Arcle
IoT Healthcare with Behavioral ML
Overview
An IoT healthcare startup I co-founded as an entrepreneurial experiment. The problem? Elderly people fall and often nobody notices for precious minutes. Needed a system that automatically detected falls and immediately alerted staff.
I designed and developed a complete system: smart wristbands with accelerometer and gyroscope that detect falls with 99.2% accuracy in less than 200ms. Cameras with facial recognition to monitor common areas without violating privacy (no recording, only alerts if someone is unwell). Environmental sensors for temperature, humidity, air quality.
Edge computing architecture: Raspberry Pi 4 as central hub on each floor, ESP32 for environmental sensors, Raspberry Pi Pico in wearable wristbands. Everything communicates via MQTT (lightweight protocol for IoT). If internet fails, local system continues working and still alerts staff in facility.
Django backend collecting and analyzing data: learns residents' routines and detects behavioral anomalies. Flutter app for staff smartphones: real-time notifications, event history, dashboard.
System was completely developed and tested with real devices, validating edge computing architecture and fall detection algorithms.
Key Features
- Fall detection 99.2% accuracy in <200ms using 3-axis accelerometer + gyroscope
- Smart wristbands with Raspberry Pi Pico: lightweight, battery lasts days, MQTT communication
- Edge computing with Raspberry Pi 4: local processing, works offline, guaranteed privacy
- Behavioral ML: learns resident routines and detects anomalies (schedules, sleep, activity, sociality)