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‎pages/2024projects.mdx‎

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# TnyML Hack 2024 Projects Showcase.
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Welcome to the 2024 tinyML Hackathon Projects Showcase, where innovation meets edge intelligence! This page serves as a curated collection of all the remarkable projects created during the 2024 tinyML Hackathon, held on December 21 and 22. Participants from diverse backgrounds collaborated to leverage the power of tinyML and push the boundaries of what's possible with machine learning at the edge.
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Explore creative solutions, learn from cutting-edge implementations, and get inspired by the transformative potential of tinyML. Whether you're a maker, developer, or enthusiast, this page is your gateway to discovering impactful applications in the world of tiny machine learning.
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| **Team Name** | **Project Title** | **Team Members** | **Documentation** | **Photo** | **Winner** |
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|------------------|-------------------------------------|------------------------------|-------------------------------------------------------|-------------------------------|--------------|
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| Team DevScript! | An Tiny-ML Project which recognize babies crying voice,send notification and can remotely swigs the crib | Jerald Joyson , Bimal Devasia , Mohamed Nishan | [View Documentation]() | | 🏆 Best Product|
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| Team XYZ | Safeguarding school children's lives. Tackling the critical issue of unattended children in school buses | Richu | [View Documentation](https://example.com/doc2) | | 🏆 Best Use of XIAO |
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| Team Momentum | An intelligent and compact solution leveraging Tiny ML to detect, predict, and mitigate stampedes, ensuring public safety through real-time monitoring and analysis | Amal Murali PK,Akshay S Rajan,Naveed PN,Muhammad Rashid| [View Documentation](https://example.com/doc3) | ![Project Photo](https://example.com/photo3.jpg) | 🏆 Best Best Use of Edge Impulse / AI|

‎pages/2024projects/devscript.mdx‎

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# Team Dev-Script

‎pages/2024projects/img/devscript/devscript.jpg‎

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‎pages/2024projects/img/momentum/momentum.jpg‎

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‎pages/2024projects/momentum.mdx‎

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# Team Momentum
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<iframe src="https://docs.google.com/presentation/d/e/2PACX-1vRAtZ-QEZ6XD6VmTUr-1lzeUHheSxgmGPLdAoHO6LBEUn4LPreiNLyXrz9XYDX__Ci_lCY7EFWtJQNd/embed?start=false&loop=false&delayms=3000" frameborder="0" width="860" height="469" allowfullscreen="true" mozallowfullscreen="true" webkitallowfullscreen="true"></iframe>
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### Team Members
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* Team Lead: Akshay S Rajan - CUSAT
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* Member 2 :Rashid C A - CUSAT
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* Member 3: Naveed P N - CUSAT
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* Member 4: Amal Murali P K - CUSAT
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## Project Description
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An intelligent and compact solution leveraging Tiny ML to detect, predict, and mitigate stampedes, ensuring public safety through real-time monitoring and analysis
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## The Problem
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Stampedes in crowded areas often result in chaos, injuries, and fatalities, primarily due to the lack of real-time monitoring and effective crowd management solutions. Traditional methods are either slow, resource-intensive, or incapable of predicting such incidents proactively.
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## The Solution
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We solved this problem by implementing a Machine learning model on a TinyML component named Grove vision AI Module V2.
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This AI model utilizes the advanced Swift-YOLO algorithm, focusing on person recognition, and can accurately detect and tag individuals in real-time video streams. It is particularly suited for the SeeedStudio Grove Vision AI (V2) device, offering high compatibility and stability
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## Technical Details
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### Technologies/Components Used
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#### For Software:
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* Python - pyserial
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* Arduino IDE
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* Seeed Arduino SSCMA Library
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* Pre-trained AI Model ([Model Detail - - SenseCraft AI](https://sensecraft.seeed.cc/ai/#/model/detail?id=60242&tab=public))
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#### For Hardware:
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* Grove Vision AI Module V2
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* Seeed ESP32S3
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## Implementation
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* Plug and connect Grove Vision AI Module V2 to sense craft (Person Detection Model)
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* Download and install Seeed-Arduino-SSCMA Library from github ([GitHub](https://github.com/Seeed-Studio/Seeed_Arduino_SSCMA/))
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* Add Library to Arduino IDE and include the header file
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* Connect ESP32S3 module with Grove Vision AI Module V2
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* Write a program to extract the person count and compare with the limit.
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* Add WIFI header file to utilize the WIFI functionality of ESP32S3 ("WiFi.h")
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* Add HTTPClient header file to make HTTP requests ("HTTPClient.h")
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* Implement Telegram notification system to Alert the organisers.
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* Write python code using pyserial to read the data from Module for further data analysis.
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* The collected data is uploaded to google sheet and graphs are created.
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## Installation
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‎pages/_meta.json‎

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{
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"index": "Introduction",
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"2024projects": "TnyML Hack 2024 Projects Showcase",
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"tools": "Hardware Tools",
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"esp32s3": "XIAO ESP32S3 Sense",
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"nrf52840": "XIAO nRF52840 Sense",
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"visionv2": "Grove Vision v2",
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"edgeimpulse": "Edge Impulse",
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"examples":"Examples",
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"about": {
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"title": "About",
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"type": "page"

‎pages/examples.mdx‎

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