Self-Improved Namib beetle optimization Trained Hybrid Model for Human Activity Recognition

Human Activity Recognition is a recognition technique which identify and name the human activities by AI. It collects activity data from devices such as wearable sensors and smart devices etc. Literally, it classifies human activities on the basis of interaction, motion and action. Nowadays, its usability is spread in various applications so, the research on Human Activity Recognition is still ongoing with viable enhancements. Though, this technique needs to solve limitations such as collection and processing of data, stability of hardware and approaches, complexity in activity detection and messed up activities. This work is based on Human Activity Recognition which is trained by the hybrid model of classifiers such as LSTM and Bi-GRU via proposed Self-Improved Namib Beetle Optimization model (SINBO). The activity recognition is detected by two stages: (1) Preprocessing, (2) Feature Extraction and (3) Classification. The extracted features areImproved bag of visual words, Local texton XOR pattern and SLIF are given as an input in classification stage. The hybrid model of LSTM and Bi-GRU is enhanced by optimal tuning of weights via SINBO algorithm.