 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Davide Anguita | 1 | 1001 | 70.58 |
| Alessandro Ghio | 2 | 667 | 35.71 |
| Luca Oneto | 3 | 830 | 63.22 |
| Xavier Parra | 4 | 529 | 23.12 |
| Jorge Luis Reyes-Ortiz | 5 | 329 | 11.66 |