Evrimsel sinir ağı (CNN) kullanarak insan faaliyetlerinin tanıma için etkili bir model

Information can be obtained from individuals through the systems of classifying and recognizing human activities at any time. These systems are used in different areas such as the detection of diseases, improvement of physical therapy stages, development of smart home projects. In this study, the data obtained from accelerometer and gyroscope sensors on smart phones are used. Most of the studies in the literature are unable to analyse higher-level features and their relationships based on machine learning and deep learning techniques. Convolutional Neural Network (CNN) model is a very suitable deep learning approach due to its ability to obtain high level and sensitive features. The deep learning-based approach that includes this background has been used in the classification of various human activities in the experiments in our study. In the experiments, the classification performance accuracy rate was measured by giving different input parameters, layer and network units to the relevant network models. As a result, it has been shown that six different classes are classified with high accuracy, achieving a classification performance of approximately 97.98%