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The Development of Mobile Health Monitoring Systems

Approx. 21 hours to complete

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Course Summary

This course covers the design and implementation of mobile health monitoring systems, with an emphasis on the use of sensors and wearable devices. You will learn how to develop and deploy mobile health applications, and how to use data analytics to gain insights from the collected data.

Key Learning Points

Learn about the latest trends and technologies in mobile health monitoring systems
Understand the design principles behind mobile health applications
Gain practical experience in developing and deploying mobile health applications
Learn how to use data analytics to gain insights from collected data

Learning Outcomes

Design and develop a mobile health monitoring system
Deploy a mobile health application
Use data analytics to gain insights from collected data

Prerequisites or good to have knowledge before taking this course

Basic knowledge of programming
Familiarity with healthcare and medical terminology

Course Difficulty Level

Intermediate

Course Format

Online Course
Self-paced
Video Lectures
Quizzes and Assignments

Similar Courses

Introduction to Healthcare IoT
Wearable Technology and Mobile Healthcare

Related Education Paths

Notable People in This Field

Dr. Eric Topol
Dr. Kavita Patel

Related Books

Description

This join course created by SPSU and ETU includes 5 modules dedicated to different stages of the system development. Its modules represent several widely separated fields of biomedical engineering. We interconnect them by applying the knowledge from them all to a common task – the development of a prototype of an mHealth ECG system with built-in data-driven signal processing and analysis. Working on this task throughout the course, you will acquire a knowledge on how these branches of science, including electronics, mathematics, data science and programming are applied together in a real project. Pieces of hardware and software, as well as the data sets that we utilize in this course are the same components that we use in our work developing prototypes of devices and algorithms for our tasks in science and engineering.

The course is a joint work of Saint Petersburg State University and Saint Petersburg Electrotechnical University ETU ("LETI"). Note that the goal of the course is not to provide you with fundamental knowledge on any of the topics highlighted in the modules, but to give you some useful skills on implementing them in practical tasks using MATLAB environment (the course requires a licences copy of MATLAB).

Outline

Remote health monitoring system hardware
Introduction
The Structure of Remote Health Monitoring Systems
Problems Related to Hardware Development
Typical Scheme of an ECG Recording Channel
The Basics of Analog-to-Digital Conversion. Types of ADC
AFE Implementation for ECG Registration
Implementation of an AFE Evaluation Board
Basics of Arduino Platform Programming
A Simple Program for the Arduino Board
Arduino Peripheral Devices (UART, ADC, GPIO)
Programming an Arduino ADC
Basics of remote monitoring systems design
Basics of Analog-to-digital convertion
Basics of Electrocardiography
Basics of Arduino
Remote health monitoring system hardware

Data Exchange Between Device And Personal Computer
Serial Interface
COM-Port Setup in MATLAB
Package Data Transferring
Connecting Arduino Via Virtual COM-Port
Working with Arduino Hardware Support Package for MATLAB
Plotting Real-Time Data with Arduino Hardware Package
Saving Data from Arduino Virtual COM-Port
Serial communication basics
Serial communication in MATLAB
New Reading
Data Exchange Basics

Preprocessing of Biomedical Signals
Measurement System Chain
Noise in Biomedical Signals
Spectral Analysis Basics. The Fourier Transform
Alternative Spectral Estimation
Spectral Analysis in MATLAB
Digital Filters
Transfer Function
Filter Design Using MATLAB
Programming assignment, additional explanation
Spectral Estimation Approaches
Digital Filters
Biomedical signal preprocessing

Event Detection in Biomedical Signals
Problem Statement
Feature Extraction Based on Signal Spectral Characteristics
Statistical Signal Processing Methods
Feature Extraction Using MATLAB
Peak Detection Algorithms
Simple Derivative-Based Algorithm for QRS Detection
Peak Detector in MATLAB
Feature Engineering in Accordance with the Task
Feature Engineering Example for an ECG Signal
Results Visualization and Analysis
Feature Extraction
Peak Detection Algorithms
Multi-Dimensional Feature Space
Event detection in biomedical signals

Developing Data-Driven Recommendation System
Exploration of Data Sample
Statistical Metrics and Sample Data Visualization
Analysis of Variance
Creating Data-Driven Models
Logistic Regression
Model Performance Evaluation
Improving Model Performance
Real-Time Data Flow Simulation
Practical Implementation of a Data-Driven Recommendation System
Extracting Diagnostic Features
Classification of Events
Developing data-driven evaluation systems

Summary of User Reviews

Discover the latest advancements in mobile health monitoring systems with Coursera's course. Users have praised the course for its comprehensive coverage of the topic, giving it a high overall rating. One key aspect that many users thought was good is the course's practical approach to learning.

Pros from User Reviews

Comprehensive coverage of the topic
Practical approach to learning
Engaging course materials
Interactive quizzes and assessments
Great value for the price

Cons from User Reviews

Some users found the course too basic
Lack of hands-on experience
Limited interaction with the instructor
Course content not updated frequently enough
Lengthy lectures

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