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6
minutes

How to Build a Fall Detector

In this guide, we’re going to walk through creating a fall detector app in just 5 minutes. This app will automatically alert you if someone falls—a potentially life-saving tool for elderly care, workplace safety, and more. Let’s show you how easy it is to turn an idea into reality with EyePop.ai.

Falls can be a major safety concern. Whether in elder care, workplaces, or even at home, fall detection can prevent severe injuries and save lives.

Typically, creating a fall detector requires complex coding, model training, and specialized hardware. With EyePop.ai, we’ve simplified this entire process.

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In just a few minutes, you’ll have a working fall detection app that will:

  1. Capture images from your webcam.
  2. Send the images to your EyePop endpoint.
  3. Analyze whether someone is standing or has fallen.
  4. Play an audio alert if a fall is detected.


EyePop’s Pop API endpoints handle detection and alerts so you can focus on deployment, not backend setup. While Python 3.10+ is required, this guide only needs basic Python knowledge so you can dive right in! Let's get started...

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Step 1: Create Your Free EyePop.ai Account

The first step is to sign up for a free EyePop account. This will give you access to the platform’s tools and allow you to create custom computer vision models. 

Simply sign up here for free and you’re good to go.

Once you’re in, take a moment to explore the dashboard to see all we have inside for you.

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Step 2: Set Up a Pop

In EyePop, an endpoint is called a “Pop.” Think of a Pop as your personal API endpoint, where all the magic happens. This endpoint connects your app directly with EyePop’s object detection tools, so you can capture and process images instantly. 

Here’s how to set up your Pop:

Select [ Person w/2D Body Points ] in the 'What to Find' dropdown menu.

Once your Pop is set up, it’s ready to receive images, detect objects, and return the results. This setup lets you control your model’s functionality with just a few lines of code.

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Step 3: Create a Project Folder

Before we jump into coding, create a new folder on your computer. This will be the home for all your fall detector files, including your script and any assets, like the audio alert we’ll set up next. Keeping everything organized will make testing and troubleshooting easier down the line.

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Step 4: Record an Alert Sound

To make the fall detector truly functional, we’ll set up an audio alert that plays whenever a fall is detected. Record a short sound or message (for example, “Alert! Fall detected!”) and save it as fall_detector.mp3 in your project folder. This way, your detector can communicate its findings immediately—essential in a real-world application.

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Step 5: Install Necessary Libraries

To make everything run smoothly, we need to install a few Python libraries. These libraries will handle everything from playing sounds to sending images to EyePop. In your terminal, type the following command:


pip install requests playsound opencv-python eyepop



Once these libraries are installed, your environment is ready to handle video, sounds, and EyePop’s API—all the essentials for your fall detector.

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Step 6: Add the Code

Here’s the main code for your fall detector. Paste it into your Python 3.10+ editor, and let’s go through what it does.

This script will:

  1. Capture images from your webcam.
  2. Send the images to your EyePop endpoint.
  3. Analyze whether someone is standing or has fallen.
  4. Play an audio alert if a fall is detected.

Each part of this script connects directly to EyePop’s tools, allowing it to detect objects and respond instantly. Copy and paste the code, and I’ll guide you through setting up the details.


import requests
import cv2
import time
import playsound
from eyepop import EyePopSdk
import matplotlib.pyplot as plt
from PIL import Image

pop_id = 'YOUR_POP_ID'
secret_key = 'YOUR_SECRET_KEY'

def upload_photo(file_name):
    with EyePopSdk.workerEndpoint(pop_id=pop_id, secret_key=secret_key) as endpoint:
        result = endpoint.upload(file_name).predict()
    with Image.open(file_name) as image:
        fig, ax = plt.subplots()
        ax.imshow(image)
        plot = EyePopSdk.plot(ax)
        plot.prediction(result)
        plt.draw()
        plt.pause(2)
        plt.close(fig)
    return result    

previous_height = 0

def process_results(result):
    data = result['objects'][0]
    height, width = data['height'], data['width']
    return 'Not Standing' if height < width else 'Standing'

def compare_results(previous, current):
    if previous == 'Standing' and current == 'Not Standing':
        playsound.playsound('fall_detector.mp3')

cap = cv2.VideoCapture(0)

prev = 'Not Standing'

while True:
    ret, frame = cap.read()
    
    if not ret:
        print('Failed to capture a photo')
        break

    cv2.imshow('Captured Frame', frame)
    cv2.waitKey(10)
    cv2.imwrite('captured_photo.jpg', frame)
    result = upload_photo('captured_photo.jpg')

    if result:
        if 'objects' in result:
            curr = process_results(result)
            print(curr)
            compare_results(prev, curr)
            prev = curr
        else:
            print("Your camera probably isn't working")
            print(result)
    
    time.sleep(4)

cap.release()
cv2.destroyAllWindows()

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Step 7: Enter Your Credentials

Before you run the code, replace the placeholder values for pop_id and secret_key with your actual credentials. These are unique to your EyePop account and allow your script to communicate with your specific Pop.

  1. Your Pop UUID
  2. Your Secret API Key

Here’s where to find them from your EyePop dashboard:

This will authenticate your connection to EyePop’s API and allow your fall detector to interact with your endpoint.

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Final Step: Testing Your Detector

Now that your code is ready, it’s time to test it out. Start the script, and it will begin analyzing video frames. If someone falls, the alert you recorded will play automatically.

Give it a try! Adjust your camera, make sure the lighting is clear, and watch your detector in action.

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Wrapping Up

Congratulations! 🎉 You’ve just created a fully functional fall detector with EyePop.ai in a matter of minutes. Instead of spending hours building a computer vision MVP, EyePop helps you get set up quickly so you can focus on making a difference.

Check out more exciting demos here to see what else you can build!

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EyePop.ai: Your Fast Track to Building Powerful Computer Vision Apps

Imagine being able to set up a cutting-edge computer vision app in a matter of minutes, without needing a team of developers, a deep understanding of machine learning, or spending weeks troubleshooting complex configurations. EyePop.ai makes it possible.

Computer vision is transforming industries, from enhancing workplace safety to streamlining e-commerce processes and automating quality control. Traditionally, building these apps was time-consuming and technically challenging, but EyePop.ai is here to change that. EyePop is like having a superpower in your toolkit, letting you create high-quality, AI-driven vision apps with ease, accuracy, and at a fraction of the time and cost it usually takes.

What is EyePop.ai?

EyePop.ai is a platform designed to empower anyone to develop AI-powered vision applications with minimal setup. Instead of spending hours training a model and configuring endpoints, EyePop does the heavy lifting for you. Simply set up an account, configure your API endpoint (called a Pop), and start detecting objects in images or video streams in no time.

Ready to get started? The first step is simple—sign up for a free EyePop account and see the power of EyePop for yourself.

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