Human Activity Recognition Final Year Projects with Source Code

Byadmin Final Year Projects

Human Activity Recognition Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Human Activity Recognition projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Human Activity Recognition Final Year Projects

  1. Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree
    This project focuses on improving computer network security by detecting cyber-attacks more accurately. It reviews different intrusion detection methods, datasets, and challenges faced by researchers. Machine learning and deep learning are used to identify threats and reduce false alarms. The study proposes using a decision tree model to create an efficient system for spotting unusual activity in networks.
  2. Human Behavior Recognition Based on Multiscale Convolutional Neural Network
    This project focuses on recognizing human actions in videos using a smarter neural network. It improves how the network looks at both the overall and local details in each video. The method splits videos into segments, extracts important behavior information, and combines it over time to understand the full action. Tests show it is accurate, faster, and more efficient than older methods.
  3. Boosted Barnacles Algorithm Optimizer Comprehensive Analysis for Social IoT Applications
    This project focuses on improving the Social Internet of Things (SIoT), where smart devices share data for health monitoring, emergency alerts, and learning systems. It introduces a new method using the Barnacles Mating Optimizer to make data transfer faster and more accurate. The method was tested on real datasets and showed better performance than existing approaches. Overall, it helps smart devices work together more efficiently.
  4. A Multi-Order Distributed HOSVD with Its Incremental Computing for Big Services in Cyber-Physical-Social Systems
    This project focuses on improving how large amounts of complex data from interconnected systems, like smart devices and social networks, are analyzed. The researchers developed a new method called MDHOSVD that processes data efficiently in small blocks and updates results incrementally. This approach makes data processing faster, handles more data, and turns raw information into useful insights. A real-world case study showed that this method works well for managing and understanding big, diverse datasets.
  5. The Biomechanical Analysis on the Tennis Batting Angle Selection under Deep Learning
    This study analyzes how the strength and angle of a tennis player’s swing affect their performance. It uses video images and a deep learning model to track and evaluate player movements in real time. The system compares joint angles and motion with other methods and shows better accuracy. Results reveal how specific joint positions, like the ankle and knee, influence hitting speed and success in volleys.
  6. Cascade Windows-Based Multi-Stream Convolutional Neural Networks Framework for Early Detecting In-Sleep Stroke Using Wristbands
    This project develops a wearable system with two wristbands to monitor people while they sleep for signs of stroke. It uses motion data from both hands and a deep learning framework called EDIS to detect strokes quickly and accurately. The system processes the data with multiple neural network models and combines their results to make a final decision. This approach helps detect strokes early, allowing timely treatment and potentially reducing long-term disabilities.
  7. STGL-GCN SpatialTemporal Mixing of Global and Local Self-Attention Graph Convolutional Networks for Human Action Recognition
    This project focuses on recognizing human actions using skeleton data from videos. The method looks at both local and global connections between body joints to better understand movements. It uses a special neural network that learns which joint connections are most important for each action. Tests show it can accurately identify different human actions.
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Human Activity Recognition Project Synopsis & Presentation

Final Year Projects helps prepare Human Activity Recognition project synopsis, including problem statement, objectives, existing system, disadvantages, proposed system, advantages and research motivation. We provide PPT slides, tutorials, and full documentation for presentations.

Human Activity Recognition Project Thesis Writing

Final Year Projects provides thesis writing services for Human Activity Recognition projects. We help BE, BTech, ME, MSc, MCA and MTech students complete their final year project work efficiently.

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