Smart home environments are designed to provide services that help improve the quality of life for the occupant via a variety of sensors and actuators installed throughout the space. Many automated actions taken by a smart home are governed by the output of an underlying activity recognition system. However, activity recognition systems may not be perfectly accurate and therefore inconsistencies in smart home operations can lead users reliant on smart home predictions to wonder "why did the smart home do that?" In this work, we build on insights from Explainable Artificial Intelligence (XAI) techniques and introduce an explainable activity recognition framework in which we leverage leading XAI methods to generate natural language explanations that explain what about an activity led to the given classification. Within the context of remote caregiver monitoring, we perform a two-step evaluation: (a) utilize ML experts to assess the sensibility of explanations, and (b) recruit non-experts in two user remote caregiver monitoring scenarios, synchronous and asynchronous, to assess the effectiveness of explanations generated via our framework. Our results show that the XAI approach, SHAP, has a 92% success rate in generating sensible explanations. Moreover, in 83% of sampled scenarios users preferred natural language explanations over a simple activity label, underscoring the need for explainable activity recognition systems. Finally, we show that explanations generated by some XAI methods can lead users to lose confidence in the accuracy of the underlying activity recognition model. We make a recommendation regarding which existing XAI method leads to the best performance in the domain of smart home automation, and discuss a range of topics for future work to further improve explainable activity recognition.

翻译：智能家居环境旨在通过各种安装在空间中的传感器和执行器，提供有助于提高居住者生活质量的服务。智能家居采取的许多自动化操作都受底层活动识别系统输出的指导。然而，活动识别系统可能并非完全准确，因此智能家居操作中的不一致性可能导致依赖智能家居预测的用户产生疑问：“智能家居为什么会这样做？” 在这项工作中，我们基于可解释人工智能（XAI）技术的见解，引入了一个可解释的活动识别框架，在该框架中，我们利用领先的XAI方法生成自然语言解释，阐明活动中的哪些方面导致了给定的分类。在远程照护者监控的背景下，我们进行了两步评估：（a）利用机器学习专家评估解释的合理性，（b）在同步和异步两种远程照护者监控场景中招募非专家用户，评估通过我们的框架生成的解释的有效性。我们的结果表明，XAI方法SHAP在生成合理解释方面的成功率达92%。此外，在83%的采样场景中，用户更倾向于自然语言解释而非简单的活动标签，这凸显了可解释活动识别系统的必要性。最后，我们表明，某些XAI方法生成的解释可能导致用户对底层活动识别模型的准确性失去信心。我们提出了关于现有哪种XAI方法在智能家居自动化领域性能最佳的推荐，并讨论了未来进一步改进可解释活动识别的一系列主题。