This project was submitted by R.M. Dreifuerst in Make With Mesh: The Cypress & Digi-Key Bluetooth 5 IoT Design Contest Sponsored by Embedded Computing Design and was one of the winners!
Project Technology Focuses
- CYW20819 Dual-Mode Bluetooth 5 MCU
- Machine Learning
Project Requirements
Hardware
- CYW20819 Arduino Eval Kit (CYW920819EVB-02)
- Additional button for forced fall detection
Software
Project Description
This project leverages the CYW20819 Arduino Eval Kit to implement a machine learning powered automatic fall detection IoT device. Providing a fast and early response to medical emergencies is one of the most effective methods of ensuring recovery from hazardous situations - and so adding some automation to this process certainly brings to light some of the benefits in the medical world the IoT can bring. The project first created a data pipeline with the desired "fall" output using the on-board accelerometer on the CYW20819. The data was then fed into multiple machine learning models - eventually with a tested model being migrated to run on the CYW20819 processor to do automatic fall detection. In the end version of this project, if a fall is detected an emergency call on the user's smartphone would be initiated via Bluetooth.
Project Links
Resource List
- Bluetooth SDK Documentation
- Bluetooth SDK Technical Brief
- Bluetooth SDK Example Projects
- Cypress Bluetooth Mesh Webpage
- Cypress Bluetooth SDK Community Forum
- CYW20819 Dual-Mode Bluetooth 5 MCU Datasheet
- CYW20819 Feature and Peripheral Guide
- CYW20819 Low-Power Guidelines
- Getting Started With Bluetooth Mesh App Note