Biologically-inspired machine intelligence technique for activity classification in smart home environments
With the widespread adoption of Internet-connected devices and the prevalence of applications in the Internet of things (IoT), devices in smart homes can generate enormous amounts of data. There is a requirement for machine-learning techniques to learn from historical patterns and predict future activities. There is an increased interest in machine-learning techniques that can provide useful and interesting services in the smart home domain. The areas that machine-learning techniques can help advance are varied and ever-evolving. Predicting and classifying the Activities of Daily Living (ADLs) of inhabitants in a smart home environment are key modules to automate smart home devices. Some prominent examples include uses for entertainment, elderly care, healthcare, and security. The abilities of the machine-learning technique to find meaningful spatio-temporal relations of high-dimensional data and to learn from streaming datasets are important requirements. Recently, the Hierarchical Temporal Memory (HTM) theory has been presented as a biologically-inspired machine-learning theory that attempts to mimic the neocortex, the front part of the human brain. The main features of the HTM theory include the ability
Alshammari, Talal
Biologically-inspired machine intelligence technique for activity classification in smart home environments. With the widespread adoption of Internet-connected devices and the prevalence of applications in the Internet of things (IoT), devices in smart homes can generate enormous amounts of data. There is a requirement for machine-learning techniques to learn from historical patterns and predict future activities. There is an increased interest in machine-learning techniques that can provide useful and interesting services in the smart home domain. The areas that machine-learning techniques can help advance are varied and ever-evolving. Predicting and classifying the Activities of Daily Living (ADLs) of inhabitants in a smart home environment are key modules to automate smart home devices. Some prominent examples include uses for entertainment, elderly care, healthcare, and security. The abilities of the machine-learning technique to find meaningful spatio-temporal relations of high-dimensional data and to learn from streaming datasets are important requirements. Recently, the Hierarchical Temporal Memory (HTM) theory has been presented as a biologically-inspired machine-learning theory that attempts to mimic the neocortex, the front part of the human brain. The main features of the HTM theory include the ability
biologically inspired machine intelligence technique activity classification home environment widespread_adoption internet connected device prevalence application internet thing device home generate enormous amount_data requirement machine learning technique learn historical pattern predict future activity increase_interest machine learning technique provide_useful interesting service home domain area machine learning technique help advance varied ever predicting classify activities daily living home environment key module automate home device prominent example include uses entertainment elderly care healthcare security ability machine learning technique meaningful spatio_temporal relation high dimensional data learn stream datasets important requirement recently hierarchical temporal memory theory present biologically inspired machine learning theory attempt front part human brain main_feature theory include ability