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The Engine for Automatic Biomechanical Evaluation or ENABLE™ captures and analyzes 3D motion to help athletes achieve peak performance and avoid injury. Beyond sports, it has the potential to be a useful tool for healthcare and military applications. Strategically placed cameras and powerful algorithms drive this portable, user-friendly system, eliminating the need for attached, movement-restricting sensors.
Plus, human performance professionals will connect with sports scientists at the upcoming International Human Performance Summit hosted by SwRI. Hear how your organization can join the Summit to explore the latest breakthroughs in human performance research.
Below is a transcript of the episode, modified for clarity.
Lisa Peña (LP): An SwRI technology is capturing 3D motion data to help athletes improve performance and avoid injury, and the potential for this portable, user-friendly, AI-driven system is expanding. How does it work and how is it changing the game? That's next on this episode of Technology Today.
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Hello, and welcome to Technology Today. I'm Lisa Peña. Today, we're talking about the Engine for Automatic Biomechanical Evaluation, or ENABLE, a portable, user-friendly system of cameras and computer vision algorithms that captures and analyzes motion. It's a game-changer for athletes and others working to achieve peak performance. Also, later in this episode, we'll discuss the upcoming International Human Performance Summit hosted by SwRI. It's an opportunity for human performance professionals to connect with sports scientists. SwRI engineers Ty Templin and Travis Eliason join us now. They are on the Enable Development team and they have some exciting projects in the works with this groundbreaking system. Thanks for joining us, Ty and Travis.
Ty Templin (TT): Thank you for having us.
LP: And Travis, thanks for hopping off a plane and coming straight over here to talk to us about ENABLE™ today. You were just at a conference.
Travis Eliason (TE): Absolutely. I was talking about this technology all week, so might as well get off the plane and do it again.
LP: All right, perfect. So in Washington, DC, this morning, with us here in San Antonio now to talk about ENABLE. So we want to start with the basics for our listeners. When discussing the ENABLE technology, it's important to understand some key words. So let's start with biomechanics. It's in ENABLE's name. So please define biomechanics for us, and we'll start with you, Ty.
TT: All right, yeah. So biomechanics is a fairly broad field. And a lot of what we're focused on with ENABLE is human movement biomechanics. And so that's specifically focusing on understanding the forces that act on the body and the effects of those forces which are the movements. So what we're trying to analyze with ENABLE is the positions, velocities, and accelerations of our body segments as we move.
LP: With your analysis, you are targeting human performance. So what attributes are under the human performance umbrella? What are you trying to improve with ENABLE?
TT: Yeah, absolutely. So when you have a detailed description of the way a person or an athlete is moving, you can start to make a number of just general fitness assessments. So you can look at things like speed, strength, range of motion or flexibility, and also balance and coordination. So once you have a general idea of fitness assessments, you can also look at specific questions of how movements can be modified or trained for specific applications to move more efficiently or in a way that is better for the health of your body.
LP: All right. So you bring up health, so we're talking about better performance in sports or maybe even better performance just for a healthy body in general. So how can understanding biomechanics improve performance? Let's put those two parts together and connect the dots for listeners.
TT: Yeah, absolutely. And this is really the million-dollar question and it's what we get really excited about, researching and pursuing. So our group has been developing this technology ENABLE for the last six or seven years, and it's finally at a point where we can start to answer that type of question. So the basic process for understanding performance currently is identifying what the best performers in the world do that subelite performers don't do or don't do very well. And then we can provide feedback or training that can help narrow that gap. So we're also pursuing other avenues other than just comparison between individuals that will provide more information for specific applications to improve your performance, but I think we'll jump into a little bit of that later on.
TE: Just a follow-on to that is, what we're providing different than what a traditional coach would provide? Is we're adding that quantification factor. So what I mean by that is, we're adding the math and the numbers to, we're trying to quantify what that coach's gut is telling them. A coach is naturally looking for all the same things that we're looking for. They just have an instinctual understanding of what good motion looks like. We're just trying to put the numbers so we can quantify it. So instead of a coach saying, hey, your shoulder was externally rotated a little bit too much that time, you can actually put numbers on it and then say, hey, you were 5 degrees out, coach then use that, give that feedback to their player, and then track their performance to see how they're incorporating that feedback they're getting from the coach. So ultimately, we're not replacing the coach, we're just giving the coaches the tools and the data they need to be even more effective in their training.
LP: So translating that for an athlete, that 5 degrees or whatever, those numbers, does that mean like, OK, bring your arm in a little more or throw a little bit more to this side or that?
TT: Right. And a lot of, one of the benefits of this system, too, is you also get some visual representation of your movement. And so, yeah, you may not know what 5 degrees more tucked or whatever the coaching tip is, but if you have a visual of, oh, here's what it should look like, athletes can really connect with that and implement that as they're training.
LP: So the visuals are part of the system? OK. What industries benefit most from biomechanics data?
TE: Yeah, that's a great question. I'll say, we call our system ENABLE. It's a bit of a forced acronym, but we chose that because we see this as an enabling technology to allow all sorts of new, interesting application areas to be built out that were previously infeasible. And this is applicable across a wide variety of different domains from sports applications, which we've been talking about so far, and that can be any sport you might want to think about. Military applications. The military is now viewing their soldiers as tactical athletes. So we're treating those soldiers as high-performance athletes, which they are. We also have medical applications. Using how people move as biomarkers for different disease states and using this as feedback for just general health. So really, it's a little bit hyperbolic to say, but we say that the applications for this are almost endless. It's really down to the creativity of the people who have interesting problems to solve where we can use this tool to provide them the data they need to make those interesting solutions.
LP: So you provided a sports example earlier. Could you provide a health example of how this system might work?
TE: Absolutely. One of the areas that we're very interested in pursuing is looking at neurodegeneration. So think of Alzheimer's or dementia. There's research out there that shows that you have changes in how you walk potentially years before you have measurable cognitive decline. So the idea would be is, if you use could use this technology to track how people walk just in your normal clinic, so think about go in for your yearly checkup and you get a gait analysis done, and this could be a flight, it's not going to be an ultimate diagnosis, but it could be a flag to highlight to the doctor like something's changing, maybe you need to look into this. Now there are other, that's one example. There are other disease states such as frailty and aging, helping people stay fit. Physical therapy applications, rehabilitation from injuries. The applications are almost endless.
LP: OK. A lot of great examples there. So back in May 2019 on Episode 7, Human Digital Twins, we discussed SwRI's Markerless Motion Capture technology, which, as we've discussed, is now called the Engine for Automatic Biomechanical Evaluation, or ENABLE. So there have been updates and new capabilities added since then, 2019. So tell us about this unique system in particular. You've already started, but how does it work? What is it doing for clients right now?
TE: Yeah. I mean, so like Ty mentioned earlier, we've been working on this for about six or seven years. It's a more challenging problem than we thought it was going to be initially. I like to say that we found a lot of ways how not to do it, but we've been persistent and pursuing because we've always believed in this end goal. And what we're excited about now is that over that time, we've now gotten the system to a state where it's good enough to actually be used and deployed and actually handed to people. And so the developments we've been making over the last few years have really been focused on accuracy, reliability, speed, and ease of use. So we've been packaging it up into a format that we can actually hand to our clients where it no longer needs that development team, technical expert in order to run it, increasing the reliability, decreasing the runtime, getting these processing done faster so it can be used in more deployed environments where they don't have to sit and wait for the results to come back.
LP: Just to delve a little further in a little bit more about this unique system, how does ENABLE collect this biomechanical data?
TE: That's a great question. So it's a, ENABLE is really a merger of two different technical expertise areas here at the Institute where, so Ty and I bring our biomechanical expertise from the Musculoskeletal Biomechanics Group here, and then we've partnered with our Artificial Intelligence Groups here at the Institute. And so how the system works is we've effectively trained our system to automatically identify 85 key locations on the body so that neural network or the artificial intelligence system automatically identifies all those key locations. We then do that from multiple cameras so we can get those positions in 3D space. And then we use those to drive a biomechanical model, which gets us to those biomechanics measures that we've been talking about. And so it's really a technology stack of different specialties all coming together to solve a very complicated problem.
LP: OK. And this system is unique because it does not use sensors. So how does it differ from a system that uses sensors and why is the ENABLE approach beneficial?
TE: Absolutely. So the biggest difference, just as you mentioned, is that we don't have to apply any external sensors to the body. So in the traditional way of doing motion capture, let me take one step back, is everything that we're measuring, you've been able to measure in a biomechanics lab for the last couple of decades using what we would call marker-based motion capture. If you've ever seen how they made the movie Avatar or any other Hollywood movies where they put all the little white balls on people and they in funny suits running around, it's the exact same technology. And that technology works really great, but the problem is is it's pretty much, it's usually limited to a laboratory environment because it's expensive, it's very time-consuming to set up, you have environment considerations, it's hard to use outside. So by eliminating having to put all those sensors on you, you can pull those measurements out of the laboratory and out into the real world. So effectively, what ENABLE is doing is just allowing laboratory-grade measurements to be made out in the real world. And then it's also making those measurements easier and faster. So what used to take potentially an hour of your subject's time plus a couple of man hours of processing afterwards can now be done in minutes. And so really, what this is doing is applications that were previously not impossible, but infeasible to apply in the real world are now easy to do. And so that's where this is an enabling technology to bring that measurement out into the real world.
TT: Yeah. And just one more thing that I'll add is don't have, these athletes that you're measuring or whoever it is you're measuring, they don't have to feel the fact that they are wearing sensors and know that they're in a lab getting their movement analyzed. This can be in practice, in game. So it's their natural environment, which is really a huge advantage of collecting data markerlessly.
LP: So you're going to get higher-quality data?
TT: Higher-quality, more realistic.
LP: And in real-time. Almost real-time. Near real-time.
TT: Near, well, so, yeah, so justm Travis mentioned this. It would take hours, maybe a day or two to process some of this traditional data, whereas this will take a minute or a couple minutes.
LP: So you're saving a ton of time.
TT: Saving a lot of time.
LP: From hours to minutes here. So you're collecting this data through cameras set up in certain spots. Tell us about the camera setup. How does that work?
TT: Yeah. So basically, all that's required as input to the software is video data from multiple cameras. So those cameras have to be synchronized. So the way that we typically collect data is we have tripods and put the cameras around the capture volume. We're identifying those 2D points in each camera frame. And so they need to be synchronized so we can get the 3D data from all the different cameras.
TE: But I would add to that, we are, our software is camera-agnostic. We're not tied to any specific hardware. So we have portable cameras, which are just commercial off-the-shelf cameras basically bought off of Amazon that we can use. We're also integrated with some commercial partners where our engine can be plugged into their camera systems so that if a client already owns one of these more traditional camera systems, they can just plug our system in and use what the hardware they already have. Or if our client wants to set up a custom solution, whether that be they've got surveillance cameras or they already have in-stadium camera systems set up, we can work with all of that. So ultimately, like I mentioned, our only requirement is that we have multi-view synchronized cameras.
LP: All right. And that's where the user-friendly comes in.
LP: Any user can use it with the cameras they have. That's great. So artificial intelligence is playing a huge role in this. The cameras are collecting the data, but then it's being run through software, and that's where the AI comes in, the algorithms come in. So tell us a little bit about that.
TE: Absolutely. So where the AI comes into our technology stack is, I started talking about it a little bit earlier, is where we're having that AI system identify those key locations on the body. And the way to simplify what it's doing is effectively we're feeding that AI system an image. And if I gave you an image, you could sit there on your computer and I said, hey, click on their left elbow. Click on their right knee. You could go through that image and identify all of these key locations. Effectively what we've done is we've trained that neural network to do that automatically so that you don't have to have a person sitting there clicking points, which biomechanics researchers have done that for years, too, and that's a traditional thing that you have interns or grad students do and it's always a pain.
TE: It's time-consuming, it's no fun. But you have an AI system do it for you. Now you can increase accuracy, because obviously humans can make errors. You increase accuracy and significantly increase the speed. So hand-labeling data in the past could take you days. Now it's done in minutes.
LP: The miracle of AI.
LP: So you've done a great job of telling us how the system works, but walk us through setting this up. What would I experience if my movement is being analyzed by ENABLE?
TE: That's a great question. So as a subject, your experience would be you would, whoever's running the capture would, say, come into your capture volume. So you'd walk into the capture volume. And then they'd run you through some movement pattern, whatever that researcher is interested in tracking, or that coach. So that could be functional movement assessment. So that could be, hey, come in here and do some squats and lunges, maybe some jumps. Or if you're an athlete, maybe you're looking at a pitcher, you have them, you have your capture volume around the mound. You just have the pitcher come in and throw 10 pitches. Your experience, that's it. There's no setup, there's no poking and prodding. You walk into the capture volume, do the motions they want you to capture, and you walk out. So as a subject, it's completely non-invasive, super easy to do, comfortable, doesn't require anything of you. Maybe, Ty, you want to talk about from maybe the running side, like how would a researcher or a coach would actually run the system.
TT: Yeah, so you, yeah, so like Travis said, all it is is it's a, you click Start and that records the video, click Stop, and then you click Process. And what you get from that is a model of the individual. So it's scaled to their body type, so their segment lengths, height. And so then from there, we also give, it's a report that has all of their degrees of freedom, how they are performing the motion, and then we look at specific things for the different applications. So in baseball, a lot of it is looking at the kinematic sequence. How are you transferring energy from the ground to your torso to your arm to the ball? But it's going to be different for every application, every sport.
LP: OK, so let's specifically talk about a recent project. ENABLE was used to assess and optimize the performance of baseball pitchers. So tell us about this project, Ty.
TT: Yeah. So this was a project that stemmed from a collaboration with Dr. Sakiko Oyama at UTSA. So we started talking to her about some of the updates we had been making that we've been talking about here. And it turned out to be a really good timing because she was doing a study that, where she was looking at using a marker base, the more traditional method of motion capture to look at pitching mechanics. So we decided it would be a really great opportunity for us to get some quantitative results on how accurate our system was compared to the more traditional system. So we helped her set up the cameras in her lab and we processed 11 pitchers throwing a number of fastballs and compared the results from our ENABLE system to the marker-based system. So we're really pleased with the results. The accuracy compared really well to the marker-based system. But one of Saki's goals. and one of our goals is, so what? Great, we can measure the way a pitcher moves, but what do we do with that information? And so we wanted to take our analysis a little bit further in this project and look at not only how to measure the way that these pitchers are moving, but identify ways that we can make slight modifications to their technique to reduce injury risk while maintaining performance.
LP: Has that part of the project started? And what did you see if it has?
TT: Yeah, absolutely. So yeah, so to answer the, so we were specifically looking at running an optimization where we were looking at one particular pitcher. Can we optimize the way, or reduce, reduce the amount of stress on their shoulder while maintaining ball velocity? So those are the objectives of this optimization. Reduce shoulder torque but maintain ball velocity. And the reason we picked the shoulder is just because a lot of times, pitchers that's where they're getting injured. It's the shoulder, also the elbow. This framework could be modified to account for either of those. We just chose the shoulder for the proof of concept. And so yeah, when we ran through the optimization, we found a solution that reduced the peak shoulder torque by about 30%, and we were keeping ball velocity within about 0.1%. So a substantial reduce in the torque and the stress on your shoulder while maintaining ball velocity pitching performance.
So yeah, the big question for us is, how do we get there? What was different about the optimized solution compared to the actual movement? And so what we found was the primary difference between the two, the optimized and the experimental, was the optimized solution produced a greater braking force on the pelvis and the torso. And so one way to conceptualize what's happening there is if you imagine you're riding a bicycle at full speed and you slam on the brakes of your front tire, what happens is the bike stops, the rear tire flips over the top, and maybe throws you of the handlebars. And so that's basically the same concept that's happening in the pitching delivery, is the more forcefully you can apply brakes with your lower body, the more the force that gets transferred to your upper body and then ultimately to the ball.
LP: Success. Figuring all that out.
TT: And this isn't, and this is supported by some other literature that's out there, but it was really interesting to be able to see that, OK, we can tease out these things that are not aren't necessarily completely novel, but you can start to look at, OK, well, what does that look like for me? What do my mechanics, what are the small changes that I need to make to get to that outcome?
TE: And just add, too, it was a good validation that the point of this project was proof of concept to build out this technique to do this optimization, and it was a good validation that what our optimization and model came up with is matching what pitching coaches are telling their players to do, what's already out there in the literature of saying, what is ideal to do in a pitch? We didn't force it to go that way. Our optimization came to the same conclusion, which is that first validation step of, if it was coming up with something radically different, people have been pitching for a long time, you would think someone would probably would have tried it. So it was a good validation that is coming, and then to Ty's point, now that we have that methodology in place, we can start looking at more subtle, more individualized things where it's not just how do you make a pitch better, but it's now, how do you make this specific pitcher better? And big-picture, of course, for coaches is winning more games.
TT: Absolutely. It's throwing faster, getting more strikeouts, there's other teams scoring less runs.
TE: And getting less injuries.
TT: And also not, yeah. You're able to keep throwing the same number of pitches, number of innings.
LP: Yeah. So wins and keeping your players healthy.
LP: All right. And so now, you're expanding to other sports, which is great. So tell us about plans to use this technology in basketball.
TT: Yeah. So baseball is, we started with baseball because it's been the sport that has been the earliest adopter for biomechanics technology and a lot of other technology as well. So we see basketball as the next sport that's really going to start using and benefiting from biomechanics technology. So yeah, so we're starting to try to look at, OK, what are ways that shooters, what makes a good shooter a high-percentage shooter? Are they more consistent? We're tracking things like the flight of the ball. So in addition to their movement, we're actually tracking the ball as it leaves their hand, goes through the hoop. We can look at things like, OK, how does the trajectory of that impact shooting performance?
TE: And how did the mechanics of their shot affect that trajectory? What are the mechanics that lead to those most consistent shooters with the goal of effectively creating a shooting lab where players can come in, they can shoot their shots while they're being captured, and then they can get key feedback metrics that are defined by their coaches on these are the things that we want you to do, and here, we're quantifying all of those things and giving that back to you. And you can imagine for something that's very repetitive like basketball, shooting free throws, for example, you could track yourself across a 100-shot free throw session and how consistent are you? Like, yeah, you can shoot 10 shots perfect, but then shots 30 through 60, you were off. Or, that's just an example. But you can imagine how you can use this because consistency is the name of the game. So it's not just, can you do it once? It's, can you do it the same way every time, time and time again, with the goal of making that higher percentage free throw shooter?
LP: Do you foresee this being useful for teams of all sizes from little league to pro teams?
TE: Absolutely. The way we view this is the trickle-down. So if anyone's familiar with the F1 model where these car companies will spend ungodly amounts of money for their F1 teams to develop these crazy technology, but all of that eventually trickles down to your everyday road cars. That's why it's worth it to them to make that investment. Antilock brakes, suspension updates, traction control. All of that was originally developed in the racing space and it trickles down. Similarly, we're starting out with professional sports teams, collegiate sports teams, military applications because they're the early adopters, but our vision for this is that this will trickle down through all levels from high school, middle school, little league, to apps on your phone where you might have a golf app where you're working on your swing in your garage and it can be giving you feedback. Back to those clinical applications that we were talking about earlier. We want to see this out in the real world used by everyday people.
LP: I would love that. We were at golf practice with my son yesterday. It would have come in really handy.
LP: Right? So what type of feedback are you getting from people who are these early adopters, who are already using the system?
TT: Yeah, so I think, especially for the users who have used the traditional marker-based system, it's a huge improvement in the speed like we've been talking about, and the effort that goes into collecting this data and getting something meaningful from it. So I think there's a huge like, oh, this takes a big burden off of us and off our research team, we can get results that we want really pretty easily. And I also think kind of one of the things that they appreciate about ENABLE specifically is the ability to customize the software. So if there's a specific model that you want to be able to use, so for pitching, maybe you care about a detailed shoulder model, or if there's a specific applications that you have mind, we are able to modify the underlying code and work with you to meet that specific need.
LP: How can someone interested in ENABLE get the system for their organization?
TE: Yeah, that's a great question. And I say, as SwRI, we're a nonprofit independent research organization. So we have a lot of flexibility in how we use, how we work with our customers. And so we do a broad range of different relationships with our customers. Everything from just a pure license, like if you are a biomechanics researcher or a sports scientist, you're comfortable with doing motion capture and you already have the "so what" answered on the backend, you can just buy a license and just use it. If you need some more help or more customization like Ty was talking about, we like to work with our clients, figure out what their problems are, what are they trying to solve, and then we work collaboratively with them to build a statement of work and build a project where we can then go that extra mile and kind of customize it for their specific application. And that can be small, that can be big, it can be anything in between. We have a lot of flexibility in how we work with our customers.
LP: All right. So best place to get all the information about ENABLE.
TE: Yeah. The best place would be to go to our website, which is enable.swri.org. That's going to have some information about the system on there, some example videos that you can see, but also have contact information for how to get a hold of us, and happy to talk with you about what your specific needs are and how we might be able to help.
LP: All right. enable.swri.org. And have you used the system, either one of you used the system to analyze your movements, and have you learned anything new about yourself?
TT: Yeah, so this is a great question and definitely a motivating factor for me to dig into of the sports performance side of using ENABLE. One thing that I've found about myself, as we're developing this basketball shooting lab approach, is, one of the things that whenever I am consistently missing shots, a lot of times it's because the arc of the flight of the ball is too low. And so that, and in one sense, that's really good because you're minimizing the speed and minimize the variability of shot. But if you shoot the ball with more arc, there's an effective area of the rim that's larger, and that gives you a greater chance to have the shot go in. And so that's something that I didn't realize about my, yeah, my technique that I've been working on here and there. I'm playing pickup at lunch and things like that.
LP: Yeah, really cool. Have you tried, Travis?
TE: Yeah. I mean, absolutely. I mean, as we've been developing this, we've, all the people on the development team have been subjects many, many times. I'm not the athlete that Ty is, but I've obviously tracked myself, and one of the interesting things is just showing the sensitivity that the system has is, I've had multiple shoulder surgeries throughout my life, and so I have limited range of motion in one of my arms. And so looking at my data, I can see a clear difference between my left and the right. My uninjured arm versus my injured arm, I can see that clear difference. So it's just an example of how those sensitive measures can pick up unique things about each individual that you might not know or might not see, or a coach might not know about or might not see just innately without that quantification.
LP: Yeah, those traits that affect performance. So really neat that you both tried out the system and have learned something about yourselves. So what's next for this technology? What do you envision for the future? You talked a little bit about seeing it go from the top-down. What's the big picture for it?
TE: Absolutely. I mean, I think this is just to reiterate, we really do the future of this as doing that top-down approach. But ultimately, what we want is we want this technology to get out in the real world where it can solve real problems and help people. We love our professional sports teams that we work with, but we didn't set out to develop this technology to make professional athletes better. We love that they use it, but that was not our goal. Our goal is to improve the health and performance of everyday people. And so as we build this out, we're going to continue looking for new partners and clients who have interesting problems to solve, whether that be in the sports arena, really love to get down into the high school and youth sports world where we can help kids develop in a proper, with proper mechanics so they minimize the risk of injury going forward.
We're really pursuing a lot of really interesting applications with the military, both in optimizing performance, but even bigger on trying to predict injury so we can reduce injury rates within the military, which is a big problem. But then also, the medical applications we talked about before. I would love to see this get out more in the medical community, whether it be in physical therapy, using this as a biomarker for different disease states. The future is. it's exciting, but unknown. I like to tell people, my guess and my prediction is that five years from now, we're going to be doing stuff I can't even guess right now. Someone's going to come up with us with a great idea, they're going to say, hey, can your technology do this? I'm going to say, I've never thought of that, but that is a great idea, let's go do it. And so I guess the short answer there is, wait, we'll see. And we're excited about where we're going.
LP: That's very much the SwRI spirit and way of doing things. We conduct research and development to better humanity, and ENABLE is fitting right into that space. So thanks for sharing about that. Before we go, though, I do want to talk about this upcoming summit. So Southwest Research Institute is a leader, as we've said, in human performance research. So we are hosting the International Human Performance Summit. It's coming up this month, July 20th through the 21st at our SwRI San Antonio campus. The theme this year is Emerging Technologies and Solutions in Human Performance. So tell us about this conference. What can attendees expect?
TE: Yeah, so this is a conference we've been running for a few years now. And the goal of this conference is to bring together human performance specialists and experts from a wide variety of different specialties because human performance can go everything from the physical performance that we've been talking about to nutrition, to sleep, to psychological performance, to you name it. So this conference is really to try to get that broad spectrum of getting people across all of those different specialties to come and speak. We give them hour-long slots so they can really dive deep into their specialty. And our goal there is that we can have sports scientists, performance staff from different teams and organizations, universities can all come.
And our goal is that when they leave, they leave with something actionable. They've learned something from the experts we have come in and talk, something that they can apply to their programs with their players or their soldiers, whoever they might be supporting, but the goal is to try and expose everyone to the fields that they may not normally see and hopefully bring back those tangible nuggets of information that can come back and incorporate.
LP: It really is such a great conference. I was able to participate last year, so a lot of great information. So the summit will feature national and international speakers. Topics will include motion optimization, sprint biomechanics, the human digital twin, strength and conditioning applications, and more. We do want to let listeners know the registration window is closing soon, so if you are a sports, military, or health care professional interested in attending for your organization, don't wait. Head to the event website at ihps.swri.org. The summit registration cost of $300 includes course materials, two days of interactive presentations and discussions, and breakfast, lunch, and refreshments. Certainly a great opportunity to connect with sports scientists.
And if you're listening after July 21, 2023, make sure to keep this event on your radar for 2024. Details will be posted at swri.org under the Events tab. OK, guys. Really fascinating discussion today about ENABLE. I can't wait to use the ENABLE app. That, I'm sure, is around the corner. So thank you, Ty and Travis, for stopping by to talk to us today. Travis, for flying in straight to us today from DC. We loved hearing about this game-changing system and the upcoming summit. Thank you both.
TE: Yeah, thank you.
TT: Thank you.
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Ian McKinney and Bryan Ortiz are the podcast audio engineers and editors. I am producer and host, Lisa Peña.
Thanks for listening.
The Engine for Automatic Biomechanical Evaluation (ENABLE™) combines Southwest Research Institute’s biomechanical modeling expertise with computer vision and deep learning to develop powerful algorithms that enable accurate, reliable markerless motion capture for a variety of clients. Following years of internal research and applied client projects, SwRI has introduced the ENABLE markerless biomechanics modeling engine for biomechanical analysis in sports, medicine, research and other human performance applications.