Fundamental Recognition of ADL Assessments Using Machine Learning Engineering

This paper describes an RGBD (Red Green Blue Depth) image daily life activity identification system that can monitor and detect human activities without the need of optical markers or motion sensors. In this paper, we have developed a practical methodology for detecting human activity in the daily environment. For feature extraction technique, we suggested a noble method for recognition of ADL activities based on symmetry principle. By extracting silhouette from depth images and performing mapping operations over RGB images, we have been able to extract the skeleton information of the human body in RGBD images and identify ADL activities using four critical parameters: angle formation between hand and upper half of the body, angle between center body point and hand, angle formation between hand and lower half of the body, and angle between two hands of the single silhouette. We employed the linearly dependent concept (LDC) and long short-term memory-recurrent neural networks (LSTM-RNN) for feature selection and classification, compared the results to existing approaches. The objective of our research is to not only find an effective and useful collection of features from the silhouette, but also to outperform current approaches. Finally, the suggested method’s testing results showed a 2.5 percent increase in accuracy with a 92.83 percent success rate, as well as a reduction in relative error to 2.47 percent of the original dataset.