Manufacturing QC Vision Kit v26 – Elder Care Fall Detection System with Raspberry Pi 5 & YOLOv8

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.

Create a computer vision device that keeps watch over an elderly relative, detecting falls through YOLOv8 pose estimation on a Raspberry Pi 5, and instantly dispatches a Twilio WhatsApp alert to caregivers. Build a reliable safety system without needing a cloud subscription or expensive commercial hardware.

What You'll Build

You’ll assemble a compact camera unit that continuously captures video, runs a YOLOv8 model to track human joints, and identifies a fall event with high accuracy. When a fall is detected, the system sends a WhatsApp message containing a timestamp and snapshot to a designated phone number, ensuring immediate response. The entire pipeline runs on the edge with a fast NVMe SSD for model storage and low-latency inferencing.

What You'll Learn

• Set up YOLOv8 on Raspberry Pi 5 with OpenCV and Pi Camera Module 3.
• Integrate Twilio’s WhatsApp API to trigger real-time alerts on fall detection.
• Optimise model inference speed using the NVMe SSD for rapid loading and frame processing.
• Customise pose estimation thresholds to reduce false alarms in different room environments.

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

This kit fits makers who want to prototype elder-care technology, engineering students working on Smart India Hackathon healthcare challenges, and CBSE Class 12 students exploring AI and IoT integration for social good. B.Tech ECE/EEE students