A smart home inhabitant performs a unique pattern or sequence of tasks repeatedly. Thus, a machine learning approach will be required to build an intelligent network of home appliances, and the algorithm should respond quickly to execute the decision. This study proposes a decision tree-based machine learning approach for predicting the activities using different appliances such as state, locations and time. A noise filter is employed to remove unwanted data and generate task sequences, and dual state properties of a home appliance are utilized to extract episodes from the sequence. An incremental decision tree approach was taken to reduce execution time. The algorithm was tested using a well-known smart home dataset from MavLab. The experimental results showed that the algorithm successfully extracted 689 predictions and their location at 90% accuracy, and the total execution time was 94 s, which is less than that of existing methods. A hardware prototype was designed using Raspberry Pi 2 B to validate the proposed prediction system. The general-purpose input-output (GPIO) interfaces of Raspberry Pi 2 B were used to communicate with the prototype testbed and showed that the algorithm successfully predicted the next activities.

中文翻译：

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一种预测智能家居居民活动的机器学习方法

智能家居居民重复执行独特的模式或任务序列。因此，将需要机器学习方法来构建家用电器的智能网络，并且算法应快速响应以执行决策。本研究提出了一种基于决策树的机器学习方法，用于使用不同的设备（例如状态、位置和时间）来预测活动。噪声滤波器用于去除不需要的数据并生成任务序列，并利用家用电器的双状态属性从序列中提取情节。采用增量决策树方法来减少执行时间。该算法使用来自 MavLab 的著名智能家居数据集进行了测试。实验结果表明，该算法以90%的准确率成功提取了689个预测及其位置，总执行时间为94 s，低于现有方法。使用 Raspberry Pi 2 B 设计了一个硬件原型来验证所提出的预测系统。Raspberry Pi 2 B 的通用输入输出 (GPIO) 接口用于与原型测试台进行通信，并表明该算法成功预测了下一步活动。