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首页> 外文期刊>IEEE Transactions on Knowledge and Data Engineering >Learning Activity Predictors from Sensor Data: Algorithms, Evaluation, and Applications
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Learning Activity Predictors from Sensor Data: Algorithms, Evaluation, and Applications

机译：从传感器数据中学习活动预测变量：算法，评估和应用

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摘要

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.
机译：物联网（IoT）平台的最新进展使我们能够收集大量传感数据。但是，将这种大规模的传感数据转换为现实应用的决策时面临着巨大的挑战。受健康监控，干预和家庭自动化等应用程序的激励，我们考虑了一个称为“活动预测”的新问题，该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内制定并解决了活动预测问题，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功能强大的回归学习器，这些学习器可以用可忽略的计算开销来推理问题的关系结构。其次，我们提出了几种指标来评估实际应用中的活动预测指标。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，并针对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，我们的活动预测器的性能优于基线方法，并提供了一种从传感器数据预测活动的简单方法。

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