Mobile & IoT
A complete IoT safety system, firmware to dashboard
Role
Developer
Duration
One Trimester
Date
2025
Type
University Project
A four-layer system built end to end. An Arduino-based wearable written in C++ streams motion sensor data, a Python machine learning model classifies falls versus normal activity, a Node.js server ingests events and triggers alerts, and a web dashboard gives caregivers live monitoring. One of the few student projects where hardware, ML, and web engineering all had to work together.
Detecting a fall reliably from noisy accelerometer data, while keeping the pipeline fast enough that an alert reaches a caregiver when it matters.
Sensor data flows from the C++ firmware to a Python classifier trained on motion patterns; detections post to a Node.js server that pushes alerts to a live dashboard.
C++
Wearable Firmware
ML
Fall Classifier
Live
Alert Dashboard
Reach out for source code walkthroughs or collaboration.