Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Koganei, Tokyo 184-8588, Japan.
Sensors (Basel). 2019 Feb 9;19(3):710. doi: 10.3390/s19030710.
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.
一种人体姿态预测系统可用于在实际动作之前预测人体姿态，从而减少交互系统中的延迟。手势对于人机交互来说尤为必要。因此，姿态预测系统必须能够捕捉到既复杂又快速的手部动作。我们已经报道了一种使用热解石墨片（PGS）以简单且容易的方式制造应变传感器和可穿戴设备的方法。可穿戴电子设备可以用高灵敏度、高耐久性和快速响应来检测各种类型的人体姿态。在这项研究中，我们使用基于 PGS 的数据手套获取的姿态数据，通过人工神经网络（ANNs）演示了手势预测。我们的实验包括测量主体的手势以供学习使用，并且我们使用这些数据创建了四层的 ANNs，从而使提出的系统能够成功实时预测手势。通过对基于时间的数据进行分析，将提出的方法与其他算法进行比较，结果表明，使用基于 PGS 的可穿戴设备获得的电阻数据，基于 ANNs 的手势预测系统将能够预测各种类型的手势。
