Falls could be dangerous in any situation but for certain working scenarios, consequences are definitely worst, therefore came the idea of developing a Machine Learning fall detection/report system. Each worker has a small TinyML device in charge of detecting falls with accelerometer data and reporting to a server through Bluetooth. The server is a Raspberry Pi running a Python script that scans specific BT announcements, parse fall information and store it into a SQL Lite database for reports and alerts.

Client

Connect the battery to the Tp4056 and the Tp4056 to the Arduino Nano BLE 33 Sense.

Download the machine learning model

Install as a zip library into the Arduino IDE

Upload the client code to the Arduino

3d print .gcode files attached to this tutorial

Server

Download Raspberry Pi Imager from https://www.raspberrypi.com/software/

Burn the Image of Raspberry PI OS Lite – no desktop is required - to a microSD card

Insert the microSD to a Raspberry Pi 3 and completed the setup (entering user and password)

Run

$ sudo raspi-config

and enable SSH and enter WiFi Credentials.

Install requisites

$ sudo apt install bluetooth libbluetooth-dev $ sudo apt-get install python3-pip $ pip3 install pybluez $ sudo apt-get install libbluetooth-dev bluez bluez-hcidump libboost-python-dev libboost-thread-dev libglib2.0-dev $ sudo pip3 install gattlib $ sudo apt-get install bluetooth libbluetooth-dev $ sudo python3 -m pip install pybluez $ sudo pip3 install termcolor

Upload to the Raspby Python scripts

Run the database structure setup

$ sudo python3 databaseSetup.py

Run the scan script

$ sudo python3 scan6.py