Kinthada Harish Chandra, B Ravindar Reddy 02 08 In the evolving landscape of healthcare continuous patient monitoring has shifted from manual oversight to intelligent automation powered by IoT devices and deep learning models This project presents a robust system for recognizing human activities in a healthcare setting using multimodal IoT sensor data from accelerometers and gyroscopes The proposed model integrates a hybrid deep learning architecture combining Random Forest for feature selection Gated Recurrent Unit GRU for temporal analysis and an Attention Mechanism AM for focusing on critical features The system processes the KUHAR dataset training the hybrid ELMGRUAM model on 80 of the data and testing on the remaining 20 Experimental results show that the proposed model achieves outperforming traditional models such as Random Forest Performance metrics including precision recall F1score and confusion matrices confirm the models reliability A webbased interface supports functionalities such as user registration login dataset processing model training and activity recognition The enduser can upload test data and receive realtime activity predictions making the system practical for realworld personal healthcare applications Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026