Hello, everyone. Welcome to Capstone two, the remote healthcare monitoring system. Let us begin with a description or actually an overview. So, we have the hardware requirements first, and then we have the description after that, and then we'll describe the major components of the system, how it's implemented and after that, we'll show a demo of the project itself. So here, we have hardware requirements. The only thing that you'll need is a computer and the dragonboard. The computer will act as the interface to the website that we'll be connecting to, and the dragon board will act as our medical device that we will be remotely monitoring. And then we'll go to the description. Basically, it demonstrates how the cloud can connect the patient and doctor instantaneously, allowing for near real time analysis of data using cloud resources and feedback through normal communication channels. What that means is we're going to be using the dragonboard as a simulated medical device like a heart rate monitor, and it will stream its data to the cloud which is Amazon Web Services. And there, we can store the data, we can analyze the data, and then we can act on that data by sending a text or an e-mail to the doctor or the patient which is the feedback through normal communication channels. And the real time analysis is checking those anomalies in the heart rate data, which could possibly be fatal or not actually fatal and just something that's just an irregularity but not serious. Here, we have the WaveForm Database. That is the data that will be streaming to AWS. It comes from physionet.org, which is a massive database for medical data. A good thing about this data is that it has annotations of the waveforms, meaning that at certain points in the stream of like heart rate data, you can see where doctors have labeled where there is an anomaly or an arrhythmia. Another thing is that the waveform database is also really good because it has this thing called Wave, which lets you visualize the ECG data as you can see here in the picture. The picture itself is actually lightwave, which is the javascript version, so that you can be used on our web browser. So we'll be using Amazon Web Services which is a major player in cloud computing, and it is used by companies such as Coursera, Yelp, and many more. We will use it to handle IoT communication, which is the dragonboard to the server, database management which is storing that data, and the Java application hosting, which is the lightwave server, lightwave application that we'll be showing. You will be learning how to do all these things in course three which will focus on the cloud and how to use it. Here's the implementation for the entire system itself. At the top, you can see the dragonboard which will be the quote unquote ECG device, and it will send data to the AWS IoT, STK which is just a message broker and it allows us to do various things such as send it to the web server and database. So it just sends it to the database stores that there, which is DNO DB. It also sends it to the EC2 server, which will serve a web application using javascript to show and visualize the data and interact with the system. So as you can see here, it sends it to the front using HTTP and a lightwave front end. So as the data's streaming, if there's no problems connected, it continually visualizes data and just keeps storing it. But if it sees a problem, such as a flag for an arrhythmia, it will send a notification to the doctor and the patient in the form of an email and text. And then from there, the doctor can simply just click on a link wherever he is, and then go to that exact point in the data to see where the problem is, and see for himself if he wants to act on it. Either tell the patient that's a serious problem and they should do something, or just tell them it was nothing and they don't have to worry about it. So from here, we'll go to the dragonboard and run the simulated device streaming to AWS. We'll just be reading a data file and then sending it to AWS. So, just go to the dragonboard, and on the dragonboard, we can read and see that it's the directory for the project itself. It has all the files, even the files that will be running on the web server. But we'll be focusing on the device.py file and just running that. And when we run that, it will start to stream to AWS all the data that we have stored here. And then we can go to my computer and then we can access the web server itself. So here is the application itself. This is nicely created by physionet, all of this front end. I did edit it and add some features specifically for this project. Everything else here is still the same. We're even using data from their database. This section here is different, I added heart rate 77, and major issue, and all clear, that's for the doctor to use and to notify the patient with. So here, we can see the data and we can start streaming the data, pressing play, and you can see here, this little A, that stands for arrhythmia and once it passes center, which is what we're measuring or checking the data for. As you can see here, we'll go to this tab and we'll see if we have an e-mail from AWS certification message, and it says, "Hello, Hari. We've detected a possible problem with your heart. Doctor Watson will notify you if you need take further action." So I just named our patient and doctor. So our patient's Hari, one of our instructors, and our doctor's just Dr. Watson, just because of the IBM Watson. And then here's the message for the doctor, for Dr. Watson. It says, "Hello, Dr. Watson. On your patient Harinath's ECG signal, we have detected a possible arrhythmia. To see the problem in that area, click on the following link." And as you can see here, it takes us directly in the waveform where it detected the problem. This looks exactly where we started on here, but that's just because we jumped on the waveform to the specific point, so that we could easily go for the demo. But normally, this would start at zero and it would just take a whole four minutes to get to the point. So here, it drops us exactly where you want to be. And just a quick note, it also sends a text message, and we'll be showing that on the device. So we can let it zoom in a bit. And you can see here at top, having the doctor's message and the patient's message sent to my phone because I don't have two phones. At the top, you can see it says Dr. Watson the same message and the link, so I can easily just click that and go to the website. And then the bottom is the message for the patient. So if I go back to the website itself on my computer, we can see here. So after the doctor has received the notification through the e-mail or the text, he can go to this link and then either look at this data, see if it's actually a problem. Like maybe this is too close to this and say, "Okay, it is a problem." So when the patient clicks major issue, and then that should send an email and a text to the patient. So you can see here, it just sent a text saying EMERGENCY in big capital letters, saying that Dr. Watson has determined that there is a serious heart. So this is really nice in the fact that you don't have to download a specific app or anything. You just give them your phone number, and then you'd just get a text on, which is something you normally interact with on a daily basis anyways. In addition, you also get an e-mail. Let's check here. It is oops! Emergency as seen. And then let's say, this is just nothing out of the ordinary and it's just not a problem. We can also just click all clear, telling the patient that they don't need to worry anymore and nothing is a problem. So we can see there's a new message here. Oops! It says, "Don't worry. Doctor Watson didn't see any issue. Have a great day." We can also go to my phone and then see that same message. See, there is two messages at the bottom and the last one is don't worry and the one on top has Emergency from before. So there we have it. This is the capstone two. This is just showing the pipeline for if you had an IoT medical device, and you wanted to actually use it in the real world. This is an application or a feature you can add to it to make it more user friendly, and just overall better to use. So take this as your inspiration and think about what you want to build for your own capstone.
