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%.

• Klíčová slova
• artificial neural network (ANN), human activity recognition, intelligent buildings (IB), smart home (SH),
• MeSH
• automatizace MeSH
• bydlení MeSH
• lidé MeSH
• lidské činnosti * MeSH
• neuronové sítě * MeSH
• zápěstí MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH