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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. AAL is already being used to observe the wellness of the elderly population across the globe. This paper gives an insight into human activity recognition in a smart home living environment. A smart home activity recognition model is proposed and implemented using four machine learning algorithms. The proposed model is applied to six different publicly available datasets and the accuracy was estimated using different measures. It has been observed that Random Forest machine learning algorithm shows the best accuracy on most of the datasets.
How to Cite
Activity Recognition, Health care., Elder care, Machine Learning, Smart Home
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