Abstract
The identification of hand postures based on force myography (FMG) measurements using a fiber specklegram sensor is reported. The microbending transducers were attached to the user forearm in order to detect the radial forces due to hand movements, and the normalized intensity inner products of output specklegrams were computed with reference to calibration positions. The correlation between measured specklegrams and postures was carried out by artificial neural networks, resulting in an overall accuracy of 91.3% on the retrieval of hand configuration.
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