AutoLoc: Autonomous Sensor Location Configuration via Cross Modal Sensing

Abstract

Internet-of-Things (IoT) systems have become pervasive for smart homes. In recent years, many of these IoT sensing systems are developed to enable in-home long-term monitoring applications, such as personalized services in smart homes, elderly/patient monitoring, etc. However, these systems often require complicated and expensive installation processes, which are some of the main concerns affecting users' adoption of smart home systems. In this work, we focus on floor vibration-based occupant monitoring systems, which enables non-intrusive in-home continuous occupant monitoring, such as patient step tracking and gait analysis. However, to enable these applications, the system would require known locations of vibration sensors placed in the environment. Current practice relies on manually input of location, which makes the installation labor-intensive, time consuming, and expensive. On the other hand, without known location of vibration sensors, the output of the system does not have intuitive physical meaning and is incomprehensive to users, which limits the systems' usability. We present AutoLoc, a scheme to estimate the location of the vibration sensors in a two-dimensional space in the view of a nearby camera, which has spatial physical meaning. AutoLoc utilizes occupants' walking events captured by both vibration sensors and the co-located camera to estimate the vibration sensors' location in the camera view. First, AutoLoc detects and localizes the occupant's footsteps in the vision data. Then, it associates the time and location of the event to the floor vibration data. Next, the extracted vibration data of the given event from multiple vibration sensors are used to estimate the sensors' locations in the camera view coordinates. We conducted real-world experiments and achieved up to 0.07 meters localization accuracy.