Abstract
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%.
Highlights
IntroductionSmart home technologies enable voiceactivated functions, automation, monitoring, and tracking events such as the status of windows and doors, entry, and presence detection
Sensors 2021, The availability and affordability of smart home technology have driven the rapid increase in the number of smart homes
Human activity recognition systems can be applied to many fields, such as assisted living, injury detection, personal healthcare, elderly care, fall detection, rehabilitation, entertainment, and surveillance in smart home environments [2]
Summary
Smart home technologies enable voiceactivated functions, automation, monitoring, and tracking events such as the status of windows and doors, entry, and presence detection. Activity recognition within smart homes can provide valuable information about the well-being of the smart home residence. Such information can be utilized to automatically adjust the ambient conditions of the rooms with the use of heating, ventilation, and air conditioning (HVAC). Another use of this information could be the detection of irregularities within the residence’s activities that indicate that assistance is required or a medical emergency. Human activity recognition systems can be applied to many fields, such as assisted living, injury detection, personal healthcare, elderly care, fall detection, rehabilitation, entertainment, and surveillance in smart home environments [2]
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