使用ml密集型相机传感器的DIY智能家居的潜力和挑战

Sojeong Yun, Youn-Kyung Lim
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摘要

传感器和执行器是DIY (do-it-yourself)智能家居系统的关键组件,使用户能够成功构建智能家居功能。此外,机器学习(ML)(例如,ML密集型相机传感器)可以应用于传感器技术,以提高其精度。虽然在家庭中经常使用相机传感器,但使用相机传感器的DIY智能家居系统的用户体验研究仍处于起步阶段。与常用的物联网传感器相比,本研究在使用ml密集型相机传感器构建DIY智能家居功能的同时,调查了新颖的用户体验。因此,我们对12个家庭进行了为期7天的实地日记研究,这些家庭获得了DIY智能家居套件。在这里,我们评估了相机传感器的五个特点,以及在DIY智能家居中使用相机传感器的潜力和挑战,并讨论了解决现有DIY智能家居问题的机会。
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Potential and Challenges of DIY Smart Homes with an ML-intensive Camera Sensor
Sensors and actuators are crucial components of a do-it-yourself (DIY) smart home system that enables users to construct smart home features successfully. In addition, machine learning (ML) (e.g., ML-intensive camera sensors) can be applied to sensor technology to increase its accuracy. Although camera sensors are often utilized in homes, research on user experiences with DIY smart home systems employing camera sensors is still in its infancy. This research investigates novel user experiences while constructing DIY smart home features using an ML-intensive camera sensor in contrast to commonly used IoT sensors. Thus, we conducted a seven-day field diary study with 12 families who were given a DIY smart home kit. Here, we assess the five characteristics of the camera sensor as well as the potential and challenges of utilizing the camera sensor in the DIY smart home and discuss the opportunities to address existing DIY smart home issues.
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