Human Activity Recognition (HAR) plays a significant role in several fields by automatically identifying and monitoring human activities using advanced techniques. It enhances safety, improves healthcare services, optimizes fitness routines, and enables context-aware applications in various fields. HAR contributes to a more efficient and intelligent interaction between humans and technology. It has emerged as an essential research domain with applications in healthcare, smart environments, and human-computer interaction. This study aims to provide a comprehensive survey of the evolving landscape of HAR, including key methodologies, techniques, and trends in existing research. The study discusses various applications of HAR and their significance in modern smart environments. The survey also highlights different types of HAR and data collection techniques. Additionally, it explores various methods for analyzing the collected data and provides a comprehensive analysis of existing human activity classification datasets. It offers valuable insights into understanding the strengths and limitations of various HAR techniques. The study also discusses various challenges and future directions for HAR.
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Medical image fusion technology and its collective diagnosis are becoming crucial day by day.This task confers the latest algorithm for image fusion of medical images to many diagnostic complications. Firstly, transform is employed on input source images. The result of the application of transform is the decomposition of source images into various subbands. Eminent features are extracted from these subbands by using resnet50. These features are fused by phase congruency and guided filtering fusion rules. Finally, inverse transform gives the original image. The experiment results of this algorithm are compared with different methods by taking some pairs of medical images. Subjective and objective outcomes prove that the proposed algorithm exceeds the current methods by giving optimal performance measures in the area of medical diagnosis. Thus, it is revealed that the suggested multimodal image fusion model provides elevated performance over existing models via diverse diseases using MRI-SPECT and MRI-PET.