Omobot: a low-cost mobile robot for autonomous search and fall detection

TL;DR

This paper presents Omobot, a low-cost mobile robot with advanced features for autonomous house search and fall detection, improving detection accuracy and robot control through novel design and image transformation techniques.

Contribution

The paper introduces a low-cost robot with omnidirectional wheels, deep learning capabilities, and autonomous charging, along with improved fall detection accuracy via image viewpoint transformation and a learned robot controller.

Findings

Fall detection accuracy improved by up to 12%.

Robot features include omnidirectional wheels and autonomous charging.

Enhanced robot control through learned velocity prediction.

Abstract

Detecting falls among the elderly and alerting their community responders can save countless lives. We design and develop a low-cost mobile robot that periodically searches the house for the person being monitored and sends an email to a set of designated responders if a fall is detected. In this project, we make three novel design decisions and contributions. First, our custom-designed low-cost robot has advanced features like omnidirectional wheels, the ability to run deep learning models, and autonomous wireless charging. Second, we improve the accuracy of fall detection for the YOLOv8-Pose-nano object detection network by 6% and YOLOv8-Pose-large by 12%. We do so by transforming the images captured from the robot viewpoint (camera height 0.15m from the ground) to a typical human viewpoint (1.5m above the ground) using a principally computed Homography matrix. This improves network accuracy because the training dataset MS-COCO on which YOLOv8-Pose is trained is captured from a human-height viewpoint. Lastly, we improve the robot controller by learning a model that predicts the robot velocity from the input signal to the motor controller.