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.

翻译：检测老年人跌倒并通知其社区响应人员能够挽救无数生命。我们设计并开发了一款低成本移动机器人，可定期在住宅内搜寻被监测人员，并在检测到跌倒时向指定响应人员发送电子邮件。本项目提出了三项创新性设计决策与贡献。首先，我们自主设计的低成本机器人具备全向轮、运行深度学习模型及自主无线充电等先进功能。其次，通过使用原理计算的单应性矩阵将机器人视角（相机距地面0.15米）采集的图像转换为典型人类视角（距地面1.5米），我们将YOLOv8-Pose-nano目标检测网络的跌倒检测准确率提升6%，YOLOv8-Pose-large提升12%。该改进源于YOLOv8-Pose训练所用的MS-COCO数据集采集自人类身高视角。最后，我们通过学习从电机控制器输入信号预测机器人速度的模型，改进了机器人控制器。