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%.
Nowadays, humanity is facing a difficult challenge focused on the sustainability of further years. One of the major ways is the usage of renewable energy as a sustainable and reliable source of electric power. This trend is also obvious in the field of the Internet of Things, where research teams are increasingly focusing on renewable energy and its improvement. This paper aims to map current research on the use of renewable resources on the Internet of Things with a focus on use in geothermal applications. Information concerning renewable energy sources and individual IoT platforms is summarized.
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