Modified Osprey-Optimized DM-CNN Model for Human Activity Recognition

Human Activity Recognition (HAR) is an AI-based technique aimed at identifying and labeling human activities. It gathers activity data from sources like wearable sensors and smart devices. Essentially, it categorizes human activities based on interactions, movements, and actions. Currently, it finds widespread application across various domains, leading to continuous research efforts for practical improvements. The present study revolves around HAR, a hybrid DM-CNN model, where the activities of humans are recognized by four phases including preprocessing, segmentation, feature extraction, and recognition. Here, the hybrid DM-CNN model uses a specific set of extracted features for enhanced training and they are MTH, IGBP and hierarchy of skeleton. Most importantly, the hybrid DM-CNN model provides excellent results on human activity recognition through the development of the Modified Osprey Optimization (MOO) algorithm for fine-tuning the DM and CNN models. The specificity of the proposed model is 95%.