Nowadays, the advancement of sensing and communication technologies has led to the possibility of collecting a large amount of sensor data, however, to build a reliable computational model and accurately recognise human activities we still need the annotations on sensor data. Acquiring high-quality, detailed, continuous annotations is a challenging task. In this paper, we explore the solution space on sharing annotated activities across different datasets in order to enhance the recognition accuracies. The main challenge is to resolve heterogeneity in feature and activity space between datasets; that is, each dataset can have a different number of sensors in heterogeneous sensing technologies and deployed in diverse environments and record various activities on different users. To address the challenge, we have designed and developed sharing data and sharing classifiers algorithms that feature the knowledge model to enable computationally-efficient feature space remapping and uncertainty reasoning to enable effective classifier fusion. We have validated the algorithms on three third-party real-world datasets and demonstrated their effectiveness in recognising activities only with annotations from as little as 0.1% of each dataset.

中文翻译：

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跨异构数据集共享学习活动标签

如今，传感和通信技术的进步已导致收集大量传感器数据的可能性，但是，要建立可靠的计算模型并准确识别人类活动，我们仍然需要在传感器数据上添加注释。获取高质量，详细，连续的注释是一项艰巨的任务。在本文中，我们探索了在不同数据集之间共享带注释的活动的解决方案空间，以增强识别的准确性。主要挑战是解决数据集之间特征和活动空间的异质性。也就是说，每个数据集在异构传感技术中可以具有不同数量的传感器，并且可以部署在不同的环境中，并在不同的用户上记录各种活动。为了应对挑战，我们设计和开发了具有知识模型的共享数据和共享分类器算法，以实现计算效率高的特征空间重映射和不确定性推理，从而实现有效的分类器融合。我们已经在三个第三方真实世界数据集上验证了该算法，并展示了它们仅通过来自每个数据集的0.1％的注释来识别活动的有效性。