RESUMO
The goal of this work is to present a novel continuous finger gesture recognition system based on flex sensors. The system is able to carry out accurate recognition of a sequence of gestures. Wireless smart gloves equipped with flex sensors were implemented for the collection of the training and testing sets. Given the sensory data acquired from the smart gloves, the gated recurrent unit (GRU) algorithm was then adopted for gesture spotting. During the training process for the GRU, the movements associated with different fingers and the transitions between two successive gestures were taken into consideration. On the basis of the gesture spotting results, the maximum a posteriori (MAP) estimation was carried out for the final gesture classification. Because of the effectiveness of the proposed spotting scheme, accurate gesture recognition was achieved even for complicated transitions between successive gestures. From the experimental results, it can be observed that the proposed system is an effective alternative for robust recognition of a sequence of finger gestures.
Assuntos
Dedos/fisiologia , Gestos , Monitorização Fisiológica/instrumentação , Tecnologia sem Fio , Algoritmos , HumanosRESUMO
Fifteen ferret badgers (Melogale moschata subaurantiaca), collected 2010-13 and stored frozen, were submitted for rabies diagnosis by direct fluorescent antibody test and reverse transcription PCR. We detected seven positive animal samples, including some from 2010, which indicated that the ferret badger population in Taiwan had been affected by rabies prior to 2010.