Human Activity Recognition Methods: A Review

Abstract

The Human activity recognition (HAR) collects events to distinguish as the sequence of annotations to recognise the actions of subjects to determine the ecological situation. Humans have the ability to recognize an event from a single movement. It is the natural tendency of human beings gives more attention to dynamic objects than the static objects. Human motion analysis is currently one of the most active research topics in machine learning. In this analysis the machine learning techniques for human activity recognition provides detail issues, there has been an influx to the recent situation from that the effective extraction and learning from live datasets as information. The methodology differs from traditional algorithms to the present machine learning techniques uses hand-crafted heuristically derived features to the newly generate hierarchical based self-evolving features. Different types of quantitative and statistical tools are available for prediction and thus results are evaluated with various existing methods to get better results of recognition. These techniques are classified into statistical forecasting models, shallow machine learning models, ensemble learning models, deep learning models and other learning models. From the literature review this work produces depth analysis results of deep learning models which are found to provide improved accuracy when it is calculated RMSE values in recognising the routine activity.