There are 4 main components in the total system.

1. Wearable sensors or monitoring devices

Attached to the patient. To monitor patient heart rate pulse, blood pressure and activities. How many sensors are needed depends on the patient disease.

2. Controller

A controller to collect the data from the monitoring devices and send to cloud server.When the data is exceeding the alert level, it will trigger a notification to the mobile application.It also has buttons to provide alert function to notify the end users (Family Members) at mobile application side.

3. Cloud server

Server to store data and push notify to mobile application.

4. Mobile Application

Mobile application to monitoring patient data and receive notification when patient trigger alert function.

The alert function is useful when the patient is in danger or needs help.

There are 2 cases will trigger the alert.

1. Data collected triggered the alert level
2. Patient push the button manually

When the alert happened, it will send a notification to every end user. For example, when the patient needs family’s help, he can push the button. When server receives the signal, server will push a notification to all the end application users.

After end user received the notification, they need to open the application, and click the resolve button, otherwise, server will continue to push the notification every several minutes, until someone resolve it. If the end user resolves the issue, means he will go to the patient for help.

The project mainly has 3 parts, GR-Peach monitor, AWS(Amazon Cloud Services) and a mobile application.

Part 1 GR-Peach monitor 
We integrated 5 different kinds of sensors on GR-Peach board. The application code for GR-Peach is implemented by C++, make sure the code is easy to extend. You can easily add as many of channels as you can for the same kinds of sensor, as long as the IO port is enough. For every sensor, we will assign a unique sensor ID to it.
The biggest challenge of this part is the integration of AWS SDK. Because of security requirements, AWS only supports TLSv1.1 above. So we use the wolfssl library to create a TLS layer for AWS SDK. We also integrated the SDK to the embed OS, make sure AWS SDK can use the OS resources.
Our solution also supports many GP-Peach monitors work together. Every monitor has a device ID, on the mobile APP side, when the user register the account, it needs to set this device ID, after that, the APP only can receive the topic with the same device ID.

Part 2 AWS
The services we use for AWS are, AWS IoT, Dynamo DB, AWS SNS and Lambda Functions.
We use Dynamo DB to log all the history data to database. AWS SNS is used to send notification to mobile APP and SMS to hand phone. Lambda functions is used to process the alert message to mobile APP.

Part 3 Mobile Application
We made this mobile app to demo all the functionalities we have, including receive real time IoT message, review history data, set alert level, receive alert message, receive emergency message from monitor...


The attached source code is missing one folder, asp-gr_peach_gcc-mbed/asp-1.9.2-utf8/cfg. It is needed for compilation.

/edDir/files/RemoteMedicalMonitor.zip