Advances in embedded systems have made it possible to design wireless sensor networks that are tiny, low-power, wearable and hence suitable for bio-monitoring. This case study proposes a short overview of possible solutions and uses of an inertial-based wireless sensor node called WiMoCA, (Farella et al. 2005), both for use alone or in a body area network to track gestures and movements for different purposes. Thanks to the flexibility of WiMoCA architecture, it was possible to implement a different node along with the ones known as 3dID glove nodes, dedicated to hand movement tracking. The general scenario is one of ambient intelligence where gestures and movements can be used as natural interfaces for humanmachine interaction. Moreover, movement, posture and gait tracking may be the keys to understanding user behaviour and thus enabling seamless provision, in a smart environment, of context-aware services such as domotic applications and remote medical monitoring.

Case Study: Gesture and posture recognition using WSN

Farella, Elisabetta;
2008-01-01

Abstract

Advances in embedded systems have made it possible to design wireless sensor networks that are tiny, low-power, wearable and hence suitable for bio-monitoring. This case study proposes a short overview of possible solutions and uses of an inertial-based wireless sensor node called WiMoCA, (Farella et al. 2005), both for use alone or in a body area network to track gestures and movements for different purposes. Thanks to the flexibility of WiMoCA architecture, it was possible to implement a different node along with the ones known as 3dID glove nodes, dedicated to hand movement tracking. The general scenario is one of ambient intelligence where gestures and movements can be used as natural interfaces for humanmachine interaction. Moreover, movement, posture and gait tracking may be the keys to understanding user behaviour and thus enabling seamless provision, in a smart environment, of context-aware services such as domotic applications and remote medical monitoring.
2008
978-0-470-51294-4
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/215910
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