Omobot: a low-cost mobile robot for autonomous search and fall detection
<|reference_start|>Omobot: a low-cost mobile robot for autonomous search and fall detection: 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.<|reference_end|>
@article{ahamad2024omobot:,
    title={Omobot: a low-cost mobile robot for autonomous search and fall detection},
    author={Shihab Uddin Ahamad, Masoud Ataei, Vijay Devabhaktuni, Vikas Dhiman},
    journal={arXiv preprint arXiv:2408.05315},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05315},
    primaryClass={cs.RO}
}