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An ensemble approach to activity recognition based on binary sensor readings

机译：一种基于二进制传感器读数的整体活动识别方法

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Research on activity recognition provides a wide range of ubiquitous computing applications. Once activities are recognized, computers can use this information to provide people with suitable services. In the past decade, many classification algorithms have been applied to activity recognition. However, most of them were based on the use of inertial measurement sensors, such as tri-axial accelerometers and gyroscopes. There is still room for investigating the application of binary sensors in activity recognition. Simple binary sensors such as contact switches and motion detectors are commonly used in home security systems. In addition to low cost and ease of installation, the use of binary sensors is the least invasive approach from a privacy perspective. In this study, we developed an ensemble approach to human activity recognition based on binary sensor data. We verified our ensemble model with a publicly accessible activity dataset. The preliminary results showed that the proposed approach is stable and effective in recognizing activities of daily living (ADLs) in a smart home setting.
机译：活动识别的研究提供了广泛的计算应用程序。一旦识别出活动，计算机就可以使用此信息为人们提供适当的服务。在过去的十年中，许多分类算法已应用于活动识别。然而，它们大多数是基于惯性测量传感器的使用，例如三轴加速度计和陀螺仪。在活动识别中，仍有研究二进制传感器应用的空间。简单的二进制传感器（例如接触开关和运动检测器）通常用于家庭安全系统。从低成本的角度来看，除了低成本和易于安装之外，二进制传感器的使用也是侵入性最小的方法。在这项研究中，我们开发了一种基于二进制传感器数据的整体人类活动识别方法。我们使用可公开访问的活动数据集验证了集成模型。初步结果表明，该方法在识别智能家居环境中的日常生活（ADL）方面是稳定且有效的。

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