The number of smart homes is rapidly increasing. Smart homes typically feature functions such as voice-activated functions, automation, monitoring, and tracking events. Besides comfort and convenience, the integration of smart home functionality with data processing methods can provide valuable information about the well-being of the smart home residence. This study is aimed at taking the data analysis within smart homes beyond occupancy monitoring and fall detection. This work uses a multilayer perceptron neural network to recognize multiple human activities from wrist- and ankle-worn devices. The developed models show very high recognition accuracy across all activity classes. The cross-validation results indicate accuracy levels above 98% across all models, and scoring evaluation methods only resulted in an average accuracy reduction of 10%.

中文翻译：

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使用多层感知器的人类活动分类

智能家居的数量正在迅速增加。智能家居通常具有语音激活功能、自动化、监控和跟踪事件等功能。除了舒适性和便利性之外，智能家居功能与数据处理方法的集成可以提供有关智能家居居住状况的宝贵信息。本研究旨在对智能家居中的数据分析进行超越占用监控和跌倒检测。这项工作使用多层感知器神经网络来识别手腕和脚踝佩戴设备的多种人类活动。开发的模型在所有活动类别中都显示出非常高的识别准确度。交叉验证结果表明所有模型的准确率都在 98% 以上，评分评估方法仅导致平均准确率降低 10%。