Human Activity Recognition Projects for ME, MTech, Masters, MS abroad, and PhD students. These Human Activity Recognition ieee projects are implemented with future work and extension for final year students with research paper writing and publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Human Activity Recognition Projects
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Mobiprox: Supporting Dynamic Approximate Computing on Mobiles
This project develops a system called Mobiprox that makes deep learning on mobile devices more flexible and efficient. It allows the model to adjust its computations depending on the situation, like how hard the input is or how much battery is left. The system saves energy while keeping almost the same accuracy. It was tested on tasks like activity recognition and voice commands on Android phones. -
Human Activity Recognition Based on Wireless Electrocardiogram and Inertial Sensors
This project uses wearable sensors to monitor a person’s heart activity and body movements. It applies deep learning to identify what activity a person is doing, such as running, sitting, or climbing stairs. The system achieved very high accuracy, showing that combining heart signals and motion data gives better results. It helps in tracking health and daily activity remotely with precision. -
A High-Level Synthesis Library for Synthesizing Efficient and FunctionalSafe CNN Dataflow Accelerators
This project focuses on making deep learning models run faster and more efficiently using special hardware. It allows users to turn CNN models from Python frameworks like TensorFlow or PyTorch into hardware designs automatically. The system improves speed and energy use while keeping the design process simple. It also adds safety and better data handling features for reliable performance.
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