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AutoLoc: Autonomous Sensor Location Configuration via Cross Modal Sensing.
Shubham Rohal1, Yue Zhang1, Carlos Ruiz2
1Computer Science and Engineering, University of California, Merced, Merced, CA, United States.
Frontiers in Big Data
|April 14, 2022
Summary
AutoLoc automatically estimates vibration sensor locations in smart homes using walking events captured by cameras and sensors. This simplifies installation and improves occupant monitoring systems for applications like gait analysis.
Area of Science:
- Computer Science
- Ubiquitous Computing
- Sensor Networks
Background:
- Internet-of-Things (IoT) systems are common in smart homes for monitoring.
- Current IoT sensing systems require complex and costly installation.
- Floor vibration sensors enable non-intrusive occupant monitoring but need known locations.
Purpose of the Study:
- To develop an automated method for locating vibration sensors in smart home environments.
- To address the challenges of manual sensor placement, which is labor-intensive and expensive.
- To enhance the usability and physical interpretability of vibration-based monitoring systems.
Main Methods:
- The AutoLoc scheme estimates sensor locations in a 2D space using a nearby camera.
- It detects and localizes occupant footsteps in video data.
- It associates footstep events with vibration sensor data to estimate sensor positions.
Main Results:
- AutoLoc successfully estimates the locations of vibration sensors.
- Real-world experiments achieved a localization accuracy of up to 0.07 meters.
- The system provides spatially meaningful and comprehensible sensor data.
Conclusions:
- AutoLoc offers an automated and accurate solution for vibration sensor localization.
- This method reduces installation costs and complexity for smart home monitoring.
- Improved sensor localization enhances the practical application of occupant monitoring systems.
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