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    • AI-driven Audio and Thermal HVAC Monitoring - SeeedStudio XIAO ESP32
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    • Arduino x K-Way - Outdoor Activity Tracker
    • Arduino x K-Way - Gesture Recognition for Hiking
    • Arduino x K-Way - TinyML Fall Detection
    • Posture Detection for Worker Safety - SiLabs Thunderboard Sense 2
    • Hand Gesture Recognition - OpenMV Cam H7
    • Arduin-Row, a TinyML Rowing Machine Coach - Arduino Nicla Sense ME
    • Fall Detection using a Transformer Model – Arduino Giga R1 WiFi
    • Bluetooth Fall Detection - Arduino Nano 33 BLE Sense
    • Monitor Packages During Transit with AI - Arduino Nano 33 BLE Sense
    • Smart Baby Swing - Arduino Portenta H7
    • Warehouse Shipment Monitoring - SiLabs Thunderboard Sense 2
    • Gesture Recognition - Bangle.js Smartwatch
    • Gesture Recognition for Patient Communication - SiLabs Thunderboard Sense 2
    • Hospital Bed Occupancy Detection - Arduino Nano 33 BLE Sense
    • Porting a Posture Detection Project from the SiLabs Thunderboard Sense 2 to xG24
    • Porting a Gesture Recognition Project from the SiLabs Thunderboard Sense 2 to xG24
    • Continuous Gait Monitor (Anomaly Detection) - Nordic Thingy:53
    • Classifying Exercise Activities on a BangleJS Smartwatch
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    • Indoor CO2 Level Estimation - Arduino Portenta H7
    • Harmful Gases Detection - Arduino Nano 33 BLE Sense
    • Fire Detection Using Sensor Fusion and TinyML - Arduino Nano 33 BLE Sense
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    • Methane Monitoring in Mines - Silabs xG24 Dev Kit
    • Smart Building Ventilation with Environmental Sensor Fusion
    • Sensor Data Fusion with Spresense and CommonSense
    • Water Pollution Detection - Arduino Nano ESP32 + Ultrasonic Scan
    • Fire Detection Using Sensor Fusion - Arduino Nano 33 BLE Sense
  • Novel Sensor Projects
    • 8x8 ToF Gesture Classification - Arduino RP2040 Connect
    • Food Irradiation Dose Detection - DFRobot Beetle ESP32C3
    • Applying EEG Data to Machine Learning, Part 1
    • Applying EEG Data to Machine Learning, Part 2
    • Applying EEG Data to Machine Learning, Part 3
    • Liquid Classification with TinyML - Seeed Wio Terminal + TDS Sensor
    • AI-Assisted Pipeline Diagnostics and Inspection with mmWave Radar
    • Soil Quality Detection Using AI and LoRaWAN - Seeed Sensecap A1101
    • Smart Diaper Prototype - Arduino Nicla Sense ME
    • DIY Smart Glove with Flex Sensors
    • EdgeML Energy Monitoring - Particle Photon 2
    • Wearable for Monitoring Worker Stress using HR/HRV DSP Block - Arduino Portenta
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    • ROS2 + Edge Impulse, Part 1: Pub/Sub Node in Python
    • ROS2 + Edge Impulse, Part 2: MicroROS
    • Using Hugging Face Datasets in Edge Impulse
    • Using Hugging Face Image Classification Datasets with Edge Impulse
    • Edge Impulse API Usage Sample Application - Jetson Nano Trainer
    • MLOps with Edge Impulse and Azure IoT Edge
    • A Federated Approach to Train and Deploy Machine Learning Models
    • DIY Model Weight Update for Continuous AI Deployments
    • Automate the CI/CD Pipeline of your Models with Edge Impulse and GitHub Actions
    • Deploying Edge Impulse Models on ZEDEDA Cloud Devices
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On this page
  • Project Demo
  • Intro
  • How Does it Work?
  • Client Devices
  • Client 3D Printed Case
  • Server Setup
  • Conclusion

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  1. Accelerometer and Activity Projects

Bluetooth Fall Detection - Arduino Nano 33 BLE Sense

A client/server device to detect and analyze worker falls with machine learning and an Arduino Nano 33 BLE Sense.

PreviousFall Detection using a Transformer Model – Arduino Giga R1 WiFiNextMonitor Packages During Transit with AI - Arduino Nano 33 BLE Sense

Last updated 1 year ago

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Created By: Roni Bandini

Public Project Link:

Project Demo

Intro

A fall could be dangerous in any situation, but for certain working scenarios, consequences can be very harmful. Therefore, the idea of developing a Machine Learning fall detection and reporting system could be quite useful in some industries.

How Does it Work?

Each worker has a small TinyML device in charge of detecting falls via the onboard accelerometer data, and reporting to a server through Bluetooth. The server is a Raspberry Pi running a Python script that scans specific BT announcements, parses the fall alert information, and stores it into a SQL Lite database for reports and alerts.

Client Devices

The electronics part of the client build is easy: just a battery, a TP4056 and the Arduino Nano 33 BLE Sense. The board has an onboard accelerometer, onboard RGB led, and enough processing power to run an Edge Impulse library for inferencing locally.

If you want to train your own fall model, go to the Edge Impulse Studio and log in, click on Data Acquisition, WebUSB, and choose the Inertial sensor. Obtain 5 minutes of data; Standing normally and Falling Down samples.

Design an Impulse with a 1500ms window size, 150ms window increase, and 100HZ frequency. Add Spectral Analysis with just 3 axis: accx, accy, accz. Choose Keras classification and 2 output features: Stand and Fall. For the Neural Network training, 50 training cycles with a 0.0005 learning rate, Autobalance the dataset, and 20% validation worked fine.

After model testing, go to the Deployment page and export an Arduino Library (which will contain your Machine Learning Model). Then import this library (Zip file) inside the Arduino IDE Sketch by selecting Include, Add Zip.

Once running, every fall is advertised with this format:

advertiseFall("Fall-"+worker+"-"+String(myCounter));

For example: Fall-Smith-1922

The device will change it's RGB LED from green to red, whenever a fall is detected.

Client 3D Printed Case

Server Setup

The other component we need to build next is the Python and database server, listening for bluetooth data coming from the Arduino. A Raspberry Pi will run the code fine, so, simply install Raspberry Pi OS Lite on an SD Card, boot up, and upload the Python files linked above from the GitHub repo.

Next, create a database structure with:

$ sudo python3 databaseSetup.py

Start scanning for bluetooth packets from the Arduino with:

$ sudo python3 scan.py

Other scripts included are: clearDatabase.py (removes all database records), and chart.py (creates a chart rendered from all of the database records).

Conclusion

In this project, we have demonstrated a simple method for Fall Detection using a client / server system running on an Arduino Nano 33 BLE Sense turned into a wearable device, along with a listening server running on a Raspberry Pi.

To add install Edge Impulse Firmware on the Nano 33, simply download the firmware from this link . Unzip the contents, connect the Arduino to your computer with a microUSB cable, double-click the Reset button on the Arduino, and run flash_window.bat from inside the folder (or the Mac or Linux commands if you are on one of those platforms).

All of the code for this project, including both the Client script file and the Python server files can be downloaded from .

The device will work without a case of course, but, to make it more convenient to wear and to hold all the pieces in place, two parts should be 3D printed and a strap should be attached. The Gcode files for this particular design can be .

https://cdn.edgeimpulse.com/firmware/arduino-nano-33-ble-sense.zip
this Github link
downloaded here
https://studio.edgeimpulse.com/public/130968/latest