Abstract Human Activity Recognition (HAR) has been a challenging problem yet it needs to be solved. It will mainly be used for eldercare and healthcare as an assistive technology when ensemble with other technologies like Internet of Things(IoT). HAR can be done with the help of sensors, smartphones or images. In this paper, we present various state-of-the-art methods and describe each of them by literature survey. Different datasets are used for each of the methods wherein the data are collected by different means such as sensors, images, accelerometer, gyroscopes, etc. and the placement of these devices at various locations. The results obtained by each technique and the type of dataset are then compared. Machine learning techniques like decision trees, K-nearest neighbours, support vector machines, hidden markov models are reviewed for HAR and later the survey for deep neural network techniques like artificial neural networks, convolutional neural networks and recurrent neural networks is also presented.