Speaker 0: There will be others joining us in just a little
Speaker 0: bit.
Speaker 0: It's my pleasure to introduce Dr. Paying to us speaking
Speaker 0: today.
Speaker 0: Dr. Peng is an associate professor of electrical and computer
Speaker 0: engineering at UNLV.
Speaker 0: He introduced me to a word I didn't know before.
Speaker 0: Steganography, which is not stenography.
Speaker 0: It's the practice of concealing a message within a message.
Speaker 0: And his work seems to be along the areas of
Speaker 0: wireless technology, data security and efficiency of data transmission.
Speaker 0: So, Dr. Pang, welcome and thank you.
Speaker 1: I think you'd out the window for an introduction.
Speaker 1: So I think I guess I can start to share
Speaker 1: my screen.
Speaker 1: And they can talk about this.
Speaker 1: The idea of the work.
Speaker 1: They have been doing.
Speaker 1: It is still a kind of a preliminary stage of
Speaker 1: the work, even though I have been working with general
Speaker 1: security and information security for a long time now, especially
Speaker 1: against the iconography, you know, the hiding data into the
Speaker 1: multimedia, like digital pictures, things like that.
Speaker 1: So I'll just go ahead and start present this.
Speaker 1: Also thanks to my collaborator, Professor Hamid Cherif.
Speaker 1: He is in the same department as I am and
Speaker 1: to his knowledge about your fellow.
Speaker 1: Also, I work with Dr. Jason King at the U.A.E.,
Speaker 1: who is a cardiologist.
Speaker 1: Now, the topic of the work that I want to
Speaker 1: present today is additional computing and security with Embedded Processing
Speaker 1: for Wearable Sensor Network.
Speaker 1: Here have the proposed system architecture with three layers.
Speaker 1: I would just briefly talk about there's a lot of
Speaker 1: details here, but I can come back and address the
Speaker 1: details.
Speaker 1: But they were to view a bigger picture as a
Speaker 1: three layer, the local layer after layer and a magical
Speaker 1: layer with this city.
Speaker 1: So the objective here is trying to come up with
Speaker 1: something that can have 24 seven continuous monitoring, not only
Speaker 1: monitoring, but also your real time diagnosis with that can
Speaker 1: be accomplished by the collaboration between the three layers as
Speaker 1: some simple example of the local layer could be like
Speaker 1: a wearable sensor, not like a Fitbit.
Speaker 1: Make an Apple Watch, like a special EKG sensor we
Speaker 1: can put on our test and layer could be like
Speaker 1: our smartphones could be home station, like laptop computers, medical
Speaker 1: layer would be the cloud supercomputing center, things like that
Speaker 1: that is directly controlled or related to the hospital system.
Speaker 1: And then the workflow of the user, three layers.
Speaker 1: Basically the original data come from the sensor.
Speaker 1: And then through all these security measure and also the
Speaker 1: onboard computing, they can detect to some extent applying the
Speaker 1: AI algorithms that can attack some, detect some problems.
Speaker 1: And then based on that result of that detection, we
Speaker 1: can do things that we'll talk about some of these
Speaker 1: algorithms in the next few slides.
Speaker 1: So that's the local layer.
Speaker 1: And then the data will be communicated with and layer
Speaker 1: and then eventually to the medical layer.
Speaker 1: Now in this work, we especially focus on the two
Speaker 1: aspects.
Speaker 1: One is the we proposed as a spa steganography that
Speaker 1: is blended with a symmetric cryptography that can help provide
Speaker 1: the security protection for this data that's coming back and
Speaker 1: forth between the local device and Android device and medical
Speaker 1: server.
Speaker 1: And the second aspect is about these different communication modes
Speaker 1: as a protocol between these different layers and these communication
Speaker 1: modes could be adjusted or dynamically switched based upon these
Speaker 1: diagnosis results.
Speaker 1: No, just a little bit of background.
Speaker 1: So take a step back and talk about them again.
Speaker 1: The scope and the motivation of our work basically are
Speaker 1: three components.
Speaker 1: First is, again, the real time monitoring and the diagnosis.
Speaker 1: That is based upon the collaboration between cloud computing for
Speaker 1: computing and computing and also the local computing.
Speaker 1: And then the second component is there's a partitioned the
Speaker 1: algorithms.
Speaker 1: And the third component is the security protection.
Speaker 1: Now the background or related work involve two of four
Speaker 1: areas with the two focus now that existing state of
Speaker 1: the art and related technologies include.
Speaker 1: We have seen a lot of new to smartphone based
Speaker 1: mobile house apps that can do something like what you
Speaker 1: are proposing to do, but just not like in real
Speaker 1: time.
Speaker 1: They have to do it like they recorded the usage
Speaker 1: data, for example, and then after recording, then be uploaded
Speaker 1: to somewhere and then perform analysis and then give the
Speaker 1: diagnosis results.
Speaker 1: So the real time performance, real time computing will be
Speaker 1: a challenge for the first bullet here, and that's taking
Speaker 1: the bullet.
Speaker 1: There's a lot of the AI algorithms regarding analyzing any
Speaker 1: signals or anything for at least a 20 or 30
Speaker 1: years, if not 40 years already up to now.
Speaker 1: But there there's a big gap here is usually these
Speaker 1: air rhythms are developed under this, a software environment without
Speaker 1: too much consideration with the hardware platforms like what I
Speaker 1: said, those are three layers between the ads layer, the
Speaker 1: local layer and medical layer.
Speaker 1: So how do we actually partition these API rhythms into
Speaker 1: that kind of distribute in the system and in the
Speaker 1: meantime also protect the data integrity, data privacy.
Speaker 1: So that's and no, that's what we want to address.
Speaker 1: My third bullet is the security mechanism, including the of
Speaker 1: traditional cryptography and also these steganography and finally, blockchain system
Speaker 1: for being information sharing.
Speaker 1: Now, blockchain has been research a lot, but not much
Speaker 1: in this kind of setting with edge computing and also
Speaker 1: the local computing environments.
Speaker 1: So that's the background.
Speaker 1: Please stop me if there's any question, there's any interesting
Speaker 1: point that we can discuss or anything I need to
Speaker 1: clarify.
Speaker 1: Otherwise I would just go out.
Speaker 1: So those three layers, the local layer, the added layer
Speaker 1: and medical layer again locally or corresponds to the wearable
Speaker 1: sensors and that edge layer corresponds to like a.
Speaker 1: And then the medical layer corresponds to the cloud servers.
Speaker 1: So we have these three categories of the algorithms designed
Speaker 1: for each layer.
Speaker 1: So the first category is the algorithm that's going to
Speaker 1: be performed by the local layer, which is very small
Speaker 1: hardware and run by a battery.
Speaker 1: The high level source is very limited and on a
Speaker 1: hands these days with the powerful microchips, these are small
Speaker 1: wearable, even disposable sensor nodes can actually have to some
Speaker 1: extent some amazing computing power.
Speaker 1: All right.
Speaker 1: So anyway, so that's the first kind of.
Speaker 1: Right.
Speaker 1: Now, this algorithm along here has several elements.
Speaker 1: The first element we call 1.1.
Speaker 1: Now I'm here when I here is that the abnormality
Speaker 1: check.
Speaker 1: So the sensor receives the signal and then I'm easily
Speaker 1: performed based on board computing using this.
Speaker 1: Our peak detection in the camera's complex and using some
Speaker 1: two dimensional convolutional neural network, basically deep learning algorithms.
Speaker 1: Neural network, artificial neural network algorithms to basically differentiate the
Speaker 1: signal between how they data and abnormal data.
Speaker 1: Now, these are two decision as a very, very important
Speaker 1: topic.
Speaker 1: Usually they run on powerful computing platforms, not in the
Speaker 1: like a just like a you know, like a like
Speaker 1: a quarter a coin, you know, like the small size
Speaker 1: sense or not.
Speaker 1: The reason is because, of course, of the training of
Speaker 1: these and your network algorithms will take a lot of
Speaker 1: resources, computing resources, computing power, also data storage.
Speaker 1: But we here talk about some kind of inference logic,
Speaker 1: and that could be executable locally.
Speaker 1: So anyway, so that's the algorithm, 1.1.
Speaker 1: Algorithm.
Speaker 1: 1.2 is basically to encrypt the patient's identity information.
Speaker 1: And get ready for hiding that I then encrypted identity
Speaker 1: information into the data.
Speaker 1: So now easy data will be used as the carrier
Speaker 1: and then recover and then the ID information ID data.
Speaker 1: Patient I.D. data will be the secret message that has
Speaker 1: embedded into this subject.
Speaker 1: Any questions?
Speaker 1: Any.
Speaker 1: Any discussion?
Speaker 1: Please.
Speaker 1: Just tell me if there's any interest.
Speaker 1: Okay.
Speaker 1: So here we have so kind of sparse.
Speaker 1: We proposed this sparse steganography.
Speaker 1: Simply speaking, because pitching it, we really don't need to
Speaker 1: hide a lot of the secret message.
Speaker 1: All we need is basically bundle the users I.D. data.
Speaker 1: With that users usage signal so that after this information,
Speaker 1: these signal have been send it to like an added
Speaker 1: layer or cloud layer.
Speaker 1: Then doesn't matter where they go.
Speaker 1: These data are always going to stay anonymous on either
Speaker 1: hand with this technology that can also extract the ID
Speaker 1: data secretly, and then we can know who belongs to
Speaker 1: whom.
Speaker 1: So this is our algorithm, 1.3 to perform this sparse.
Speaker 1: And then finally, algorithm 1.4 here is we design.
Speaker 1: These are two different communication modes card closed loop communication
Speaker 1: and an open loop communication.
Speaker 1: So maybe I can just go back a little bit
Speaker 1: in this diagram here.
Speaker 1: So closed loop is like a two way communication, so
Speaker 1: between the local layer engineer.
Speaker 1: Again, edge layer could be a smartphone locally or could
Speaker 1: be a sensor node or engineer, could be a laptop
Speaker 1: computer at home or in a, for example, assisted living
Speaker 1: facility or wherever, you know, a relatively more powerful computing
Speaker 1: platform compared to a local.
Speaker 1: Now, the difference between open loop communication and a closed
Speaker 1: loop communication is that open loop is one way.
Speaker 1: So the data is only transmitted downwards in one way.
Speaker 1: Closed loop is a two way communication, so the local
Speaker 1: layer can also receive commands from this lower lower layer
Speaker 1: or control layers.
Speaker 1: So that's the difference between the two communication modes.
Speaker 1: So here in this algorithm, 1.4 here we choose to
Speaker 1: use closed loop, either a closed loop or open local
Speaker 1: communication, depending on the patient's health status.
Speaker 1: The data is analyzed from the ICU data.
Speaker 1: Very simple preliminary data analysis, for example, just based on
Speaker 1: like, for example, for arrhythmia.
Speaker 1: Then we can just rely on this simple our peak
Speaker 1: detection, and then it has some preliminary different differentiation between
Speaker 1: the two.
Speaker 1: So if it is healthy, then we don't need to
Speaker 1: worry a lot about just to send one way communication.
Speaker 1: If it is healthy, If it is not normal, then
Speaker 1: we have closed watch.
Speaker 1: So these are the two communication modes that we can
Speaker 1: select.
Speaker 1: So that's the local layer.
Speaker 1: Now in Adelaide we perform more comprehensive tuning algorithms.
Speaker 1: So I guess I don't need to go to the
Speaker 1: details about that.
Speaker 1: There's a lot as we said, for example, Google now
Speaker 1: has a very useful platform that in our simulation we
Speaker 1: can use that to verify this classification accuracy.
Speaker 1: And then the third category is the medical layer, the
Speaker 1: expert analysis with the use of cryptography and the decryption
Speaker 1: and the extraction of the steganography process.
Speaker 1: The medical layer can recover all the information that's been
Speaker 1: needed to to do this expert analysis.
Speaker 1: Here, there's more in-depth analysis on ICD signals, of course.
Speaker 1: Actually, the study here doesn't have to be applied to
Speaker 1: ICD only.
Speaker 1: It can be in principle.
Speaker 1: It can be applicable to, you know, any kind of
Speaker 1: the medical sensor network system like we kind of i
Speaker 1: amp Internet of medical things.
Speaker 1: Okay.
Speaker 1: So these are the algorithms.
Speaker 1: So some of the highlights and the highlights in our
Speaker 1: work focus here.
Speaker 1: And the first one is the the adaptive partition algorithm.
Speaker 1: So we need to in our work, we had to
Speaker 1: consider the best way to partition this air with between
Speaker 1: those three layers, if not more than three layers.
Speaker 1: So the top layer, the local layer has the simplest
Speaker 1: platform, has the least had our resources, but it is
Speaker 1: closest to the data, original data, and then the further
Speaker 1: go down to the lower layer, then we have more
Speaker 1: powerful computing and then it will involve more need for
Speaker 1: transmission of the data and then consumes more cost because
Speaker 1: of that.
Speaker 1: So what's the best way to partition this over those
Speaker 1: two?
Speaker 1: So that by itself is a big problem.
Speaker 1: It's a big question mark.
Speaker 1: And we like to, you know, make some contribution to
Speaker 1: this.
Speaker 1: So in our what?
Speaker 1: Okay.
Speaker 1: So the second highlight is this a proposal of the
Speaker 1: two different communication modes.
Speaker 1: And the third is this integrated cryptography consisting of the
Speaker 1: steganography as a important part of that.
Speaker 1: And then the flow of the system.
Speaker 1: Maybe I can just wrap it up here is like
Speaker 1: first the diagnosis is initiated by the wearable in-situ sensor
Speaker 1: attached to the patient's body, and then after the training
Speaker 1: of the API is performed, then this inference margin inverse
Speaker 1: module means after the algorithm is trained, then all these
Speaker 1: parameters are known.
Speaker 1: They can be used to classify the data.
Speaker 1: So this inference module has a relatively simpler computer computational
Speaker 1: complexity, and that will be performed by the wearable sensor
Speaker 1: based upon evaluating the severity of the diagnosis.
Speaker 1: So we could choose either open or closed loop communication,
Speaker 1: and then the data will be send it to the
Speaker 1: engineer Dad earlier receiving the data will have these keys
Speaker 1: to decrypt the data, and then finally the engineer will
Speaker 1: send if necessary.
Speaker 1: The engineer will send all the information to the medical
Speaker 1: server for final analysis.
Speaker 1: Here we have some numerical results regarding our proposed new
Speaker 1: methods in the simulation.
Speaker 1: So this first graph shows the our resilience or success
Speaker 1: rate of the responses because we have to consider the
Speaker 1: noises in the communication channel and all the data we
Speaker 1: are hiding will survive against this disturbance without affecting the
Speaker 1: UCG data quality to South.
Speaker 1: And then this table shows the difference between open loop
Speaker 1: transmission and the closed loop transmission.
Speaker 1: As you can see, open loop transmission, we really don't
Speaker 1: need to transmit here.
Speaker 1: So we have these 18 different patients usage data.
Speaker 1: Now, 12 of them are healthy people and then the
Speaker 1: other the rest of them are abnormal.
Speaker 1: So these data are coming from public about a publicly
Speaker 1: accessible database from America.
Speaker 1: And then we did our experiment, and with this open
Speaker 1: loop communication, all we need to transmit are like 48
Speaker 1: bits each patient.
Speaker 1: Your words.
Speaker 1: If it is if the data is the housing is
Speaker 1: a healthy OECD, then we don't need to transmit anything,
Speaker 1: basically, except those are renovating information.
Speaker 1: But on the other hand, if there is some abnormal
Speaker 1: situation, then we have to transmit a majority or even
Speaker 1: 100% of the data to the edge layer where the
Speaker 1: engineer can perform further verification of that analysis or more
Speaker 1: in-depth analysis of that incident.
Speaker 1: So here, as you can see, we need to transmit
Speaker 1: a lot of bits.
Speaker 1: If you compare open loop and a closed loop, you
Speaker 1: can say open look or say 99.99% of the transmission
Speaker 1: costs.
Speaker 1: So in this way it is possible we can make
Speaker 1: now 24 seven continuous monitoring.
Speaker 1: Otherwise, I wouldn't be practical.
Speaker 2: And I guess I may no dummy may say something
Speaker 2: to please the in for the for the non engineer
Speaker 2: person like for myself.
Speaker 2: It took me a while to really get the significance
Speaker 2: of that.
Speaker 2: But the energy and I may be using wrong terms,
Speaker 2: but the energy expensive step is the transmission of data.
Speaker 2: So if you if you're if you're transmitting a lot
Speaker 2: of data that is normal, that is a energy wasteful
Speaker 2: situation.
Speaker 2: And thus that's really what this this avoids.
Speaker 2: So basically, if the data is normal, basically it it
Speaker 2: it is transmitted in a single direction as opposed to
Speaker 2: having that that closed loop communication, which which utilizes a
Speaker 2: lot of bandwidth and a lot of energy and time
Speaker 2: and processing power.
Speaker 2: And thus basically the benefit of this, that's part of
Speaker 2: the whole thing.
Speaker 2: It took me a while as a non engineer to
Speaker 2: really kind of grasp the significance of that and hopefully
Speaker 2: that.
Speaker 2: Mm hmm.
Speaker 2: I don't know what kind of people other people got
Speaker 2: it immediately, but that's what took me a little while.
Speaker 1: To say Thank you.
Speaker 1: Thank you, Jason.
Speaker 1: Yeah, you're absolutely right.
Speaker 1: You know, this is a very important in terms of,
Speaker 1: you know, minimizing the amount of data to transmit.
Speaker 1: So our our slogan is like we only transmit data
Speaker 1: if it is absolutely necessary.
Speaker 1: So whenever we transmit something unnecessarily, so that means something
Speaker 1: wrong and it can be improved or optimized for that.
Speaker 1: As a matter of fact, this is, again, as I
Speaker 1: said in the beginning of the presentation, this is only
Speaker 1: preliminary.
Speaker 1: You know, Norwegian.
Speaker 1: I think there's a lot of things we can can
Speaker 1: do here.
Speaker 1: For example, these are healthy patients.
Speaker 1: We don't need to transmit their data either continuously because
Speaker 1: because, you know, all we need to do is to
Speaker 1: transmit this representative usage of data.
Speaker 1: You know, I'm talking about these are sick people.
Speaker 1: These are people with some diseases because we don't need,
Speaker 1: even though we are doing the continuous monitoring.
Speaker 1: But these transmission doesn't have to be continuous because with
Speaker 1: our cardiologists needs as we need to transmit.
Speaker 1: That's right.
Speaker 1: So cardiologist doesn't have to look at this .07.
Speaker 1: So now so there's a lot of room we can
Speaker 1: talk about this, but bottom lines, as Jason said, it's
Speaker 1: very important to save the communication energy of this because
Speaker 1: that will significantly increase the lifetime of the sensor nodes
Speaker 1: and because they run on batteries.
Speaker 1: Right.
Speaker 1: So I can continue this.
Speaker 1: And then here we have this, an analysis of our
Speaker 1: peak detection of abnormal usage signals that shows up some
Speaker 1: diagnosis results.
Speaker 1: Are we kind of like initial diagnosis best done by
Speaker 1: the local layer?
Speaker 1: You know, we here we have different patients with different
Speaker 1: heartbeats and within a closed loop separation.
Speaker 1: This is not what we can address.
Speaker 1: Here.
Speaker 1: This slide shows the the Google NAT training, actually the
Speaker 1: machine learning algorithms that can provide pretty high accuracy, 99.12%
Speaker 1: classification accuracy between the how the data and how the
Speaker 1: data.
Speaker 1: Again, these are healthy and healthy easily data come from
Speaker 1: the online database.
Speaker 1: So we use those data and then perform this simulation
Speaker 1: to test our the AI classification performance.
Speaker 1: This is a further table showing that different situation with
Speaker 1: these records and different parameters that can be done by
Speaker 1: the kind of classification, as you can see here.
Speaker 1: The local device here has, because of the simpler computing
Speaker 1: platforms or more limited hardware resource, we can perform simpler
Speaker 1: computing.
Speaker 1: Agarwal.
Speaker 1: So the classification algorithm is 19 2.7%.
Speaker 1: It is not as high as the medical layer, but
Speaker 1: basically it gave some initial differentiation between the healthy and
Speaker 1: healthy.
Speaker 1: Actually, the more we think about this and the more
Speaker 1: interesting question, because for the local device classification here, there
Speaker 1: are two kinds of force situation.
Speaker 1: One is.
Speaker 1: Sorry, but there's something wrong with the mind.
Speaker 1: Okay, so one is the false positive.
Speaker 1: That is false negative.
Speaker 1: So what we need to do at the local device
Speaker 1: is to make sure we want to minimize the false
Speaker 1: positive.
Speaker 1: We I mean, we want to minimize the false negative.
Speaker 1: And we don't care about false positive.
Speaker 1: We can tolerate that, but we cannot tolerate false negative.
Speaker 1: So in other words, if someone some people has an
Speaker 1: abnormal situation, we don't want to miss that.
Speaker 1: We want to capture every other normal, even under the
Speaker 1: cost of a false positive situation.
Speaker 1: Okay.
Speaker 1: So there's a lot of details about this, but this
Speaker 1: gives some superficial, superficial glimpse of what's going out within
Speaker 1: those three layers of the classification.
Speaker 1: And this flowchart shows how the diagnosis target can be
Speaker 1: partitioned among the three layers of local and in the
Speaker 1: cloud environment.
Speaker 1: Know some other examples of the information.
Speaker 1: This table shows the comparison between a local device and
Speaker 1: edge device.
Speaker 1: Here, a local device is a Arduino microcontroller system and
Speaker 1: anti device.
Speaker 1: Here, as I mentioned, it could be a smartphone or
Speaker 1: it could use some special home station or could be
Speaker 1: like a portable box or something.
Speaker 1: So in this table and the device could be like
Speaker 1: a raspberry, which is a well known microprocessor system that
Speaker 1: can be developed.
Speaker 1: So as you can see, the comparison between their CPU,
Speaker 1: between their storage, their weight science, you know, these things.
Speaker 1: And here this table is the comparison between a fog
Speaker 1: server and a cluster focus, or sometimes can be taken
Speaker 1: as the same as the anti server, but also can
Speaker 1: be something bigger than the server.
Speaker 1: So going to device.
Speaker 1: So one example of this is the like the edge.
Speaker 1: So it's like a you have like a building with
Speaker 1: 200 residents in it, like an assisted living building, something
Speaker 1: like that.
Speaker 1: And each of these 200 residents would have something wearable
Speaker 1: and then something in their room.
Speaker 1: And then in the building there could be a aggregation
Speaker 1: data aggregation system.
Speaker 1: And that could be this fog here.
Speaker 1: It's basically like a desktop computer.
Speaker 1: You know, So cloud will be the example.
Speaker 1: The cloud will be like this enterprise, you know, the
Speaker 1: popular Amazon cloud server system.
Speaker 1: And then the sensor node that can where I tried
Speaker 1: that there's a lot of commercially available options about these
Speaker 1: sensors.
Speaker 1: One example is this Philips biosensor circuit system, and that
Speaker 1: can have some battery and can a Bluetooth and then
Speaker 1: can run up to like 115 hours of communication, something
Speaker 1: like that.
Speaker 1: Again, if we can use this open loop, closed loop,
Speaker 1: intermittent communication rather than, you know, continuous communication, that will
Speaker 1: make a big difference.
Speaker 1: But again, we're emphasizing we're doing the 24 seven monitoring
Speaker 1: continuous, but it doesn't mean 24 seven transmission.
Speaker 1: The transmission is triggered by the result of this onboard
Speaker 1: initial diagnosis.
Speaker 1: So that's the whole idea is simply expressed by that
Speaker 1: one sentence.
Speaker 1: Okay.
Speaker 1: So and of course, doing that, the whole process, protecting
Speaker 1: the data security, making sure the data, you know, like
Speaker 1: privacy is and a protected data integrity assure all these
Speaker 1: things.
Speaker 1: So anyway, so the summary of this work, I can
Speaker 1: have several highlights.
Speaker 1: In this work, we addressed the security concerns and the
Speaker 1: countermeasures, basically hiding the ID information into the usage data
Speaker 1: itself and making sure the data can be anonymous.
Speaker 1: And we have a three main layers so far.
Speaker 1: As far as we know from the open public nature,
Speaker 1: we don't see something like this yet.
Speaker 1: I'm sure at this time there are many other research
Speaker 1: groups around the world are doing something maybe.
Speaker 1: But so far in the paper database, in the literature,
Speaker 1: we don't see something like that yet.
Speaker 1: To partition algorithms between these different layers.
Speaker 1: And then in order to have a successful partition, you
Speaker 1: have to provide security.
Speaker 1: So there's a lot of tradeoffs, you know, between a
Speaker 1: lot of these variables.
Speaker 1: All right.
Speaker 1: So we have these two different modes of transmission, and
Speaker 1: then the open loop is triggered by a detected how
Speaker 1: they etc. and then closed loop is triggered by the
Speaker 1: abnormal, etc..
Speaker 1: And then we have this closed loop will provide a
Speaker 1: feedback paths.
Speaker 1: This feedback path can do a lot of things.
Speaker 1: For example, you know, most often the challenge to this
Speaker 1: wearable sensor is the contact area.
Speaker 1: Sometimes they, they don't contact to the skin very well.
Speaker 1: So there is some kind of a notice could be
Speaker 1: provided by this kind of closed loop communication or just
Speaker 1: a pure hardware to our communication to extract more information
Speaker 1: like that.
Speaker 1: And then here, the last bullet here is the to
Speaker 1: DC in an inference module.
Speaker 1: So the air is not trained in a local layer,
Speaker 1: this training and automatic layer, but is executed in the
Speaker 1: local layer.
Speaker 1: And then continue to summarize the highlights.
Speaker 1: So here are the diagnosed abnormal EKG data in the
Speaker 1: local area will be sent to the more powerful agitator
Speaker 1: for verification or further classification analysis, and then finally send
Speaker 1: it to the medical data.
Speaker 1: And then during third of the process, we proposed this.
Speaker 1: This technology landed with cryptography and is being deployed in
Speaker 1: every layer, local layer after layer and cloud layer.
Speaker 1: And now here we can with all these keys for
Speaker 1: cryptography in this technology stuck in our galaxy, now we
Speaker 1: have these IDs protected.
Speaker 1: And we did some experiments with these.
Speaker 1: And the shows we can make the whole system more
Speaker 1: efficient in terms of transmission energy and also in terms
Speaker 1: of the data security.
Speaker 1: The future work will be actually we are currently working
Speaker 1: on this to propose this blockchain based infrastructure that can
Speaker 1: securely share information between these different layers.
Speaker 1: Not only they hide in the data into SD, but
Speaker 1: also making a very robust system they can have at
Speaker 1: the registration how sash and protection, have the keys, protection,
Speaker 1: things like that.
Speaker 1: Right.
Speaker 1: So I think there should be all any questions.
Speaker 1: Any discussion.
Speaker 1: Dr. Window, I cannot hear you.
Speaker 1: I think it seems on your mute.
Speaker 0: I will unmute.
Speaker 0: Thank you very much.
Speaker 0: As I listen to this and I'm interested in what
Speaker 0: Jason has to say, I think to me one of
Speaker 0: the interesting use cases would actually be to gather more
Speaker 0: information in a more dynamic environment.
Speaker 0: And what I was thinking about is a arrhythmia monitor
Speaker 0: such as we have in our hospital as a source
Speaker 0: of.
Speaker 0: Data to further train the algorithm.
Speaker 0: Do you have ideas?
Speaker 0: And does Jason have ideas of how many?
Speaker 0: Unique cases you think you need to to fully get
Speaker 0: things trained.
Speaker 0: Both in terms of the duration of monitoring and the
Speaker 0: number of events.
Speaker 2: So in terms of the clinical I guess in terms
Speaker 2: of the clinical application, I think yeah, I think definitely
Speaker 2: an inpatient setting would be useful because.
Speaker 2: Not only do you have I mean, not only would
Speaker 2: it be easy for us to kind of verify the
Speaker 2: data, but you would have other critical data to go
Speaker 2: along with it to kind of verify.
Speaker 2: The accuracy of the of the system, basically, meaning you
Speaker 2: could use it alongside another proven system and basically just
Speaker 2: show comparatively that you have less alerts unless basically less
Speaker 2: work to do with the with with the system.
Speaker 2: But yet it it notifies you with a similar rate
Speaker 2: when there's a natural problem in terms of the training,
Speaker 2: you know, in terms of the training, that part maybe
Speaker 2: don me I don't know if you would know like
Speaker 2: how many patient, how much time, how much data we
Speaker 2: would need to to really determine the accuracy of the
Speaker 2: system.
Speaker 2: I don't know if you have an idea of that
Speaker 2: part of the question.
Speaker 1: Mm hmm.
Speaker 1: Yeah.
Speaker 1: We basically use those I 90 database for the training
Speaker 1: and and basically hundreds of these patients should be good
Speaker 1: enough.
Speaker 1: The point of this training is that we do need
Speaker 1: to retrain this once it is trained.
Speaker 1: So for certain diseases, like, for example, I mean, as
Speaker 1: long as we have all the representative cases, then our
Speaker 1: in our testing, we for example, we take like a
Speaker 1: 200 patient data and then we input it to the
Speaker 1: trainer and then, well, you know, we give the results
Speaker 1: of the how they are, how they are then ones
Speaker 1: that are available to learn it.
Speaker 1: Then after that you just give a new class, then
Speaker 1: you test how accurate you can get the results.
Speaker 2: Yeah, I guess.
Speaker 2: I guess.
Speaker 2: John I guess some.
Speaker 2: Yeah, I guess what, what you're saying is that, um,
Speaker 2: and my team has made it freely available.
Speaker 2: Actually, they have some databases that you can use to
Speaker 2: train your AI.
Speaker 2: I guess that would be one step.
Speaker 2: But then clinical testing, I guess, is more what you
Speaker 2: were asking.
Speaker 0: Yeah.
Speaker 0: So there's always a step between testing on a on
Speaker 0: a team paged data and testing in the wild where
Speaker 0: you have lots of issues.
Speaker 0: I, I don't see anything from a technical perspective that
Speaker 0: is challenging.
Speaker 0: I, you know, I think blockchain's pretty well established as
Speaker 0: a secure thing.
Speaker 0: We can see if others comment.
Speaker 0: There will be a challenge in terms of getting the
Speaker 0: data into, um, electronic health records, but that's something that
Speaker 0: can be worked on later.
Speaker 0: Um, I just think that.
Speaker 0: A good use case with a new data set with
Speaker 0: ground truth Teaching is going to be necessary too.
Speaker 0: I'll say it this way to move the concept to
Speaker 0: a proof of value statement where it would be attractive
Speaker 0: to commercial entities to adopt the technology.
Speaker 1: Yeah, I fully agree on a more to the real
Speaker 1: world.
Speaker 1: Actually the other day this earlier this week I was
Speaker 1: discussing with someone you know in this is that eventually
Speaker 1: to some point probably I hope in the near future
Speaker 1: that will involve the real patients that you know for
Speaker 1: example people can wear these things now walking around for
Speaker 1: a while, week to week, and then we can see
Speaker 1: how effective we can monitor these data and we can
Speaker 1: can show it works.
Speaker 1: You know, that would be a big, big plus.
Speaker 1: I think that will definitely be the next phase of
Speaker 1: this work.
Speaker 1: Maybe we can include that into our future proposal.
Speaker 1: Now back to that blockchain.
Speaker 1: One thing I want to say about that is that,
Speaker 1: of course blockchain has been saturated with research too, for
Speaker 1: a lot of years, 90 10:09 years.
Speaker 1: But there hasn't been really an regarding blockchain with information
Speaker 1: sharing, especially medical information sharing.
Speaker 1: Now in our vision, our informational component is we kind
Speaker 1: of Chino chance and the whole blockchain is basically to
Speaker 1: ensure who I can trust and why.
Speaker 1: So if you are not like in the chain, then
Speaker 1: I don't trust you.
Speaker 1: You have to be registered first and then it's like
Speaker 1: a banking system.
Speaker 1: If you are in my ledger, then I can recognize
Speaker 1: you.
Speaker 1: Otherwise I don't.
Speaker 1: Now, the problem with these medical system is that wearable
Speaker 1: system, there could be millions of these users.
Speaker 1: Now, it's impossible or totally unrealistic to have each of
Speaker 1: these user to register into that blockchain because it involves
Speaker 1: a huge amount of communication.
Speaker 1: Well, again, due to the the energy constraint, battery constraint
Speaker 1: and the bandwidth constraint.
Speaker 1: So we're inventing this tin of a chance layer of
Speaker 1: layers of the blockchain.
Speaker 1: So every element in that blockchain by itself is the
Speaker 1: chain.
Speaker 1: So something like that, it's still not very clear to
Speaker 1: us yet.
Speaker 1: We are currently working on that.
Speaker 1: Maybe you next few months I can come back and
Speaker 1: report some progress.
Speaker 2: I guess.
Speaker 2: For for me, I guess I go in with them
Speaker 2: looking very well.
Speaker 2: So I think at some point maybe you could if
Speaker 2: we're going to have another session like this, maybe you
Speaker 2: could you have a non engineer yet talk about blockchain
Speaker 2: in more a little bit more.
Speaker 2: I mean, I don't know if that might only one
Speaker 2: or four is everybody else, uh, from a I mean
Speaker 2: I know, I know I have heard of blockchain, but
Speaker 2: I'll, I'll be honest, I don't understand it very well.
Speaker 1: These days.
Speaker 1: Blockchain has been used in so many areas.
Speaker 1: It has been, of course, financial industry, write digital, digital,
Speaker 1: financial.
Speaker 2: Digital coins and some.
Speaker 1: And also in supply chain management and also information sharing
Speaker 1: with a blockchain that's a relatively new or maybe for
Speaker 1: the past five years maybe has been researched.
Speaker 1: But as far as I know for medical information sharing
Speaker 1: that's kind of new, relatively new, I would say maybe
Speaker 1: only two or three years in the past.
Speaker 1: We can find some papers about that.
Speaker 1: Now regarding this, a partition table or partition computing and
Speaker 1: the security, you know, with this inaugural thing and data
Speaker 1: protection, how they can be applied to blockchain or do
Speaker 1: they have to be applied to blockchain?
Speaker 1: That's something put in.
Speaker 1: You know, as far as I know, nobody has thought
Speaker 1: about this in all in my.
Speaker 1: To the best of my knowledge.
Speaker 2: Okay.
Speaker 0: Join other thoughts Comments.
Speaker 0: If not, thank you for this.
Speaker 0: Excellent.