Omobot: a low-cost mobile robot for autonomous search and fall detection
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911983699230720 |
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| author | Ahamad, Shihab Uddin Ataei, Masoud Devabhaktuni, Vijay Dhiman, Vikas |
| author_facet | Ahamad, Shihab Uddin Ataei, Masoud Devabhaktuni, Vijay Dhiman, Vikas |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_05315 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Omobot: a low-cost mobile robot for autonomous search and fall detection Ahamad, Shihab Uddin Ataei, Masoud Devabhaktuni, Vijay Dhiman, Vikas Robotics 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. |
| title | Omobot: a low-cost mobile robot for autonomous search and fall detection |
| topic | Robotics |
| url | https://arxiv.org/abs/2408.05315 |