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lady Desmond welcome my name is Relkin a group and I work for interrupts and today we're going to demonstrate our latest demo called hammer which is using these little devices called ibeacons which is basically representing me Internet of Things to have a scenario where we are using these things in order to have location-based triggering so imagine the situation where you have these beacons somewhere in a room once a patient or somebody else is moving towards this ibeacon in a certain action can be taken a nap these ibeacons are using bluetooth they're filtered sensors and the application we have built is called hammer which we're going to demonstrate now so the hammer electronic of medical record is a application which is using triggers by the ibeacon so once I triggered by IBM my application is automatically recognizing now that this beacon belongs to a certain patient so in this case patient recognition is almost flawless because only one person can have an IV can and that means that not being able to identify people with something of the past and what you see is that automatically the face is recognized and once I press on the yes button you will see that automatically the information is all out of Hana so these are actually the other steps from this specific patient and that is automatically stored into a into the Hana what we also have is some other sensors in this eye because one of them is temperature so you see on the right hand side the room temperature that's automatically measured by the device and also stored in to Hana and next to this we also are able to track movements so we can know we know by using these beacons where a certain patient is in the room and there's also an extra button there called hyper care and once we switch that button on the moment the patient is tripped or instant falls out of his bed automatically and the warriors let's switch them next to this also very important part is that besides being able to track movements we can also record how often the patient is moving in his Beth in combination with temperature it gives us tremendous amount of information which we're using in processing in in acid Bahama and that's used by the wheel a demo which is being done by now in the next queen as my colleague just explains we have the ibeacons capturing all sensor data it's pushing it into the Hana and I would like to get the information out and it's now it's just raw light data so we like to combine it combined it with some excel data to make it really information to do this we just start up in the mirror and get into an actual Hospital case in this case we will look at the computers which is a tomb for bedsore and it basically means if you're late mommy you're about to become one which or better which is something we definitely don't want so what we'll do now we have a good information view about movements and temperature the movements come from our eye beacon and the temperature also and we have a location here we can see for example that Larry Anderson here has very low movement and I think it has to do with the fact that he wasn't a different room and here I see it's the operation room so let me just check if what I think I see is great actually the data that I just got and it's correct so at this point I don't really care about because he's in our operation room I think the doctors will take care of him so when rather my analysis I will make sure this is excluded for now I like to combine it with one of my local files so what I would do first is at my new file put here I choose the local excel file but I see here is H and movements this is the number of movements you must be minimum for average to not get an increased risk for ticket addition in this case I could set someone for instructions but for now I'm fine so I'll just create this data set and then I actually want to combine it with a data set I already pulled out of hammer so let me just get this one and I like to combine it's fire merge in this case case H is the same as next night which is H for H and here I see perfect hundred percent match so I'm sure correct in here because my Dutch is probably better than yours I will rename this very quickly to safety because this is the moment we must get for your range to not be in any risk and how I'd like to my make combination of my average and my safety so I'll make a new calculation over here because I what I really want to see is when the average movement of a patient get lower than its safety norm because those are the increased risk moments which we can actually do something about in this case about risk what I will do here make a commission and I will say if my safety norm - that if my average if my average movement is lower than my 69 that showed me the norm of the safety and in other cases I'm not really interested in what a little shell just outputs just a value of zero and now I made a nuclear equation which should show me the risk of the people based on their age I just combined switch back to my visualization tab and in this case I like to change it to combine my charts I want my risk moved in as well then you see here I have several moments in time where I have increased risk if I just jump back to my composed step you see here the graph is updated that still remembered as lectures I mean before we just discussed that I will exclude the operation to only take hospital room and you see at this point in time there was a slight decrease risk but in this point in time there was a bigger risk because here you see the Green Line actually going way below the safety norm so this is a point in time where a nurse could take action and actually make sure the patient is moving again so he has a decreased risk in the cthis operations wearing his eye beacon is now entering the exercise room the entire Hospital is supplied with IP cos the everyone in the exercise room we are falling in the private room of the year of the patient and the reason for that is that we're using that in combination with the loved ones at the moment the pension the patient enters a certain room and it could be the exercise room or his bedroom and automatically whose message is triggered and that just happened so what we see here now is that we get a notification that are pending patients are lost one has entered the exercise room now that's also Dom when for instance he goes to bed in the evening and what's more perfect than actually sitting on your couch and knowing that everything is okay with your loved one and for that we have built something very specific which we're going to show you in the next segment it's really a internet-of-things thing and it's treated by a RSS feed because the beacons register the movement of the patients this information can be used to inform the loved ones at home using an RSS feed and a standard I of TDT service we to identify specific movement of your loved one in the hospital with this information we can turn on lights or make them change car you