EnglishThe automatic and unobtrusive identification of user’s activities is one of the challenging goals of context-aware computing. This paper discusses and experimentally evaluates instance-based algorithms to infer user’s activities on the basis of data acquired from body-worn accelerometer sensors. We show that instance-based algorithms can classify simple and specific activities with high accuracy. In addition, due to their low requirements, we show how they can be implemented on severely resource-constrained devices. Finally, we propose mechanisms to take advantage of the temporal dimension of the signal, and to identify novel activities at run time.
Detecting Activities from Body-Worn Accelerometers via Instance-based Algorithms / Bicocchi, Nicola; Mamei, Marco; Zambonelli, Franco. - In: PERVASIVE AND MOBILE COMPUTING. - ISSN 1574-1192. - STAMPA. - 6(2010), pp. 482-495.
| Data di pubblicazione: | 2010 |
| Titolo: | Detecting Activities from Body-Worn Accelerometers via Instance-based Algorithms |
| Autore/i: | Bicocchi, Nicola; Mamei, Marco; Zambonelli, Franco |
| Autore/i UNIMORE: | |
| Rivista: | |
| Volume: | 6 |
| Pagina iniziale: | 482 |
| Pagina finale: | 495 |
| Codice identificativo ISI: | WOS:000208192900007 |
| Codice identificativo Scopus: | 2-s2.0-77956411876 |
| Citazione: | Detecting Activities from Body-Worn Accelerometers via Instance-based Algorithms / Bicocchi, Nicola; Mamei, Marco; Zambonelli, Franco. - In: PERVASIVE AND MOBILE COMPUTING. - ISSN 1574-1192. - STAMPA. - 6(2010), pp. 482-495. |
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http://hdl.handle.net/11380/643710
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