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引用次数: 0

摘要

智能便携式和可穿戴设备因其“随身携带和使用”的能力在我们的生活中越来越受欢迎。然而,为了收集数据并对用户的数据进行即时评估,它们需要重量轻,尺寸紧凑,具有多个传感器和更高的处理能力。边缘计算为可穿戴设备提供了在不违反重量、尺寸和传感能力限制的情况下访问更多资源的机会。此外,边缘计算(包括TinyML)提供了许多必要的设备上处理功能,可以帮助保护用户的私人数据,因为原始个人数据(如图像和视频)不需要远程共享。在这次演讲中,我将着眼于边缘计算在保护用户隐私的同时增强可穿戴和手持设备的潜力,我将展示我们最近在智能传感实验室工作的几个例子,包括用于心理健康的小立方体,用于预防犯罪的边缘和便携式设备以及用于基于位置的游戏和健康的边缘设备。我还将介绍一些令人兴奋的未来方向,这些方向有望对用户手中的边缘计算产生深远的影响。
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Edge Computing in the Hands of Users
Smart portable and wearable devices have become more and more popular in our lives due to their ability to "Wear and Use On-the-Go". However, in order to collect data and perform momentarily assessment of users' data, they require to be light weight, compact size with multiple sensors and higher processing capabilities. Edge computing provides an opportunity for wearable devices to access more resources without violating the constraints on weight, size, and sensing capabilities. Furthermore, edge computing (including TinyML) provides many required on-device processing capabilities which can then help in protecting users' private data as raw personal data (such as images and videos) don't need to be shared remotely. In this talk, I will look at the potential of edge computing to empower wearable and handheld devices while protecting users' privacy and I will showcase several examples of our recent work at the Smart Sensing lab including fidgeting cubes for mental health, edge and portable devices for Crime prevention and edge gadgets for location-based gaming and wellbeing. I will also provide a glimpse into exciting future directions that promise to have a profound impact on the Edge-Computing in the hands of users.
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