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EnglishClassification of human activity is an increasingly popular topic, as it is employed in various fields from fitness to remote health monitoring. Current automated approaches based on wearable sensors typically use supervised learning methodologies, where a classifier is trained with experimental data. This paper proposes the use of body motion and sensor simulation for building, or extending, the training databases and improve the classifier accuracy, without requiring further experimental campaigns.
Training a classifier for activity recognition using body motion simulation / Grosso, M.; Lena, D.; Rinaudo, S.; Guzman, D. A. F.; Demarchi, D.. - 2018(2018), pp. 1-4. ((Intervento presentato al convegno 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 tenutosi a Politecnico di Torino, ita nel 2017 [10.1109/BIOCAS.2017.8325117].
| Titolo: | Training a classifier for activity recognition using body motion simulation | |
| Autori: | ||
| Data di pubblicazione: | 2018 | |
| Abstract: | Classification of human activity is an increasingly popular topic, as it is employed in various f...ields from fitness to remote health monitoring. Current automated approaches based on wearable sensors typically use supervised learning methodologies, where a classifier is trained with experimental data. This paper proposes the use of body motion and sensor simulation for building, or extending, the training databases and improve the classifier accuracy, without requiring further experimental campaigns. | |
| ISBN: | 978-1-5090-5803-7 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| 08325117.pdf | Guzman - BioCAS17 | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2845677
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