Abstract
A human gesture prediction system can be used to estimate human gestures in advance of the actual action to reduce delays in interactive systems. Hand gestures are particularly necessary for human–computer interaction. Therefore, the gesture prediction system must be able to capture hand movements that are both complex and quick. We have already reported a method that allows strain sensors and wearable devices to be fabricated in a simple and easy manner using pyrolytic graphite sheets (PGSs). The wearable electronics could detect various types of human gestures with high sensitivity, high durability, and fast response. In this study, we demonstrated hand gesture prediction by artificial neural networks (ANNs) using gesture data obtained from data gloves based on PGSs. Our experiments entailed measuring the hand gestures of subjects for learning purposes and we used these data to create four-layered ANNs, which enabled the proposed system to successfully predict hand gestures in real time. A comparison of the proposed method with other algorithms using temporal data analysis suggested that the hand gesture prediction system using ANNs would be able to forecast various types of hand gestures using resistance data obtained from wearable devices based on PGSs.
Highlights
The recognition of human gestures with the aid of wearable devices has found widespread application in modern manufacturing systems [1,2] and smart assistive technologies [3,4,5,6]
Time delay neural networks (TDNNs) and recurrent neural networks (RNNs), both of which were proven capable of reliably predicting the future trend of a time series, can be used to solve these problems [16,17,18,19]
This report presents the system we developed to demonstrate hand gesture prediction by time delay neural networks (TDNNs) using hand gesture data obtained from data gloves with pyrolytic graphite sheets (PGSs)
Summary
The recognition of human gestures with the aid of wearable devices has found widespread application in modern manufacturing systems [1,2] and smart assistive technologies [3,4,5,6] For these applications, it is desirable to detect a human gesture as early as possible [7], to make the human–computer interaction more natural, e.g., deploying a robot to help an elderly person stand up before he/she is upright and is at risk of falling. We previously reported a simple and facile method to fabricate strain sensors and wearable devices using pyrolytic graphite sheets (PGSs) [20] These PGS sheets are flexible, synthetically produced, uniform, highly oriented, and inexpensive [21]. The accuracy of the prediction model based on ANNs was verified by comparing its performance with that of a multiple linear regression (MLR) model
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