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Gesture-Enabled Remote Control for Healthcare

机译:支持手势的医疗保健远程控制

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In recent years, wearable sensor-based gesture recognition is proliferating in the field of healthcare. It could be used to enable remote control of medical devices, contactless navigation of X-ray display and Magnetic Resonance Imaging (MRI), and largely enhance patients' daily living capabilities. However, even though a few commercial or prototype devices are available for wearable gesture recognition, none of them provides a combination of (1) fully open API for various healthcare application development, (2) appropriate form factor for comfortable daily wear, and (3) affordable cost for large scale adoption. In addition, the existing gesture recognition algorithms are mainly designed for discrete gestures. Accurate recognition of continuous gestures is still a significant challenge, which prevents the wide usage of existing wearable gesture recognition technology. In this paper, we present Gemote, a smart wristband-based hardware/software platform for gesture recognition and remote control. Due to its affordability, small size, and comfortable profile, Gemote is an attractive option for mass consumption. Gemote provides full open API access for third party research and application development. In addition, it employs a novel continuous gesture segmentation and recognition algorithm, which accurately and automatically separates hand movements into segments, and merges adjacent segments if needed, so that each gesture only exists in one segment. Experiments with human subjects show that the recognition accuracy is 99.4% when users perform gestures discretely, and 94.6% when users perform gestures continuously.
机译:近年来,基于可穿戴传感器的手势识别在医疗保健领域正在迅速发展。它可以用于实现医疗设备的远程控制,X射线显示的非接触式导航和磁共振成像(MRI),并在很大程度上增强患者的日常生活能力。然而,即使有一些商用或原型设备可用于可穿戴手势识别,但它们都没有提供以下组合:(1)用于各种医疗应用开发的完全开放的API;(2)舒适的日常佩戴的合适尺寸;(3) )大规模采用的负担得起的成本。另外,现有的手势识别算法主要设计用于离散手势。连续手势的准确识别仍然是一个重大挑战,这阻碍了现有可穿戴手势识别技术的广泛使用。在本文中,我们介绍了Gemote,这是一个基于智能腕带的硬件/软件平台,用于手势识别和远程控制。由于价格适中,体积小和舒适的外形,Gemote是大众消费的有吸引力的选择。 Gemote为第三方研究和应用程序开发提供了完全开放的API访问。此外,它采用了一种新颖的连续手势分割和识别算法,该算法可以准确自动地将手部运动分离为多个片段,并在需要时合并相邻片段,从而每个手势仅存在于一个片段中。对人体对象的实验表明,用户离散执行手势时的识别准确率为99.4%,而用户连续执行手势时的识别准确率为94.6%。

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