Skip to content

Latest commit

 

History

History
90 lines (65 loc) · 3.03 KB

README.md

File metadata and controls

90 lines (65 loc) · 3.03 KB
Raw

Ecare

Main Repository for the ECare graduation project.

ECare is the gradution project of 5 graduating seniors from the American University in Cairo

Vision

E-care is a system that continuously monitors elderly people’s health and wellbeing through smartwatches. The system mainly focuses on Fall Detection and Indoor Localization. Moreover, the application also aims to facilitate the communication between the elderly and their caregivers by having a communication channel and providing real-time health data and reports.

ECare Promo

Please Click here to view ECare's promo

Final presentation

Click here to view the presentation video of ECare

System Hardware Requirements

  • Smart phone
  • Fitbit Smartwatch
  • ESP32 for IPS

System Architecture

How to use the system

System Users

The system has two types of users:

  • Patient
    • The patients are Elderly that the system is aimed to support
    • The patients are the users who will use the Smartwatch
    • The Patients have to have a fitbit account
  • Caregiver
    • Caregivers can include personal Doctors, Caregivers and Family members
    • All Caregivers have the same functionality, as they all use the system to communicate with the Patients and view their health data

System Features

  • Health Tracking:
    • Heart Rate
    • Skin Temperature
    • Step Count and Calories Burnt
    • Sleeping Patterns View How we got the health tracking via backend here
  • Fall Detection
    • Developed our own dataset for Fall Detection
    • Developed a machine learning model for Fall Detection For more details about fall detection click here
  • Indoor Positioning System for well-being monitoring For more details about indoor poisitioning system from the ESP32 side click here
  • Communication Channel between patient and caregiver This can be viewed from our frontend applications using Android and iOS

Development Tools

For the development tools we used:

  • Android Studio (Using Java)
    • For Android development
  • xCode (using Swift)
    • For iOS development
  • Arduino IDE
    • For the development on the ESP32 for the IPS
  • Google Platform
    • To deploy our backend on google's cloud server
  • Fitbit studio (Node.js)
    • To develop two applications
      • Data collection for the dataset
      • Clock face for fall detection
  • Firebase (Firestore, Realtime Database and Authentication)
    • Firestore is our main database