Activity recognition algorithms have matured and become more ubiquitous in recent years. However, these algorithms are typically customized for a particular sensor platform. In this paper, we introduce PECO, a Personalized activity ECOsystem, that transfers learned activity information seamlessly between sensor platforms in real time so that any available sensor can continue to track activities without requiring its own extensive labeled training data. We introduce a multi-view transfer learning algorithm that facilitates this information handoff between sensor platforms and provide theoretical performance bounds for the algorithm. In addition, we empirically evaluate PECO using datasets that utilize heterogeneous sensor platforms to perform activity recognition. These results indicate that not only can activity recognition algorithms transfer important information to new sensor platforms, but any number of platforms can work together as colleagues to boost performance.

中文翻译：

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异构传感器之间的大学活动学习。

近年来，活动识别算法已经成熟并变得越来越普遍。然而，这些算法通常是为特定的传感器平台定制的。在本文中，我们介绍了 PECO，一个个性化的活动生态系统，它可以在传感器平台之间实时无缝地传输学习到的活动信息，以便任何可用的传感器都可以继续跟踪活动，而无需其自己的大量标记训练数据。我们引入了一种多视图迁移学习算法，该算法有助于传感器平台之间的信息切换，并为算法提供理论性能界限。此外，我们使用利用异构传感器平台执行活动识别的数据集对 PECO 进行实证评估。