With the increasing age of an individual, the chances of being prone to chronic diseases like diabetes or non-curable diseases like Alzheimer’s Syndrome or Parkinson's Syndrome increases. Due to the health issues, elderly must be accompanied by caretakers to monitor their well-being at all times. With growing responsibilities and work pressure, the family members may find it challenging to find a trustworthy caretaker. In such scenarios, an assisted living environment acts as a boon. A normal home embedded with different sensors to monitor an individual’s well-being is called as Ambient Assisted Living(AAL). This living environment detects anomalous behaviour and recognizes human activities. In this research paper, a smart home activity recognition model is proposed and implemented using four machine learning algorithms using six different publicly available datasets. It has been observed that Random Forest machine learning algorithm shows the best accuracy on most of the dataset.

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The network of physical items/things/objects, that are implanted with sensors, software, and other networking technologies to communicate and exchange data with other devices and systems through the internet, is referred to as the Internet of Things (IoT). Environmental control in the context of IoT systems refers to the use of connected devices and sensors to manage and regulate various aspects of the environment, such as temperature, lighting, air quality, water quality, and more. The goal is to create an intelligent environment that is more efficient, comfortable, and sustainable.