Classification of Human Activities using CNN with Principal Component Analysis

Human Activities Recognition is the process of automatically identifying a person’s physical activities in order to create a secure environment for everyone, even elderly people, in their daily lives. In this paper, the classification of human activities using Conventional Neural networks with Principal Component Analysis with presented. In the proposed method, Principal Component Analysis is employed for dimensionality reduction and Conventional Neural networks are employed for classification. The Human Activities Recognition dataset from Kaggle is used in the suggested model. The effectiveness of the proposed model is assessed in terms of accuracy. The proposed model achieved an accuracy of about 96.71%.