Zensors: Adaptive, Rapidly Deployable, Human-Intelligent Sensor Feeds
Zensors: Adaptive, Rapidly Deployable, Human-Intelligent Sensor Feeds
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Zensors：自适应、可快速部署、人类智能传感器馈送
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Chris Harrison
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文献类型：
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作者：
Gierad Laput;Walter S. Lasecki;Jason Wiese;R. Xiao;Jeffrey P. Bigham;Chris Harrison
长期以来，人们一直在倡导“智能”家庭、工作场所、学校和其他环境的承诺。然而，布线和安装传感器的成本一直不具吸引力。更关键的是，原始传感器数据往往与人们想要问的问题类型不一致，例如，我需要重新储备我的储藏室吗？虽然像计算机视觉这样的技术可以回答其中的一些问题，但它需要付出巨大的努力来构建和训练合适的分类器。即使这样，这些系统往往也很脆弱，处理新的或意想不到的情况的能力有限，包括重新定位和环境变化(例如，照明、家具、季节)。我们提出了Zensors，这是一种新的感知方法，它融合了来自在线群组工作人员的实时人类智能与自动方法，以提供健壮、自适应和易于部署的智能传感器。有了Zensors，用户可以在不到60秒的时间内从问题到实时传感器馈送。通过我们的API，Zensors可以支持各种丰富的最终用户应用程序，并使我们更接近响应式智能环境的愿景。
The promise of "smart" homes, workplaces, schools, and other environments has long been championed. Unattractive, however, has been the cost to run wires and install sensors. More critically, raw sensor data tends not to align with the types of questions humans wish to ask, e.g., do I need to restock my pantry? Although techniques like computer vision can answer some of these questions, it requires significant effort to build and train appropriate classifiers. Even then, these systems are often brittle, with limited ability to handle new or unexpected situations, including being repositioned and environmental changes (e.g., lighting, furniture, seasons). We propose Zensors, a new sensing approach that fuses real-time human intelligence from online crowd workers with automatic approaches to provide robust, adaptive, and readily deployable intelligent sensors. With Zensors, users can go from question to live sensor feed in less than 60 seconds. Through our API, Zensors can enable a variety of rich end-user applications and moves us closer to the vision of responsive, intelligent environments.