CompletedHealthcare & Medical AIRobotics & Edge / Embedded

Wearable Health Monitoring & Fall Detection System

A wearable that fuses vital-sign monitoring (temp, HR, SpO2, stress) with MPU6050 fall detection, GPS, and SMS/Wi-Fi alerts — 90% fall-detection accuracy.

Watch the story
Arduino-based circuit schematic of the wearable health-monitoring and fall-detection device (sensors, GSM, GPS, power)
Visual story

Fall or vital-sign alert

Scroll to watch it unfold. Each beat is one documented step of the project.

  1. 01

    Monitoring

    The wearable tracks movement with an MPU6050 motion sensor.

  2. 02

    Fall pattern

    A sudden movement and impact match a fall.

  3. 03

    Locate

    GPS adds the wearer's position.

  4. 04

    Alert

    An emergency alert goes out by SMS and Wi-Fi.

  5. 05

    Outcome

    An alert with location goes out without the wearer pressing anything.

  6. 06

    Vital signs

    Abnormal readings raise an alert in real time.

An illustration of how the system works, drawn from the write-up below. It is not recorded output.

Overview

About one in four adults over 65 falls each year, and most falls go unreported to a healthcare provider. Existing monitors are either fixed in place or need the user to press a button, which fails exactly when someone is incapacitated — and prolonged immobility after a fall ('long lies') is independently linked to hypothermia and higher mortality. This CSE360 team project (Reshad Ul Karim, Syeda Maliha Tabassum, Sammam Mahdi, Abrar Samin) builds a single wearable that continuously tracks vital signs and detects falls, then alerts both locally and remotely with no manual step required.

Approach

A dual-Arduino master–slave split (Nano as master, Uno as slave) was the fix for a real constraint discovered during the build — a single Nano ran out of memory once sensors, GPS, GSM and a display were all wired in, so the Uno was carved off to own the OLED over I²C while the Nano handles sensing and communication. Four sensors feed it: LM35 (temperature), MAX30102 (heart rate/SpO2), a GSR sensor (skin-conductance stress), and an MPU6050 accelerometer/gyroscope for motion. Fall detection runs as a small state machine — IDLE → DESCENT → IMPACT → POST_IMPACT — that only confirms a fall after a sharp downward velocity, a high-g impact spike, and a period of post-impact stillness, which is what keeps a fast sit-down or a jump from tripping a false alarm. Confirmed falls and vital-sign anomalies trigger a buzzer/LED locally and an SMS with live GPS coordinates via a SIM800L module remotely, alongside a Wi-Fi JSON feed to a local server.

Results & engineering

In simulated trials the system caught 45 of 50 falls (90% accuracy), with sensor error within clinically acceptable margins — temperature ±0.5°C, SpO2 ±2%, heart rate ±3 bpm. SMS alerts landed within 5–7 seconds of trigger, and GPS acquired a satellite lock in 35–60 seconds outdoors, accurate to 3–5 m (indoor lock was unreliable, a known limit of low-cost GPS receivers). Getting there meant solving real integration problems along the way: GSM and GPS sharing one supply caused voltage instability until a buck converter regulated it, the Nano's single hardware UART couldn't serve GPS and GSM at once until SoftwareSerial/AltSoftSerial split the load, and I²C address clashes were resolved by giving the OLED its own dedicated slave MCU. Built for about 5,800 BDT in components.

By the numbers

90%Fall Detection Accuracy

Highlights

  • Elderly Care
  • Real-time Alerts
  • Multi-sensor Fusion

Tech stack & key skills

Core tools, methods and skills demonstrated in this project:

Arduino / Embedded CIoT sensors (MPU6050)GPS / GSMSensor fusionFall detectionReal-time SMS/Wi-Fi alerts