Development of weighted residual RNN model with hybrid heuristic algorithm for movement recognition framework in ambient assisted living.

In healthcare applications, automatic and intelligent movement recognition systems in Ambient Assisted Living (AAL) are designed for elderly and disabled persons. The AAL provides assistance as well as secure feelings to disabled persons and elderly individuals. In AAL, the movement recognition process has been emerging in recent days. The automatic and safe living of the disabled person is ensured by performing movement recognition in AAL. Movement recognition in the AAL is developed for disabled and elderly people and is also performed to provide healthcare assistance to the elderly and disabled person. The weighted deep learning model and a hybrid heuristic algorithm are proposed to achieve this goal. The required input data is initially gathered from the standard data sources. Subsequently, the essential deep features are extracted from the input data using a Convolutional Autoencoder. Finally, the resultant features are subjected to the movement recognition model, termed as Weighted Residual Recurrent Neural Network. For achieving a better training and testing process, the weights in the RRNN model are optimally selected by using the hybrid algorithm named Hybrid Rat Swarm with Coati Optimization Algorithm, which is developed with the integration of the Rat Warm Optimization and Coati Optimization. The movement recognition results are used for providing medical assistance to elderly and disabled persons. Lastly, the efficacy of the suggested strategy is validated with different measures. From the experiments, the proposed system attains standard results in terms of improved system performance and accuracy that can aid in significantly recognizing human movements.