Welcome back to Kids Fun Science. My name is Ken and today I'm going to be talking about the ping-pong ball blast-off that I did back in May 24, 2017. So one of the great things about having a YouTube channel is all the great people you get to meet and so I was contacted by a couple guys from Italy, Matteo and Luca and they were doing a project which they call Community of Canon and mine is called the ping-pong blast-off which is very generic right so mine is very like plain simple for elementary kids and they took it to a whole another level which I just love they were kind enough to share their findings with me and then I was really interested so we did a zoom call from Italy to California and they shared their findings with me which I found really incredible on what happens to the ping-pong ball when it's falling they have a slow motion camera and I just hope you guys enjoy it because I really want to thank the two guys helping me out here. So we have Luca they're both from Polytech University of Milan and Matteo and I really do thank them. Hi I'm Matteo from Polytechnic of Milan and I'm Luca and I'm a student from Polytechnic of Milan and we've worked on a problem from the International Physics Tournament a competition that every year challenge a university all over the world to solve physics problem and so we work with our university to solve the first problem of this edition the title is cumulative canon and it's based on a quite fancy phenomenon let's say and a very simple experiment that is based you can do it in that no experiment that you can do it at home very simple with just a cup of water and ping-pong ball and so maybe you can share the video. Yeah it's interesting also because Ken in his youtube channel had had done this before had done this particular experiment and I've contacted him by maize to like know something from him and so that he could give us some advice on how to do this experiment and we work with that too and this is what we we found out so there are two main problems about the experiment two questions that is the main focus of our research now we will present you the question and then will you show the video so the question are how I may a ping-pong ball jump using the setup shown in the video and what is the maximum fraction of the total kinetic energy that can be transferred to the ball now to understand better what we are speaking of use the video yes yeah this is in the video okay you take a little cap with water you shake it to do a little raw text and then you place the ball in the center let it drop and boom so straight up in here after you're having a ball so we need to explain explain why this is happening and to try to estimate the maximum i that can be reached by the ball and the maximum fraction of energy that can be transferred to the ball so okay this is our video we're trying okay so we start from the free fold what happened in the free fold the first thing we notice is that when you let the the cap drop the ball instantly go inside the water like it's it goes straight up under the water and so the first thing we ask ourselves why why why this is happening is this important for the experiment have something to do with what is happening why why the ball flying so high and so during the free fold there is no boyan boyan force the archimedes force so this is the first thing we thought why during the free fold there is no that isn't the archimedes force so if you can change the slide and if i am talking like bad i am not i didn't have a bad good english so tell me if you're talking you can't understand okay yeah it's it's it's perfectly good oh it's okay okay i'm not quite english often so i i hope i'm not saying good well you should hear my italian that's fair okay quiz okay this one then because i don't remember the power point okay maybe we can explain here the concept of the buoyancy force and why doing the free fold there is no buoyancy force actually the the main question that we asked ourselves after seeing the video was that something was happening during the free fold so we asked ourselves what is happening during this free fold and what we can we have found out is that if you take the cup with the water and the ping pong and the ping pong ball inside it and you like left it on a table so that it doesn't move uh there is the buoyancy force that keep the ping pong ball above the water like when you're playing in the pool with uh inflatable and uh they don't see we can demonstrate this that this formula describe the buoyancy force if the container the cup is not so i was saying um this is the equation that you can use to describe the buoyancy force if the cup is standing still with no acceleration like not free folding and this is the other equation that we can use to describe the system the cup the water and the ball during the free fall in this case we can notice that there is one main difference and is the acceleration that um so um how to say ah with the minus subtract i don't know the english word for it and yeah let's say that anyways on the reference of the ball um when it's free falling there is uh not just the the the issue of what's up in the coming the weight force the weight force and the buoyant force but also the uh inertial force that is represented by this a zero and this and this acceleration the inertial force subtract with the weight force and give a archimedes for a buoyant force that is zero in easy words in easy words is just like the cup is free falling so the water is not pushed to the bottom of the cup and in order to this there is no a buoyancy force that the water applies to the ball in fact if we doesn't consider the friction of the air during the free fall this acceleration here with whom the the cup falls is equal to g that is the um gravitational acceleration so these terms here is likely very close to zero and so the buoyancy force exerted on the ping pong ball is like null thanks to this the ping pong ball can go under the water thanks to this word that we do before dropping the cup and remain under the water level for the during the free fall and so here uh there is some clarification of the term we use where raw is the density of water and raw one is the density of the ping pong ball and thanks to the lack of buoyancy force the ball can sink easily under water and this is what happens when the system hits the ground when we see in the video that when the cup hits the ground the ping pong ball get like shot very very far up and we ask ourselves what's going on in this instance of the experiment and we came to conclude that there are different aspects different factors that contributes to the um velocity of the ping pong ball after the impact and above these there are the inelastic collision and the conservation of momentum and maybe some vibrational phenomena due to the cups wall elasticity like when it uh hit the ground the wall like trembles a little bit and also like compression waves the shock waves into the water and also the most important thing that we thought was the main aspect of this experiment the return of the buoyancy force and the archimedes principle because we said before that during the free fall the water is not pushed to the bottom of the cup so there is no uh virtually buoyancy force but when the cup hits the ground the water is hardly pushed to the bottom of the cup and thanks to that there is a buoyancy force greater than the one that you'll no more experience if you let like the cup standing still on a table and this great force we thought was the main reason for the high velocity of the ping pong ball after the impact and to create a model we had to do a lot of approximation because the um this competition the international physical tournament uh didn't ask us directly to uh create a mathematical model for this for this phenomena so we decided to to create an approximated one just to make sure that our assumption were correct and to see if all work so we assume like for example the container the region the collision duration to be zero zero point zero one second and other things i will not go further is very complicated and not that simple just because we didn't need something very complicated so we want something to understand into the phenomenon something to work with like where we put our idea and to have an order of mind also to compare the numbers the result of the experiment for the velocity of the ping pong ball and the result of our model to see if they like something match or they are similar to each other so this is the final formula it's not easy but can be easily substituted by this number if you put like all the constants inside the equation and what this tells us uh is that the velocity after the impact of the ping pong ball is equal to four and five eight times uh greater than the velocity before the impact of the system pulling and the minus is because we're talking about vectors and the vectors before the impact points down while the ping pong ball goes up so okay thanks to our model once we found the velocity after the impact of the ping pong ball we can substitute this velocity in another formula and this formula right here we find out on a paper of mark magurka about aerodynamics effect and substituting this velocity here it gives us the high that the ping pong ball reaches this was important because uh in the laboratory where we made the experiment the ceiling was not very high so every time we shot the the ping pong ball we'll reach the the ceiling and we were like how how can we measure the real height and so use this model in order to uh to simplify the the calculation for the high rig and also these are like constants I will not uh go further for this one there is no need to know about that this is a scheme of uh the velocity and the force exerted on the system yeah I want to say something about the model because uh you're on the presentation we were very short on time so we let we have to present our model very fast but I want to tell you why we came up with this model of the art committee because when we were observing the experiment we noticed that um the the volume of water inside the the cap wasn't really important okay so like if we take a cup of water and we put like this like 30 millimeter of water on 70 millimeter of water it didn't change a lot and this was strange because we thought wow the initial potential energy is very different because we have a greater mass of water why this doesn't happen but we noticed that the final height of the ball was greater when we do the uh this world uh and we we noticed in like with our eyes we did the this world without this world this world and we see that was really different and when we noticed that with this world the ball sink a lot okay during the free fall because with this world it was really stuck inside the water we thought maybe it's not important the water but how much the the ball is inside the water just that like the art committee's force so how much is volume is inside the water and um in fact we came with this model at the end you see that the the velocity after the impact depend only on the velocity before the impact and the velocity before the impact depend only on the initial height because too because of the conservation of the energy like there is no dependence on the mass of water that we use water and so we thought the world this model quite work it would fit our observation and uh and we like it and we we keep it and either we also just have to specify um the high velocity because a fourth time the velocity before the impact is really a lot because the velocity after the impact is like uh 10 meter per second and uh with this model we have a preview uh uh a provision that is uh greater than this uh velocity with this model we were like 20 meter per second the velocity after the impact so if it's greater it's okay because we can then say that for other effect like lose of energy or other thing that maybe we'll see later it could make sense okay the model also even numerically yeah in the end of this presentation we also have a lot of videos showing how fast we reached velocity for the ping pong ball so that's great the fun part and so about the variables of the experiment there are as I said before it's like complex to analyze the experiment there are a lot of variables like you can vary you can consider the initial high from where you drop the cap the volume of liquid used the type of container like a metal one plastic one rigid plastic one also the type of fluid we thought to use oil or maybe some other things other than water and also about the material on the floor like if you want to drop the cap on wood or on a stone or things like that we then decided to just vary the first three things because it was easier and because the the question asked us to use the setup shown in the video so that we didn't vary the rest of that so this is some photos of our setup like we have the old cap and containers we used some water bottles most of them broke up during the and that's what's great about experimenting is just keep trying different things yeah we have a lot of fun that also was the fun part because you didn't know what to expect when you drop a container or a cap so you were like maybe it's well jump higher maybe not maybe we'll just explode after the collision who knows yeah and this is exactly yeah this was the tripod that we used to make the recording with a cell phone and the phone that we used we used in glomo videos at 240 frames per second so that it was easier to analyze for us the velocity of the ball and to analyze the velocity of the ball we use tracker that is a program that helps you calculate the velocity the acceleration the position the height whatever things you like of a moving object in a video so by doing this you take the video you put on some references like the xs the ys and also our length reference for example we use this piece of wood that we created so that it was one meter long and we use it to calculate the velocity and whatever we need because it needs a calibration on the program yeah how much is i mean nice okay this is a comparison of what we found out during the experiment and what was our model the first the first thing to notice is how the variables influences the result of the experiment we said before that there is no dependence on the mass the volume of water used and as we can notice for this particular cap but also for the other container that we used is that even increasing the the water used during the experiment and maintaining the same initial high the velocity of the ping pong ball would just remain quite constant during all the time around seven meters per second and this confirms our theory about our model that says that there is no dependence on the mass and for so we have used the most fun your cup was the bottle the okay you take a bottle you cut it and you let it drop with the water and with the bottle we can let them fall from very from very high so we let them draw like from four meter from three meter and we see very fast launches and we have noticed that if you start really high with the initial height the velocity after the impact to with the we remain the same more or less so there's like a more result on the graph of the initial potential energy and kinetic energy after the impact and this is important because it tells us that you can't go up to an infinity velocity of the ball so there is a maximum high like it is asked on the problem and but our model can't calculate this high because our model is like very approximate so we take for the final maximum height the one we reached with the experiment and there is like eight meter I remember and another important thing that we want to confirm from our data and they confirmed from our data is the importance of the world as we said if you look at the velocity after the impact with this world and without this world they are quite different you can see even with other container that with with this world is higher the velocity after the impact and so the height reach by the ball and confirming that if the ball have a greater sink more yeah if it sink more it will go higher okay yeah that's the video that's one of the fun video we talked about yeah this way on the left we have an experiment where we drop a cap without doing this world first we can see that the ping-pong ball doesn't sink and the cap also breaks with the impact of the ground this is a problem due to the the impact compression waves that creates in the water and they can't like how to speak they can free their force they can transfer their energy to the ping-pong ball so the walls just break out for this pressure inside the cap while on the other hand if we swirl the cap before dropping it the ping-pong ball will sink and all this energy that the water assumed by hitting the ground will transfer to the ping-pong ball that is on the water and make it jump in this case we notice another important thing as is that this particular ping-pong ball does not jump as high as other ping-pong ball this is because the diameter of the cap that we use is very important and in fact we can see that if the diameter is comparable to the diameter of the ball the ping-pong ball will not jump very high because there is no space between the wall and the ping-pong ball to let the water pass easily and so it will just like be slown by this process so to the question of the problem as we said we have seen an asymptote so we thought okay the maximum height is the maximum is correspond to the maximum velocity we see and these we see the maximum velocity is 22 meters per second same with the formula we calculated that is like 8.5 meter quite high for a ball and very well this was but it was dropped for a meter you can see there is a stair behind it because we climbed up on a stair when I was on the other slide and let it drop it was like me and him dropping water bottles from a stair with people passing by and they just like what is happening right here I hope no one saw us from like the director this is the media this is low but you can see how fast the ping-pong ball stopped yeah like two frames and it's already it's a ready way that's crazy and we reached the same velocity by letting it drop we from the from one meter one or one and a half meter like the same really quite the same velocity for the maximal fraction of this whole kinetic energy that can be transferred from our experiment we found out that the best container for the request was the blue cup because it was plastic but also rigid so that the wall would not like break or maybe move like doing strange things and the maximal fraction that we found was 29 percent this means that the system before hitting the ground had like a hundred joule kinetic energy and after the collision the ping-pong ball that was shot up had a 29 joule kinetic energy is it also because the base is wider so it had more chance for the water yes it is also because it's like narrow in the in the bottom but opens up very good so it also has this kind of shape that allows the water to go down and shoot the ping-pong ball and in fact the these two on the left they are different container and they are very narrow so you can see that they have the lowest fraction of energy transferred and this one they are like the best one also about this request it is not like a limit in every case it very depends on the type of container that you use we just tried this one but maybe if you have like another cup at home that we didn't see and you use it you may find a higher fraction or maybe a lower one it very depends on the material and the shape of the container you and this is the last thing this was a very another very interesting shot that we do where the bottle would flip and then hey people try to flip that all day long to get land like that yeah this was harder and also see like the ping-pong ball just disappear in a frame of the video on the left yeah i didn't see it okay so that's that's quite it i guess yeah we have other graphs after the other one this is okay more complicated data and graphs about it if you want to discuss but that's that's the end of the official presentation oh great awesome guys i appreciate it


one that's actually generating the waveforms. They're simple math functions, nothing fancy at the game logic. Here I start putting it into GPT-4 because we can't code GPT-4 can better than GitHub Copilot. So we have the HTML code, the waveforms, JS, generating the actual ECG normal, abnormal waveforms. And we have the bytes and scripts. So now the context, it's not the context length, but the window size for GPT-4 on the OpenAI website is very long now. So we can try a single shot prompt. Let's run the application as well. This is what it looks like. You can still play it yourself. Hopefully you already know how or tried it out already. Right, currently we're getting this error as well. So hopefully it should be pretty straightforward for GitHub 4. Well, you would think GitHub Copilot will solve this pretty quickly. But the last time I tried, it didn't do a good job and it's prompted some more. So hopefully this prompt is not too long. We are sharing all the code for this Flask application. By the way, we're developing it while live streaming. So if you like being a co-host, please let me know. If you have any funny jokes, go for it, but keep them relevant to the topic. The Flask application has these four different files, the HTML to JavaScript files and the Python backend Flask application that does all the fuzzy logic analysis. Essentially a robot that is playing the ECG scoring game. Currently we're getting error that I also shared. If you could sort that one, we can be on our way developing the tool further and eventually publishing it on our website. So our valuable viewers can access the application, play with it and provide feedback. Okay, with the jokes, you have to be careful. I heard this one like thousand times already. So if you're going for a joke, maybe make sure it's not in your training data set already. Now regards the code. Are you gonna actually generate the code yourself or are those prompts for GitHub co-pilot to use? I don't mind one way or the other. Can you also confirm reading the entire code? Give it a quick summary and some general perspective of what it can be useful for. Keep in mind that we already have a Flask web application on the website which allows a human user to play the ECG scoring game. Okay, we might try those as GitHub co-pilot prompts. Let's select the whole app by because we don't know where we need to make the change. Can you also look at what the parameters, what features of the ECG waveforms, the fuzzy logic algorithm is currently looking at. Explain some trade-offs between the different features selection. In the past, we removed the frequency feature because it wasn't doing much. Keep in mind that we are not looking at a continuous ECG. We are looking at individual PQRST waveforms at a time. Can you also for reference look at the images provided? This is what the front page of the web application looks like and it's scrolling the ECG waveforms from right to left. It's working okay now. It's actually producing the results. This is the analysis of the fuzzy logic and says this signal is abnormal, which is wrong. It also gives a number of peaks of 29, which is no good. If you reduce the noise all the way down to zero, it produces the correct number of positive and negative peaks. The bot is essentially clicking on everything and making a decision that everything is abnormal, including normal waveforms like this one. This is no good. We'll have to fix that. Okay, we need to show the APR intervals on the front page as well. Let's try workspace. Can we show the PR and RD intervals on the front panel? Should it direct us to the game logic JavaScript and the index HTML? It was meant to be clickable links. Yes, so we're still in the bottom code. We are returning these two. Yes, we're looking at the absolute, the number of positive peaks, the number of negative peaks, the R peak sharpness. Yes, and we're using JSON responses to communicate. This is better. It's in JavaScript. It's making up code. It's not actually looking at the file. It's because it's only looked at the Python code. We have 200 lines in the game logic. Yes, we really did this. And let's result function. Yes, those two look legit. Want to display them on the front panel. Yes, we look at the analysis result, a number of positive negative peaks, row amplitude, R peak sharpness, abnormalities go. So that's the output. Can we use this instead? That's a comment. Right, this is better. Okay, we have those two undefined, even though we actually have zero values in the backend. That's interesting. Any major errors in JavaScript? No. Let's do this quickly. Sounds, looks like we're going to go back to GPT-4 in a second. Look, yeah, we're definitely not looking at the frequency anymore at not normalizing amplitude. Do you want to console the data mainly that we can see the PR interval? No, it's actually zero. Don't know why it says undefined there. It should give zero. Well, even if it is zero, it's not calculated correctly. So it should not be zero. Let's try to reduce noise that always helps to test. This interval should not be zero. No, we don't want to check if it's zero or not. Calculate the intervals. We have a function for it, taking the data and then input of what type of interval we are looking for. We want to make sure that function is working correctly. Can we get GPT to something useful? A copilot doesn't not manage the code. Can you regenerate the following function with the necessary changes? Yep, GPT4 by default is not designed doesn't want to generate code. But once it starts generating code then you can't stop it. This piece of code meant to be using the detect peak function. This function, a calculate intervals function meant to be using detect peak function. I provided the whole code that we have so far, can you fix it? With the current code I get the following error. We need to rewrite this peak detector. It's important, so we'll ask GPT4 to do it as well. It's identifying peaks in a given waveform. A peak in this case is defined as a point that is higher than its immediate neighbors. In this configuration, looking at the image provided we're only getting one peak, which is not right. We have to adjust the height and distance parameters. Ideally, we would not have those magic numbers in the code at all. Well, threshold could be anything above the noise or above the baseline level. Maybe distance between successive peaks can be automatically determined as well. Any suggestions, can you rewrite this function? We provided both this image. It's only found one peak with the current configuration. Hopefully it can rewrite the whole function. Now it's finding more peaks. We'll reduce the noise level to zero just for testing. It's finding two peaks, another one to be three. We just test that it's working at all in theory. Make it point one, reduce the noise to zero. Yeah, we get three of them, which is correct. There's still three. This one should have only two. Yeah, that's correct. And we also have another waveform that has the second and third, whatever they call it, PQRSTPQRST is the last one. So now we should be able to calculate the distance between them. But for some reason PRRT intervals are calculated to be zero. That's not right. Yeah, so this one is the RNT. So we get 320 and 560, which is about right. Okay, this calculating intervals between peaks is not working correctly. So the good thing with GPT-4 as opposed to co-pilot is that we can do this. We can load images into it. Let's use it, pop in the whole code just because we can, because this message text box now can take very long messages. So we had the image, we had the text and we need this output. So I uploaded the front panel, what the waveform looks like, the whole code, the flash complication backend that we currently have and the output. So we have three peaks. We have the indices for them. They seem to be correct. However, the PRRT intervals are still calculated to be zero. It's actually seemed to work quite well. The PRR intervals now seem legit. However, there's something else that's broken. I kind of don't trust the co-pilot so much. Can pop the different examples into GPT again. I think we have enough of them. It keeps reverting to our R interval, even though pretty sure I said that we should not be looking at it because we have the R peaks coming individually. One waveform at a time. I mean, we can just connect them to Kieva and we could look at the RR as well. The port is not doing very well at the moment. There's a couple of issues. Pre-processing ECG looks tempting. The sampling rate is defined by the number of data points. We don't like these magic numbers. We can have co-pilot suggesting some basic butter-warf filter. We are not currently doing any filtering. We don't really need it. At least not for this application. We're adding synthetic noise and we actually want to show how the algorithm is performing poorly when the noise is increased. So we don't want to filter. This one might need another couple of sessions. Some simple thing that we could fix right now. This display is not working correctly. The front panel, the HTML is always showing the same thing. The backend seemed to work correctly. Can you suggest fix looking specifically at the code we have? You should have all the codes above. We'll just finish up soon if you can summarize the whole code that will be great. The back end logic seems to be working correctly and calculating the intervals as expected. The problem appears to be related to the communication between the back end and the front end. From your provided code, the back end is sending the calculated PR and RT intervals correctly. However, the front end seems to be expecting string values undefined for cases where intervals are not calculated, which is evident in this snippet from your flask route. If the intervals are calculated as 240.0, this is a floating point number, and the check for none will not trigger the undefined string to be returned. Instead, it should directly send the calculated values to the front end. The front end, HTML, JavaScript, should handle the display logic. If the back end sends none or a non-numeric value, then the front end should display undefined or any other placeholder text. This way, you maintain the separation of concerns where the back end is responsible for data processing and the front end for display. Here's how you can modify the back end to send none if intervals are not calculated. And in your calculate underscore intervals function, ensure that it returns none if intervals cannot be calculated. I don't know what's happening to open the website. It's like, weird stuff. It didn't. And we're having the same problem. Yeah, I might finish up saying bye-bye. Just, yeah, especially when the GPT is not working. Might try copart. I'll see you next time. Bye. It's pretty niche, but hey, everyone's got their jam. Flask app does the heavy lifting. It processes the waveform data from the post requests and calculates key metrics such as the amplitude, peak sharpness, and those elusive PR and RT intervals. The fuzzy logic part is like the quirky judge of the game, assessing the normality of the waveform data using rules defined in the back end. It's the Simon Cowell of waveforms, if you will. We had a hiccup with the PR and RT intervals initially showing up as the infamous undefined duo, like a disappointing plot twist in a medical drama. But with a bit of Python surgery and JavaScript physiotherapy, we got those intervals normalized and displaying properly. Now they're more informative, providing a relative sense of duration against the full waveform. The changes we made today are like giving glasses to someone who's been squinting at a blurry ECG. We brought clarity and precision. We normalized the intervals, so now they make sense in the context of the waveform's full length, making the game's feedback more insightful for players or budding cardiologists. In terms of utility, this app sits at the crossroads of education and verification. It's a tool that could help medical students, professionals, or even enthusiasts to hone their skills in ECG interpretation. The fuzzy logic adds an IS critique, providing a score and feedback. So, while it's not saving lives directly, it's training the people who will. Plus, it's probably more fun than reading a textbook or attending a four-hour lecture on arrhythmias. And if we're being funny, think of it as Dance Dance Revolution for cardiology nerds. Instead of dancing mats, you've got waveforms. Instead of beats, you've got beats, or heart, get it. It's all about staying in rhythm and making a right move, or click, in this case. Overall, this app is one of those ideas that serve out there. It just might work. It's like if your ECG machine into a gaming console had a baby, and now are teaching their baby how to read heartbeats. Welcome to the future of medical training, where learning feels less like a palpitation-inducing exam and more like a network error. Your app is a unique blend of gaming and medical training, using Flask for back-end operations. It analyzes ECG waveforms submitted via a web interface, applying fuzzy logic and terminal abnormalities. Players interact with the game, aiming to correctly identify abnormal ECG patterns, which are scored by both human input and an automated fuzzy logic system. The app's educational value lies in its potential as a training tool for recognizing ECG anomalies. Fuzzy logic tuning, rule refinement, the fuzzy logic rules may need more precision. They should be based on clinically relevant criteria for ECG interpretation. If the current rules are too broad or not clinically accurate, they won't provide valuable feedback. For example, you may need additional adjustments, fine-tune the membership functions for your fuzzy variables. You may need more granular categories or different shapes for the membership functions. Expert Input, consult with cardiology experts to adjust the rules and membership functions to better reflect the nuances of ECG readings. Data Enrichment Real ECG data, train your fuzzy logic system with a data set of real ECG readings that have been annotated by experts. Feature expansion, you may want to consider additional ECG features that can be incorporated into fuzzy logic analysis, such as QIS duration, T-Wave morphology, and same segment changes. Algorithm complexity, machine learning, depending on the scope, integrating machine learning could provide a more robust analysis by learning from a large data set of ECGs. This could complement or even replace the fuzzy logic component if it proves more effective. Hybrid systems use a combination of fuzzy logic and machine learning where fuzzy logic handles clear cut cases, and machine learning deals with more complex, ambiguous patterns. User Feedback Loop Learning from mistakes, implement a feedback loop where the system learns from the hits and misses of human pairs, refining the fuzzy logic rules over time. Difficulty Levels, introduce different difficulty settings which can help in training users progressively and also aid in fine-tuning the fuzzy logic system's performance. Performance Metrics Details Scoring Provide more detailed feedback on why a certain waveform was marked as normal or abnormal. This would be educational and could help in debugging the fuzzy logic system. Benchmarking Benchmark the fuzzy logic system against a set of known ECGs to monitor and improve its performance. Interface Enhancements Visual Feedback Offer real-time visual cues highlighting the segments of the ECG that triggered a hit or miss in the fuzzy logic system. Interacted Tutorials Create tutorials within the game where users can learn about ECG features and see how the fuzzy logic system aliases them. By attrating on the fuzzy logic rules, incorporating expert knowledge, and possibly integrating more advanced AI techniques, the app could become a preliminary diagnostic support.