A human gesture prediction system can be used to estimate human gestures in advance of\nthe actual action to reduce delays in interactive systems. Hand gestures are particularly necessary for\nhumanâ??computer interaction. Therefore, the gesture prediction system must be able to capture hand\nmovements that are both complex and quick. We have already reported a method that allows strain\nsensors and wearable devices to be fabricated in a simple and easy manner using pyrolytic graphite\nsheets (PGSs). The wearable electronics could detect various types of human gestures with high\nsensitivity, high durability, and fast response. In this study, we demonstrated hand gesture prediction\nby artificial neural networks (ANNs) using gesture data obtained from data gloves based on PGSs.\nOur experiments entailed measuring the hand gestures of subjects for learning purposes and we\nused these data to create four-layered ANNs, which enabled the proposed system to successfully\npredict hand gestures in real time. A comparison of the proposed method with other algorithms using\ntemporal data analysis suggested that the hand gesture prediction system using ANNs would be\nable to forecast various types of hand gestures using resistance data obtained from wearable devices\nbased on PGSs.
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