Potential and Challenges of DIY Smart Homes with an ML-intensive Camera Sensor

Abstract

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.