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@article{195133,
        author = {P.Usha Manikyam and N.Prasannakumari and P.Suma Gayathri and Sk.Yaseen and M.Gopi Venkata Satish},
        title = {HUMAN ACTIVITY RECOGNITION DEEP LEARNING CLASSIFICATION FOR RECOGNIZING HUMAN ACTIVITIES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {8204-8209},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195133},
        abstract = {Human Activity Recognition (HAR) is an important application of computer vision that focuses on identifying human actions from video data. In this paper, a real-time activity recognition system is developed using a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The proposed approach captures both spatial and temporal information from video sequences to improve classification performance. Video frames are collected using a webcam and processed through resizing and normalization. The CNN model extracts visual features from individual frames, while the LSTM network analyses the sequence of frames to understand motion patterns over time. A fully connected Softmax layer is used to classify the activities into multiple categories. The model is trained using standard human activity datasets and evaluated in both offline and real-time environments. Experimental results show that the combined CNN-LSTM model provides better accuracy compared to using individual models. The system is efficient, scalable, and suitable for real-time applications such as surveillance, healthcare monitoring, and smart systems.},
        keywords = {Human Activity Recognition, Deep Learning, CNN, LSTM, Computer Vision, Real-Time Prediction, Video Classification},
        month = {March},
        }