Gait recognition, a biometric identification method, has garnered significant attention due to its unique attributes, including non-invasiveness, long-distance capture, and resistance to impersonation. Gait recognition has undergone a revolution driven by the remarkable capacity of deep learning to extract complicated features from data. An overview of the current developments in deep learning-based gait identification methods is provided in this work. We explore and analyze the development of gait recognition and highlight its uses in forensics, security, and criminal investigations. The article delves into the challenges associated with gait recognition, such as variations in walking conditions, viewing angles, and clothing as well. We discuss about the effectiveness of deep neural networks in addressing these challenges by providing a comprehensive analysis of state-of-the-art architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms. Diverse neural network-based gait recognition models, such as Gate Controlled and Shared Attention ICDNet (GA-ICDNet), Multi-Scale Temporal Feature Extractor (MSTFE), GaitNet, and various CNN-based approaches, demonstrate impressive accuracy across different walking conditions, showcasing the effectiveness of these models in capturing unique gait patterns. GaitNet achieved an exceptional identification accuracy of 99.7%, whereas GA-ICDNet showed high precision with an equal error rate of 0.67% in verification tasks. GaitGraph (ResGCN+2D CNN) achieved rank-1 accuracies ranging from 66.3% to 87.7%, whereas a Fully Connected Network with Koopman Operator achieved an average rank-1 accuracy of 74.7% for OU-MVLP across various conditions. However, GCPFP (GCN with Graph Convolution-Based Part Feature Polling) utilizing graph convolutional network (GCN) and GaitSet achieves the lowest average rank-1 accuracy of 62.4% for CASIA-B, while MFINet (Multiple Factor Inference Network) exhibits the lowest accuracy range of 11.72% to 19.32% under clothing variation conditions on CASIA-B. In addition to an across-the-board analysis of recent breakthroughs in gait recognition, the scope for potential future research direction is also assessed.

The past health survey shows death of few thousand people due to their heavy workload and mental pressure. Death of more patients is unavailability of doctors in critical situation. With the advancement in technology, the proposed system can monitor the patient's body condition uninterruptedly from anywhere in the world. The researcher is planned to make an inter-net of things for health care system which can monitor health of human being. In emergency situation notification will be send to doctors immediately. It takes the body parameters like Temperature, humidity, and pulse sensor and body movements. Analysis of patients’ body data is done against the normal situation to track abnormal physiological parameters.

Cloud computing is one term that has evolved drastically over the years. It involves deploying groups of remote servers and software that are networked, that will allow centralised data storage and online access to computer services or resources. However, an important problem in public clouds is how to selectively share data based on fine-grained attribute-based access control policies while at the same time assuring confidentiality of the data and preserving the privacy of users from the cloud. Keeping in mind the confidentiality of data that must be provided, many schemes and encryption algorithms have been implemented, but most of those schemes have high computational costs and overload on the data owner. In this paper, we are proposing a hierarchy-based privacy preserving access control scheme, in which data is encrypted and decrypted by the cloud using the blinded encryption and decryption technique that retains the constant computation cost to the owner and the user for any large file size, thus it makes our scheme suitable for energy deficient devices. The experimental result shows that our scheme is more suitable for energy deficient devices without compromising the security of data.