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Gesture-Enabled Remote Control for Healthcare

机译:启用手势的医疗保健的遥控器

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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,穿着可穿戴的传感器的手势识别在医疗保健领域正在增强。它可用于实现医疗器械的远程控制,X射线显示和磁共振成像(MRI)的非接触式导航,并大大提高患者的日常生活能力。但是,即使有一些商业或原型设备可用于可穿戴手势识别,它们都没有提供(1)完全打开API的组合,用于各种医疗保健应用程序开发,(2)适当的表单因子,以舒适的日常磨损,以及(3 )大规模采用的经济实惠的成本。此外,现有的手势识别算法主要用于离散手势。准确识别连续手势仍然是一个重大挑战,这可以防止现有的可穿戴手势识别技术的广泛使用。在本文中,我们介绍了Gemote,一种基于智能腕带的硬件/软件平台,用于手势识别和遥控器。由于其负担能力,小尺寸和舒适的型材,宝石是一种有吸引力的大量消费选择。 Gemote为第三方的第三方研究和应用程序开发提供完整的开放式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,其准确地并自动将手动移动分离成段,并在需要时合并相邻的段,使得每个手势仅存在于一个段中。当用户在不断执行手势时,用户对人类对象的实验表明,当用户在持续执行手势时,识别准确性为99.4 %。

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