Human posture estimation plays a significant role in the growth of intelligent nursing robot, a field that demands high accuracy and respect for privacy. Nevertheless, traditional approaches to enhancing data-driven studies in this domain often face challenges, primarily due to privacy concerns in sensitive healthcare environments. Federated Learning rises as the solution to the problem, as it not only improves the model learning ability but also protects the data privacy. In this paper, we proposed a Federated Learning Human Posture Recognition (FL-HPR) framework according to image and point cloud fusion. FL-HPR significantly enhances the information flow in the global model while ensuring the data privacy in local models, the benefits highlight the framework’s potential in sensitive applications. Furthermore, the key innovation of FL-HPR is the optimization of the local dynamic graph edge convolution network of robot, which improves the recognition accuracy of individual body limb and enhance overall robustness. Experiments on Non-IID datasets illustrate that the presented FL-HPR remarkably outperforms non-federated learning methods, suggesting its potential to improve the accuracy of human joint estimation. The breakthrough indicates that the proposed FL-HPR can be integrated into the intelligent nursing robot in practical applications. For further explorations, the open-source code and videos are available at .

中文翻译：

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双臂护理机器人人体姿势识别的多传感器融合联邦学习方法

人体姿势估计在智能护理机器人的发展中发挥着重要作用，这是一个要求高精度和尊重隐私的领域。然而，增强该领域数据驱动研究的传统方法经常面临挑战，这主要是由于敏感医疗环境中的隐私问题。联邦学习作为这一问题的解决方案而应运而生，它不仅提高了模型的学习能力，而且还保护了数据隐私。在本文中，我们根据图像和点云融合提出了联邦学习人体姿势识别（FL-HPR）框架。 FL-HPR显着增强了全局模型中的信息流，同时确保了本地模型中的数据隐私，其好处凸显了该框架在敏感应用中的潜力。此外，FL-HPR的关键创新在于对机器人局部动态图边缘卷积网络的优化，提高了个体肢体肢体的识别精度，增强了整体鲁棒性。非独立同分布数据集上的实验表明，所提出的 FL-HPR 显着优于非联邦学习方法，表明其具有提高人类联合估计准确性的潜力。这一突破表明所提出的FL-HPR可以在实际应用中集成到智能护理机器人中。如需进一步探索，可在 处获取开源代码和视频。