E-Gesture: a collaborative architecture for energy-efficient gesture recognition with hand-worn sensor and mobile devices
E-Gesture: a collaborative architecture for energy-efficient gesture recognition with hand-worn sensor and mobile devices
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DOI：
10.1145/2070942.2070969
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发表时间：
2011-11
期刊：
影响因子：
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通讯作者：
Taiwoo Park;Jinwon Lee;Inseok Hwang;Chungkuk Yoo;L. Nachman;Junehwa Song
中科院分区：
文献类型：
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作者：
Taiwoo Park;Jinwon Lee;Inseok Hwang;Chungkuk Yoo;L. Nachman;Junehwa Song
手势是一种很有前途的移动用户界面方式，它可以在不停止或阻碍移动的情况下实现无眼交互。在本文中，我们介绍了E-Gesture的设计、实现和评估，E-Gesture是一种使用手持传感器设备和智能手机的节能手势识别系统。E-gesture采用了一种新颖的手势识别架构，通过研究连续传感器数据流中手势的零星发生模式，并分析传感器和智能手机的能耗特征，精心设计。我们开发了一种闭环协同分割架构，它可以(1)在资源稀缺的传感器设备中实现，(2)在不影响识别精度的情况下自适应关闭耗电的运动传感器，(3)减少由于身体运动的动态变化而产生的错误分割。我们还为智能手机开发了一个移动手势分类架构，使基于hmm的分类模型能够更好地适应多种移动情况。
Gesture is a promising mobile User Interface modality that enables eyes-free interaction without stopping or impeding movement. In this paper, we present the design, implementation, and evaluation of E-Gesture, an energy-efficient gesture recognition system using a hand-worn sensor device and a smartphone. E-gesture employs a novel gesture recognition architecture carefully crafted by studying sporadic occurrence patterns of gestures in continuous sensor data streams and analyzing the energy consumption characteristics of both sensors and smartphones. We developed a closed-loop collaborative segmentation architecture, that can (1) be implemented in resource-scarce sensor devices, (2) adaptively turn off power-hungry motion sensors without compromising recognition accuracy, and (3) reduce false segmentations generated from dynamic changes of body movement. We also developed a mobile gesture classification architecture for smartphones that enables HMM-based classification models to better fit multiple mobility situations.