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EnglishIn this paper an arm rehabilitation scenario was set-up to estimate and evaluate the principal activities of this limb by the suggested WBAN-based approach. A couple of wearable sensors mounted on the arm and a fixed node constitute the wireless network. In order to identify and classify the rehabilitation activities an algorithm based on the Received Signal Strength Indicator (RSSI), a parameter already available in the wireless sensor nodes, was applied. As a first attempt, a support vector machine (SVM) learning technique was implemented.
| Titolo: | Detection and Classification of Human Arm Movements for Physical Rehabilitation | |
| Autori interni: | ||
| Anno del prodotto: | 2010 | |
| Abstract: | In this paper an arm rehabilitation scenario was set-up to estimate and evaluate the principal activities of this limb by the suggested WBAN-based approach. A couple of wearable sensors mounted on the arm and a fixed node constitute the wireless network. In order to identify and classify the rehabilitation activities an algorithm based on the Received Signal Strength Indicator (RSSI), a parameter already available in the wireless sensor nodes, was applied. As a first attempt, a support vector machine (SVM) learning technique was implemented. | |
| Handle: | http://hdl.handle.net/11568/198195 | |
| ISBN: | 9781424449682 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
http://hdl.handle.net/11568/198195
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