I'm My name is Kunal Katke, and I'm here with my coworker Nate today .
We'll also discuss how to
use IoT data for healthcare research.
o begin, I'll go over the quick session plan, which will focus on IoT in healthcare.
Consider some of the issues you can encounter if you use IoT.
Then we'll go over a few instances of how you can utilize it.
Through a full-
To begin, what is IoT in healthcare, we refer to it?
I'm T, which stands for Internet of Medical Things, so consider this a subset of medical devices that deal with it.
Patient information, to be precise.
Many of you are aware with gadgets like Fitbit and a variety of forms and sizes and may be worn.
That somebody might be able to help.
What happened to all the time?
They're also keeping an eye on things.
There's also an ambient device category, which includes sensors found in places like a hospital bed or a monitoring room.
They can collect medical readings or vital signs, or they report on the behavior of patients.
And there is a class of gadgets that may be employed, such as injectable pills and other similar devices.
So, what are a few of the most important scenarios?
We think pillars, the first of which is patient monitoring.
So, where do sensors come into play?
a Piero's, which stands for patient reported outcomes and refers to assessments of a patient's willpower.
To contribute subjective input form.
And this is useful in a lot of distant care situations.
Another pillar we've noticed is in research and life sciences, particularly in the area of clinical trials.
There's a lab, there's data, there's analytics, and there's
Finally, there is a subcategory of smart hospitals.
We're not going to give this presentation our full attention.
But I simply wanted to let folks know that it exists.
Another point they make is the usage of IoT and IOT terms.
The number of devices used in healthcare is continually increasing.
It's expected to rise to 340 billion dollars by the end of 2025, making it an attractive and rapidly expanding market.
So, now that we've covered obstacles to deploying an IoT solution?
So, if you're planning to construct your own IMT solution, there are a few things you should keep in mind.
The first is high-frequency data intake.
gadgets aren't all the same.
Some of them may only be read once a day, while many others can generate data at a rapid rate.
every second or fraction of a second
As a result, if you wish to interface with that type of device, you'll need to account for it.
You're also looking for a low latency experience.
You need to be able to get or consume data and make it accessible.
as quickly as possible
To relevant, you must additionally link record.
lot of gadgets in the ecosystem, thus there aren't any.
There is a lot of standardization out there, but each company reports data in its own way.
Interoperability is another important factor to consider, particularly in the field of healthcare data here.
We're aiming for fire here, which is a healthy open healthcare norm.
This is gaining traction in the neighborhood.
It also allows us to share data between hospital systems.
You must also consider privacy and security concerns.
What are you going to do as a result of them, and what are you going to do as a result of
What should the data coming from the device has to be updated?
So, now that we've gone over the main issues, what are some of them?
What are the difficulties you'll face, particularly if you're developing your own solution?
Late - arriving most important.
That necessitates the use of
An internet connection, such as a smartphone, might gather data for hours or even days at a time, and then that data will all be transferred so you must be able to manage that burst of data and then make sense of it.
correctly and in conjunction with the suitable time period.
Similarly, there is no standard for how data can be sent from the device.
If it's been offline for a while, it could send the most current data first.
And then go backwards in time.
It might also be sent out, based on the most recent statistics.
And there's no specific sequence, so any solution you come up with to take that into consideration.
There's also the issue of duplication to contend with.
Because it was in the process data.
It shattered the connection.
UPS did not acknowledge receipt of a certain piece of data, yet it was received successfully.
Yes, to be able to deal with data ties to patients and.
There's this as well.
There is a conflict between balancing latency and system load.
So, as soon as feasible.
However, if you go with near real
time or real
-time processing, it adds value.
On the back end, there are certain difficulties.
There are other deliver or represent data streams in a variety of ways.
There isn't much in the way of standardization.
And dealing with the influx of resources brought on by fire.
In the case of fire, sources might be thought of as entities.
When dealing with health data particular to a patient, we call them observations.
So, if I have a device that broadcasts health or heart rate data every second and I'm making a separate observation for each of them, that might offer some issues in terms of enumerating or retrieving that data.
Rewinding a patient's days.
Thousands of of data.
So we've had numerous prototypes of partners throughout the years.
go over some of the things you've learnt thus far.
thing are we talking about?
We've made advantage of it.
When we designed their solution, we used the following concepts as a guide.
One of the points I made in the previous slide was that single value observations are insufficient, therefore.
We don't simply want to save or have it; we want to have the choice of saving that heart rate data in one place.
Thankfully, fire has done so.
Sample is a time series format.
data basically categorize collections.
Describe an observation that may last an hour.
Then, throughout that heart rate would be reflected inside one observation.
One of the first places we visited when was.
What kind of content may we put on the device? you?
How do we standardize and have something that is fundamental?
any sort of programming that would run on the device to deal with it?
the recording of measurements and their transmission to the cloud?
That caused a number of issues, so we decided to try to make the solution device agnostic and, in essence, allow.
In any case, we'd want to obtain the data.
Devices and intricacy are two of the issues.
They have a wide range of similarities.
So I have a lot of processing power, but only a little in. A little in, too, if you're doing everything on the device
entry introduces them.
The the person who writes it.
There may be a buffering interval on the gateway.
That's OK if they to.
have a built
There was buil
-in delay. Then code portability to each device, which runs on its own OS or firmware version, resulting in an A1 size on the device. problematic.
Another thing to consider is that fire is a constantly changing standard.
were conducting the conversion and collecting into fire on the assure.
If we wish to upgrade the version of fire, we must do so.
That is when we must ensure that all of the been updated.
So, if we do it on the cloud, we can decouple a little bit.
Those are fascinating facts.
When we get into these. values, there is a moment of conflict.
Data from a sample.
Because some of these huge, and we wanted to keep it to a minimum.
So that's how came up with.
As a result, both are included in the OSS version.
The Azure IMT Fire connection for Azure, as well as a pass option, are now in public preview on Azure.
The allows you to
You have complete control over the code, so if you want to take the fundamental building blocks and modify meet your unique case, you may.
However, if you'd want to have a one
stop shop and use it,
you may use the solution we designed.
I recommend checking out the Azure IoT Connector for Fire.
So, what exactly is the IMT?
As a result, we provide a high
-frequency IMT data processing endpoint.
We analyze mixeddata payloads non-
fire format and merging it into a common intermediate format.
The data is then divided into groups based on several attributes.
Subsequently we do the transform phase, which converts the fire observations into fire observations, which is then stored in the target fire server.
So let's have one.
I'll go through everything in greater depth to illustrate what's going on.
So, in the normalization stage, we payloads.
Heart rate is abbreviated as HR.
Consider these three devices to the cloud.
As on a basic example heart rate.
59 beats per minute. 88 beats per minute.
It's only a guess.
As a result, devices frequently send out essentially the same message with varied properties for multiple vital indicators.
This is wonderful for the device because you're generally sampling them at the same frequency and transmitting one message, but it's not so great for processing and storage.
You spend a lot of time where your heart rate isn't particularly high.
set fire to
So, are there any normalization procedures to do this?
Is it also in favor of the forecast?
set it up so that can be saved as independent observations and fires whenever we come to that point.
And what's the name of the band?
We have data sorted
So consider it this way.
Someone configures something similar to a semantic type.
The system monitors heart rate, step count, blood pressure, and other parameters.
effectively controlled by this.
One of the possibilities is latency.
This is customizable, however
However, it will be something that up.
As a result, this determines how frequently data is egressed from the connection into fire.
So, if you reduce it, you'll receive data into fire faster, but you'll also potentially raise the stress on your fire server.
So, depending on your use case, you might want to keep it small because you'll be doing some serious labor.
For analytical reasons, you can choose a greater value.
If it's currently available on the fire server, use it.
Finally, there's the fire conversion.
We have a variety of alternatives for you to choose from.
So, if you're utilizing the time series structure as I specified, you define the period.
As such, you may say.
This should be bucketed by hour, so that the connection can uploaded.
We'll be able our system and integrate
where you would get help for diverse codes.
data, define systems.
Because the value.
We handle it for you by translating the data to the appropriate field.
We make observations based on the time they were recorded as being noticed, as being observed.
It's a deterministic identifier, so it'll keep track of data as it comes in.
If data for the
That will need to be changed later, and we'll be able to spot it.
and then everything else will be updated.
We relate the observations we make to both the patient and the equipment.
There is a concept of components, not just single value observations.
You have a blood pressure and observation, and you can have components for diastolic and systolic blood pressure, as an example.
Here's a simple illustration of fire. observation that make.
We may include things like the internal ID and resource type here.
Coding is right around the corner.
For heart rate, we have a link code.
It's merely a value quantity.
I'm going to walk you through some of the configuration stages now.
We have an idea steps.
So, here's how it would look:
I've got a sample payload from a device we're working on.
a large number of distinct signals are being captured
as well as other attributes regarding the measurement's date and time of recording
Anna is the device ID.
Configuring while utilizing the system.
t will be able to recognize and match this, so some crucial.
We have a template that is intended to map the pieces here.
This is a sort of heart rate that is semantic.
Then we use this type match phrase to do that.
In other words, if this evaluates to true, we have.
As a message, it has been identified.
After that, you'll get a normalized value as a result.
On the fire mapping side, a collection required.
Combine it with a template in which we map depending on the semantic type defined by the system's configuration.
Then utilize some more characteristics to express yourself.
We want the time interval 0. meaning so we'll just construct it as is.
The code we wish to associate with it, as well as how we extract and display the values, are all on fire.
And this is what you'd get as a result.
You might be wondering how to go about it.
The Iot connection is used.
As part of a bigger system, here's an example of how we envision the connection being utilized.
So we have the side.
Where data is coming may use a device gateway, one of our Azure Iot solutions like Iot Central Iot Hub, or go through a phone gateway and connect Iot. connector.
The data is that you can use to retrieve the data as it comes in.
You may also use this to feed data Once there, you can utilize it in a variety of apps.
Similarly, if you wanted to perform analytics, once it's in the Azure API for Fire, in multiple Azure services.
With that, I'll turn it over to Nate to begin the demonstration.
I'll get my screen down here, honey.
Is it possible for you to view my computer screen?
I'm sure I can.
Please direct me to this location.
So there you have it.
It's a demonstration of remote monitoring.
It works fine; I'm on the correct screen now.
Also, thank you to everyone who came.
So here's the demonstration I'm going to show you today.
It was really simple to build
It didn't even need developing any more code; all it took was configuring the various components.
So, first and foremost, I'm going to take a minute to walk through.
a some of the elements in this demo
As a result, the Azure API for fire complies with HIPAA regulations and.
It's also idolized, so it's safe to use.
Protected health information is stored in a secure manner.
I activated and established the mappings to process the device data that we'll be delivering.
So Kunal spoke about the device mappings that are used to
will get into the details of how this conversion works.
data taken from a real device
So I've got a gadget here.
I'm going to keep it.
It's a eyes.
It's only a store
I believe it was around $40.
I'm also screen, and you can see that I installed the eye health app.
Is the orange indicator labeled "I" health, and when should I use
So, I'd want to take a moment to discuss healthcare.
On the iPhone, you can get healthcare.
The iPhone has a health kit, and this information may be safely shared with third - party apps.
Our team took use of this feature and created the
Healthcare on Fire to the IoT connection.
This library, as well as documentation, is accessible on GitHub.
A fast start tutorial is included, as well as on an iPhone for testing and review.
You can see that I deployed the example app for this demonstration.
I set up the IoT connection endpoint custom I built.
In addition, I built an Azure client with a represent myself.
Saving the application.
exported to the Iot connection, processed, and saved
So, after the data is collected, we may put it to good use.
and here's what it looks like.
As made a
It's a crude dashboard that replicates a situation in which a physician is monitoring blood auction saturation for a group of patients.
The measurements obtained today are on bottom.
My rose has a golden accent.
This indicates that the measurement was not taken today and that it is less than 95 percent accurate.
So, as is the case with Grace Owens.
the patients on this list are fake.
All of their info is made up.
So, let's do a fast measurement to see what occurs.
So the first thing I need to do is start developing an eye health app.
So we'll go ahead and start that, and then I'll turn on the gadget.
The is measured using this device.
When I take the gadget and creates a single blood oxygen saturation measurement.
That will be included in the health kit.
As a result, I was able to pull it off.
The figure is 98 percent.
My heart rate is 128 beats per minute.
That's a lot of money.
As a consequence, the on
-care system notices
the new data right away and uploads it to the connection, which kicks off the normalization process.
It extracts the device identity that I'm measuring, as well as measurement data itself.
At this point, I'm going to have a look under the hood to see what's going on.
So, here's how the payload from the healthcare on fire looks.
The measurement may be found at the top of the page, along with the timestamp and device ID, or it can be found here.
Kunal also discussed the two mapping files that aid in the transformation of any Jason payload into fire.
The first mapping, shown on the right, was used to standardize Jason's data into a format that the IoT connection could understand.
To decide expression.
We we've found a match.
This will be utilized to construct the fire observation.
To find the extract, use
The time stamps are added so that we can determine the date and time of the measurements.
We'll retrieve the device ID so resource.
The calculation has finished.
To obtain the
The IoT connection uses this data item internally.
As a result, we devices, each of which may have a distinct data format.
Now that the data has been standardized, we may organize the data if necessary.
We don't need to group in this case because it's a single measurement, although grouping is beneficial.
It is anticipated that it will be broadcasted often.
It's beneficial because, the amount of observations generated.
Now we'll generate
So I'll get right in and show you how it's done.
This is an example of the second mapping, JSON, which identify and extract data.
So the first stage.
This fire mapping should be applied to the oxygen saturation type, and type.
We may begin the process of making
As a result, we determined the sort of value that will be included in the observation.
As you can see, this observation will have a value quantity, which means may be utilized for single measurements like the one we're doing now.
There are, however, additional sorts.
Values and sample data, which may be utilized for streaming, followed by string values and codable types, I believe Kunal indicated.
As a result, we support a variety of quantity kinds.
So we find them in the normalized data, in this example,
We then add it to the observation resource and as a result.
Because the observation value represents a quantity, it is changed from a string to a number if it is normalized, as you can see.
We just write the number exactly as it is.
from the fire mapping to the observation, ensuring that the value is appropriately coded.