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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
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Issue Published : 119
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| Paper Title: | Adaptive Action Recognition for Transport, Fitness, Healthcare and Classroom Analytics Using Deep Learning |
| Authors Name: | ANGURU KARTHIK , ACHANTA VINEESH CHOWDARY , A BHANU CHARAN REDDY , B KESHAVARDHAN REDDY , J SOFIA |
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IJRTI_210346
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| Published Paper Id: | IJRTI2603035 |
| Published In: | Volume 11 Issue 3, March-2026 |
| DOI: | |
| Abstract: | Human Activity Recognition (HAR) has become an important technology for developing intelligent systems in various real-world domains such as healthcare monitoring, smart homes, fitness tracking, surveillance, transportation analysis, and human–computer interaction. By automatically identifying human activities from video streams or sensor-based data, HAR systems enable machines to better interpret human behavior and respond effectively in dynamic environments. This project introduces a deep learning–based framework titled “Adaptive Action Recognition for Transport, Fitness, Healthcare and Classroom Analytics Using Deep Learning.” The main goal of the system is to detect and classify different human activities from live video streams captured in real-time scenarios. The proposed framework integrates multiple deep learning techniques including Convolutional Neural Networks (CNNs), 3D Convolutional Neural Networks (3D-CNNs), and Long ShortTerm Memory (LSTM) networks. These models help capture both spatial information from individual video frames and temporal patterns that represent motion across consecutive frames. Video preprocessing, feature extraction, and temporal analysis are optimized to ensure efficient real-time processing while maintaining high recognition accuracy. A sliding window strategy with buffered frame sequences is used to continuously analyze incoming video streams, enabling the system to recognize activities without interruption. Additionally, transfer learning techniques are incorporated to improve the model’s ability to adapt to variations such as lighting conditions, different backgrounds, and diverse user behaviors. The developed system can be applied in several practical scenarios including fall detection for elderly healthcare monitoring, intelligent surveillance systems, gesture-based human–computer interaction, fitness activity tracking, and classroom activity analytics. |
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| Cite Article: | "Adaptive Action Recognition for Transport, Fitness, Healthcare and Classroom Analytics Using Deep Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 3, page no.a257-a263, March-2026, Available :http://www.ijrti.org/papers/IJRTI2603035.pdf |
| Downloads: | 000152 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2603035
Registration ID:210346
Published In: Volume 11 Issue 3, March-2026
DOI (Digital Object Identifier):
Page No: a257-a263 Country: Medachal, Telangana, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2603035 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2603035 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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