Recent progress in Internet of Things (IoT) platforms has allowed us to collect large amounts of sensing data. However, there are significant challenges in converting this large-scale sensing data into decisions for real-world applications. Motivated by applications like health monitoring and intervention and home automation we consider a novel problem called Activity Prediction, where the goal is to predict future activity occurrence times from sensor data. In this paper, we make three main contributions. First, we formulate and solve the activity prediction problem in the framework of imitation learning and reduce it to a simple regression learning problem. This approach allows us to leverage powerful regression learners that can reason about the relational structure of the problem with negligible computational overhead. Second, we present several metrics to evaluate activity predictors in the context of real-world applications. Third, we evaluate our approach using real sensor data collected from 24 smart home testbeds. We also embed the learned predictor into a mobile-device-based activity prompter and evaluate the app for nine participants living in smart homes. Our results indicate that our activity predictor performs better than the baseline methods, and offers a simple approach for predicting activities from sensor data.

中文翻译：

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从传感器数据中学习活动预测器：算法、评估和应用

物联网 (IoT) 平台的最新进展使我们能够收集大量传感数据。然而，将这种大规模传感数据转化为实际应用的决策存在重大挑战。在健康监测和干预以及家庭自动化等应用的推动下，我们考虑了一个称为活动预测的新问题，其目标是根据传感器数据预测未来活动发生的时间。在本文中，我们做出了三个主要贡献。首先，我们在模仿学习的框架下制定和解决活动预测问题，并将其简化为简单的回归学习问题。这种方法使我们能够利用强大的回归学习器来推理问题的关系结构，而计算开销可以忽略不计。第二，我们提出了几个指标来评估现实世界应用程序中的活动预测器。第三，我们使用从 24 个智能家居测试台收集的真实传感器数据评估我们的方法。我们还将学习到的预测器嵌入到基于移动设备的活动提示器中，并为居住在智能家居中的九名参与者评估该应用程序。我们的结果表明，我们的活动预测器比基线方法表现更好，并提供了一种从传感器数据预测活动的简单方法。