Omobot: a low-cost mobile robot for autonomous search and fall detection
Journal:
arXiv
Published Date:
Aug 9, 2024
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.
