Internet of Things (IoT) based activity recognition strategies in smart homes: a review

A smart home, which is an extension of a traditional home, is equipped with ubiquitous sensors embedded in consumer appliances, connected via sensing technologies such as radio frequency, and communicating through the internet to gather information. They often communicate using appropriate protocols such as MQTT, CoAP, or HTTP to ensure the smooth transmission of data used by a variety of smart home services. Human activity recognition is one of the services provided by this IoT method of data collection from the sensor network when activated by residents. The obtained data can be subjected to extensive preprocessing and feature extraction tasks before being learned using appropriate machine learning or deep learning algorithms to generate a model capable of managing human activities more effectively. This technique is challenged by the nature of IoT technology and perceived data, as well as by human differences, which necessitated additional processing tasks to select significant features for the learning algorithms. In this work, we focus our review on activity recognition implementation strategies by examining various sensors and sensing technologies used to collect useful data from IoT devices, reviewing preprocessing and feature extraction techniques, as well as classification algorithms used to recognize human activities in smart homes. Many relevant works were examined and their achievements compared.The research demonstrates that IoT sensor technology for recognizing human activity in a smart home is practically feasible and efficient, even with individual differences in the smart home. However, it was discovered to be susceptible to issues such as insufficient and imbalanced data, annotation scarcity, and computational complexity. Finally, the study suggests that associating sensor data from the Internet of Things with numerous labels of activities based on time can help decrease computing overhead and improve activity recognition. © 2001-2012 IEEE.

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