Advanced internet of things enhanced activity recognition for disability people using deep learning model with nature-inspired optimization algorithms

Human activity recognition has complex applications because of its worldly use of acquisition devices, namely video cameras and smartphones, and its capability to take human activity data. Human activity recognition became a hot scientific subject in the area of computer vision. It is convoluted in the growth of numerous significant applications like virtual reality, human-computer interaction, video surveillance, home monitoring, and security. Then, a broad range of activity recognition models is established for disabled individuals. Human activity recognition is recognized as the art of naming and identifying activities utilizing artificial intelligence-based deep learning and machine learning methods. In this manuscript, an Enhanced Activity Recognition for Disability People Using a Deep Learning Model and Nature-Inspired Optimization Algorithms (EARDP-DLMNOA) model is proposed. The EARDP-DLMNOA model mainly relies on improving the activity recognition model using advanced optimization algorithms. Initially, the data normalization stage is executed using the min-max normalization to convert input data into a beneficial format. Furthermore, the EARDP-DLMNOA model employs the adaptive chimp optimization (AdCO) technique for the feature subset selection. The deep convolutional auto-encoder (DCAE) technique categorizes data into predefined classes based on its features for the activity recognition process. Finally, the DCAE model's hyperparameter selection uses the zebra optimization algorithm (ZOA) model. A wide-ranging experimentation is carried out to validate the performance of the EARDP-DLMNOA approach under the HAR through the smartphone dataset. The experimentation validation of the EARDP-DLMNOA approach portrayed a superior accuracy value of 97.58% over existing methods.