In the fourth episode of Moving Digital Health, the new podcast from MindSea, CEO Reuben Hall talks with Dr. David Burns of Halterix. David holds a PhD in biomedical engineering and is currently in training, at the University of Toronto, to become an orthopedic surgeon-scientist. As if that weren’t enough, he is also the chairman of Halterix, the pioneering motion recognition company he founded.
It was during his surgical training, working with patients in a clinical setting, that David observed the make-or-break role patient adherence plays in the success of a prescribed physical therapy. He noticed that although patients’ efforts at home were critical to recovery, physicians were unable to get a complete picture of such efforts.
David describes the research and development process he went through creating technology to mitigate this problem. He realized that if he wanted to get the technology to the patients he believed it could help, the best way to do so would be to found a company—and so Halterix was born.
Taking any idea to market is a complicated process. When that market is healthcare, and when that idea makes use of artificial intelligence (AI), there are many additional challenges. David explains how regulatory requirements can vary depending on the level of human versus AI involvement in the decision-making process, and also illuminates further considerations unique to working with AI technologies.
As the company’s founder, David was the one to assemble the Halterix team. He shares how he went about recruiting for the necessary internal expertise, and he and Reuben discuss the Halterix team’s work with MindSea as well. David gives us a preview of what’s next for Halterix, as well as elaborating on other emerging technologies and their exciting potential applications.
David’s expertise in business, engineering, and orthopedic surgery makes him a fascinating source of information on innovation in the world of digital health. We thank him for joining us to share his insight and his story, and we hope you’ll enjoy his episode of Moving Digital Health.
Welcome to the MindSea Podcast series Moving Digital Health. Our guest today is Dr. David Burns, physician and founder of Halterix. Welcome, David. I do want to say up front that Dr. David Burns and Halterix are clients of MindSea, we like to say partners because that’s the way we view our clients when we’re building tech together.
But we can get more into that later. I would like to just start off with you, David, and maybe you can tell us a bit about your background.
David Burns (00:00.34)
Thanks, Reuben. So I’m finishing my last year of training in orthopedic surgery at the University of Toronto and also an engineer by background. I worked in the nuclear industry for a period of time and then switched into medicine after that fell apart after Fukushima melted down. Anyhow, during surgical training, I had the opportunity to do some research and a Ph.D. in biomedical engineering, which is where I started focusing on digital health, wearable technologies and artificial intelligence technology and when I first met Reuben.
Ultimately I’m going to be an orthopedic surgeon scientist, working orthopedic trauma and hopefully contribute to innovating and bringing these technologies to improve care for patients suffering from injuries.
Wow. I didn’t know about your background in nuclear science at all. That’s really interesting. And then the pivot into into medicine. So along that journey, what led you to start Halteri x on top of everything else you had going on.
David Burns (00:01.43)
I spent some time working towards developing technology for helping patients engage with their physical therapy. I first noticed this problem actually in clinical setting where I’d be seeing patients with rotator cuff problems, the most common shoulder problem, and the main treatment for it is therapy. And when I’d ask these patients who are suffering from this, you know, how was their therapy going? I sort of felt like my dentist must feel when he’s asking me about flossing.
And it was some awkward conversations. And then I did some research into it and I discovered that this treatment is actually really the most important part of treating this condition. And it became quite clear to me that many patients weren’t making the most out of very important treatment and getting their full recovery.
And that led me down this path into trying to solve the problem. And it started first with trying to understand it and develop some technology to tracking physical therapy, using a smartwatch. I was able to get some research grants funded at the university where you can use this with patients and see how they’re participating in their home therapy.
And as this project was wrapping up, I need to find a home for the technology so that as well as the learnings from the research that we did so we could actually use it to help patients in the way to do that. The best way to do that is to start a company so I found some friends, and that’s what we did.
Cool. What was the hardest thing about getting the idea off the ground when you kind of identified the problem and looking at building something to solve it?
David Burns (00:03.26)
Well, it’s an ongoing journey. I still kind of feel like I’m trying to get this idea off the ground. And so there’s lots of hurdles in the way, especially when you’re tackling the complex, challenging problems. They involves many systems, many stakeholders. I think the most important thing is to recognize what it is or what the problem is.
And then to take your first steps. And then those steps lead to more steps. And if you persevere, then maybe in the end you can make a difference.
Yeah. Building solutions in digital health and health care is certainly a long journey. Maybe you could tell us about some of the findings from the Sunnybrook studies and how those have helped guide the process.
David Burns (00:04.17)
Sure. So the first research that we did with patients was actually patients who had this problem called rotator cuff pathology and who are undergoing physical therapy at Sunnybrook. And so we recruited them into our study, and our goal is really just to observe them in their participation of physical therapy, both in our clinics as well as at home.
Patients are expected to do almost 90% of their therapy at home. And so observing what they’re doing in clinic is really not enough to understand the process and sort of using their smartwatches. We were able to track their participation over the full course of treatment and look at things like how were they engaging in their therapy, How does that change over time and what are the different factors that impact that?
It was interesting to me that the engagement actually up front started off pretty well, so most patients actually were fairly engaged in their therapy in the first few weeks, but it rapidly dropped off. And by the end of treatment, which is a total of three months, most patients weren’t doing any therapy at home at all anymore.
That’s such a familiar story. It’s like the New Year’s resolutions is you start off really strong and then, you know, in a few weeks, almost everyone’s dropped off.
Now, we started working together about two years ago, I think. So we did a blueprint to help define how an application or a mobile app could help with this problem of you know, adherence to a physiotherapy program. We defined the user experience, talked about user journeys, put together the plan for development and the scope of features, and then started building.
So I’m curious from your perspective, you know, how did that process go for you? What were some of the benefits of working with an external team?
David Burns (00:06.23)
I feel really grateful to this day that I was able to discover MindSea and that we partnered with you to build the next generation in our products and user experience. The team that I was leading at the time that I’m still leading is very technical and we’re focused very much on the clinical aspect of what we’re doing.
The artificial intelligence. And, you know, user experience is something that we didn’t have a lot of experience, and I found in the first implementation of our application, we’re taking a lot of our talents and focusing it, spending their time in that area, which is crucially important, but not their core skill set. And so because we were very much technology focused with our group by partnering with you, I found that we were able to really focus on the most important technical aspects of the project.
And then, you know, with the team that you brought, I felt that everything came together very seamlessly in terms of how that was implemented and presented to the user and how they would actually end up using the technology. And some of the things that happened there too, was a lot of the user design aspects of it I hadn’t fully considered.
So there are many points that were brought up from your team and flags that were raised on how I was thinking the technology would be implemented that maybe in my mind made sense. But for somebody who’s used to building applications that people use day to day really recognize that that wasn’t the best way to implement it.
Yeah. The focus on the user is so important. And so many times we, you know, work with people who may have made some assumptions about how a user might interact with software and in a particular circumstance. And then when we go to, you know, let’s say do a user testing session with a prototype, then we learn a lot about how they actually think and comprehend and make a mental model of the system in their own right.
And thankfully, when you do application after application, you start to see some of these patterns and you just learn from those mistakes you’ve made in the past and you get a much better result right out of the gate.
I love that the partnership aspect, because even though we do a lot of work in digital health, we’re not doctors, we’re not physicians. We don’t understand that world nearly to the depth that your team does. So I think when I see those, you know, those two kind of like or not two, but, you know, multiple specialized skills come together on the technical side and user experience and design and product, when those teams, you know, share their knowledge and work collaboratively, those are the results can be really amazing.
So let’s talk about Halterix right now. How is the the products that you build, so and just to kind of outline what that is so there’s the there’s the patient smartphone app that patients use to to track their exercises and there’s also the smartwatch application that sends the data to the phone. The phone is sending the data to your algorithm on the back end. And then there’s also the physician, the tablet app that the physician can see the patient’s data and make modifications to their exercise routine.
So this system altogether, how do you find that it’s changing the way physicians are monitoring and learning from patients progress?
David Burns (00:10.34)
So still at a very early stages, we’ve deployed now the first iteration of this application to a number of different private clinics across the country, really for some further user testing, mainly to test how it’s implemented in private clinics with their physiotherapists outside of the academic institution and it’s actually going into testing as well at Sunnybrook, and we’ll be recruiting our first patients in this year.
So I don’t have a lot of experience to tell you sort of at this stage how we’ve been able to benefit with patients. But that’s what the sort of ongoing research is looking at. We found in our initial study that there were certain barriers to recovery or to engagement that we wanted to try to address with the technology.
So, for instance, one of those is this concept of self efficacy, which is how well a patient feels that they’re able to do their exercise independently. And so for those who didn’t feel confident then they were very unlikely to participate and therefore unlikely to improve. So, for instance, one of the features of the application is it gives us this reading of exercise performance.
It sort of reinforces how a patient was trained to do their exercise, taking into account their own limitations and how they’re able to do it under supervision, and by helping encourage patients that they’re doing things correctly. We hope to be able to address that one barrier. And there’s some other areas as well that we’re addressing with the application.
Yeah, so patients are being measured on their own personal baseline instead of, you know, just some benchmark for like, you know, the average individual or something like that.
David Burns (00:12.13)
Absolutely. And that’s very important because the patients are being treated with these technology and with this physical therapy. It’s not the equivalent of tracking someone’s exercise in a gym because their performance and their ability to perform those exercises depends on the severity of the pathology that they have or the stage of recovery that they’re at.
Yeah, so in that sense, it has to be very unique to their situation.
David Burns (00:12.42)
Now this is currently the application specifically for those shoulder injuries. Do you think it would take much to expand that into the, you know, other injury and rehab programs?
David Burns (00:13.01)
So we’re using the technology now in a pilot for back exercises, so back and shoulder together and make up the majority of all physical therapy. I think about 70% of physical therapy is just on those two. We’re also developing technology to use smartphone video or just 2D cameras, depending on the body part, sensors versus camera they have different advantages and disadvantages.
And so really at this point, we’re building out the technology as well as the types of sensors that we support in order to provide a comprehensive solution.
And how does the video capture work? Does the user have to, you know, wear anything or, you know, do special angles or is it pretty adaptable to whatever situation it finds itself in?
David Burns (00:13.57)
It’s fairly adaptable. I mean, the video does require that they’re facing it for most exercises, which I think is also convenient to the patient, because they’re able to receive prompts from their device as they kind of go through their exercise. And it’s simultaneously continuously capturing their video and providing some feedback about the performance.
Any other internal challenges that the back exercises and rehab poses in addition to the shoulder?
David Burns (00:14.31)
Yes. So the back is challenging from the in the sense of there’s not a device that people use already in their day to day lives all the time. That’s ideal for tracking back exercise. I mean, most people do have a smartphone and a smartphone camera is a good option. But like you said, it does have limitations on field of view and set up.
And, you know, it’s really not quite as convenient as something like a smartwatch. Unfortunately, there’s no smart shirt or something that people have or comfortable using in their day to day lives. And so to address that, that’s why we’ve looked at video and we’re also looking at partnering with some other companies to provide a garments that may be suitable.But again, the market for that or the applicability of that is more challenging.
Yeah, the watches are much more widely adopted. And also, you know, just small and fairly unobtrusive, what about like a halter, you know, something you can attach to your clothing would that be an option to?
David Burns (00:15.44)
That is an option. And we’re doing work to sort of come up with the most convenient and effective configuration of worn sensors for tracking the back. But it’s an effort that’s in progress on our end.
Yeah, There’s definitely a lot of innovation happening in that space right now. I think a lot of companies are looking for, you know, what are the other sensors and kind of unobtrusive ways that we could be tracking movement in people to get better data, whether it’s for health and fitness, you know, or for more medical applications.
David Burns (00:16.25)
What do you see? Do you see any, like, really cool or kind of like far out, you know, futuristic ideas while you’re researching in this realm?
David Burns (00:16.41)
So I work a lot with sensors and AI, and it’s not limited just to the physical therapy space, although that is an area of interest. There’s lots of very far out technologies in orthopedic using sensors. One of them is implantable sensors, which have great applications. I can give you a few examples.
So one is on monitoring bone healing. We take frequent X-rays to assess a patient’s progress after they’ve had a fracture related to repair. As to how they’re progressing with they’re healing, they’re now sensors in the research stage that you can implant attached to your fixation that will measure the strain through the fracture and be able to track the healing progress on a day to day basis.
So this is really convenient for patients because it’s kind of continuously recording progress. They don’t need to come in necessarily to have an X-ray done every couple of weeks, get it exposed to additional radiation. And I think that this is not something that we’re using it clinically, but that there’s a lot of promise in having these smart devices or sensors on implantable orthopedic implants, like for fracture repair. This is one example,
but another one where you can imagine this would be a total knee replacement that could track elements of the where that’s occurring or the number of cycles or have some information that would be helpful in future decision making.
Well, that’s really interesting. So the implanted sensor is, again, like sending the data to the smartphone, and then from there, it’s being captured and utilized for the physicians.
David Burns (00:18.30)
I mean, this is not a product that’s available or Health Canada approved. but you’re asking about sort of far out their ideas. And this is an emerging technology, just one example of instrumented implants.
Yeah, it’s really interesting to know about some of those like cutting edge research and, you know, implementations, even though if they’re, you know, they might be years down the road before they’re really out there. It’s still really fascinating to think that, you know, you could have, you know, a hip replacement or a knee replacement and that is actually kind of self-monitoring It’s function.
So it’s you know, it’s one thing for the individual to say, oh, it doesn’t feel right or, you know, something’s wrong there. They might just ignore it and not get looked at. But if the, you know, the implant or joint itself, they’re sending data that says, oh, there’s an issue that needs to be looked at, then things can get taken care of sooner or later. There’s lots of different ways you can imagine, you know, scenarios like that playing out.
David Burns (00:19.47)
Absolutely. That’s exactly right.
And when an instance of a bone fracture, though, like if that’s just, you know, an injury, you feel that would heal and then and then get better, what happens to the implant? Do you think it would be just kind of left in there and does it degrade or is that something that has to be removed?
David Burns (00:20.11)
That’s a good question. So most implants that we use for fracture fixation are suitable to be left in place permanently, depending on where the fracture is located, how prominent that is with respect to the patient’s skin in their body habitus, sometimes we do remove them on an elective basis after healing has occurred. So it’s not unusual to necessarily remove the implants, but it’s not something that we recommend in all cases.
And, you know, again, since they don’t have a Health Canada approved device that I can talk about for an implantable strain sensor, you know, it would depend on how it was approved for use, whether it would be something that would be recommended to be removed after the fracture has occurred. I expect that that would probably be the case.
Yeah So are there any other exciting projects in digital health? Sunnybrook, other colleagues exploring and kind of pushing the boundaries in that field.
David Burns (00:21.20)
In digital health. By that you mean like more.
Well, I guess we can use it like a, you know, loosely like the intersection of health and technology, right. Where, you know, there’s some sort of tech that is helping with the treatment, whether it’s, you know, software based or or hardware or whatever.
David Burns (00:21.50)
There’s a huge number of projects. The lab that I was formerly a PhD student with and seemed to collaborate with has a number of projects at the intersection of technology and health. Many of them are A.I. focused and some of them are more device focused.
One that’s really interesting and won some awards recently is a system that a friend of mine, Zachary Fishman, had built, where for someone who has lost part of their face from a traumatic injury, sometimes this can be reconstructed through the knowledge of what their face looked like before, and the ability to reconstruct that depends on surgeon skill to a large extent. If it’s injury on one side of the face or a unilateral injury, the other side is a template. But sometimes you can have a severe injury that impacts both sides of the face or centrally in the face, where it would be very challenging to have a 3D understanding of what that patient looked like before and to be able to restore that.
And so he’s created some really interesting technology where he scrapes data from their social media pictures that they have from their friends and family, and has an algorithm where he can take those pictures and then which are taken from any angles and then reconstruct what the 3D shape is.
And he’s validated that with data sets where there is a 3D, C.T. Scan and pictures, if that makes sense. And so using that they can then template their corrective surgeries and implants, some of which are custom made to provide that person a reconstruction so that they look the same to all their friends and family after that’s been done.
Wow. And any more of those projects you’re able to share or.
David Burns (00:23.51)
There’s another friend of mine, his name is Jay Toor, he’s doing a lot of work with scheduling and artificial intelligence. A lot of the work we do at the hospital seems at first to be quite unpredictable. You know, I can go on for a call shift and think that, well, I’m not really going to know what’s going to happen today.
I don’t know how many car accidents there will be, but when you actually have a lot of data to look at over the long term and you have sophisticated algorithms, there’s actually a lot of patterns that happen based on days of week, month of year, certain holidays. And so what’s actually happening at the hospital can be predicted. And once you have that knowledge ahead of time, you can actually schedule and resource the hospital appropriately for, you know, the range of what could be occurring.
And that can result in a lot of savings for the hospital and the health care dollars that can be allocated elsewhere, save taxpayer dollars in the end. I think that’s a really exciting project. It seems simple, but it’s the application of technology on the front of medicine that I think can be very useful.
Another thing that I think personally is very exciting is with computing power and again, AI technology, we can also get a sense of a fairly quickly sort of look back through records and say, okay, what are some patients who had a similar type of illness or injury or maybe risk factors for a particular disease and then be able to predict the outcome for that individual patient.
These systems are being deployed and used in research so that you can start to individualize decisions for a particular person based on what’s going on for them by having access to all the data at once to search.
Wow, that’s really some really interesting projects going on there at Sunnybrook.
David Burns (00:26.03)
It’s a big difference from decision making where we look at where you could look up a published paper that shows, you know, patients between this age and that age with this particular condition. This will be better than that. But if you have access to all the data at once in the algorithms that can properly search and model that, then you can start to understand the decision making on an individual level. And that’s also a really impactful aspect of the technology.
And how about the next step? Once, you know, once you see you have the AI technology that it’s proved itself through research and clinical trials and then to take the next step into implementing it, into a health care system. What are some of the challenges involved in that?
David Burns (00:26.52)
There’s also challenges. So when an algorithm is being used to inform decision making, it has to be rigorously validated. It depends on whether or not sort of there’s a human in the loop for that decision to something that’s fully automated, obviously has a lot greater, has much greater capacity for harm. And so if it’s just informing a clinician, then that’s more straightforward from a regulatory perspective.
And then there’s other challenges as well, for instance, funding and supporting these sorts of initiatives where it’s challenging and takes time and funding to demonstrate the efficacy of a technology in a rigorous way and also to justify an incremental expense relative to the improvement in order to have it something that a public health care would be interested in.
Even if it’s something that seems convenient and useful from a clinician’s perspective, for it to be paid for by the public health system requires multiple additional steps beyond just that.
And another interesting challenge with some of the AI technologies is that they’re very sensitive to the data that they’re based on. And so if you have a model that takes in a whole bunch of historical data and takes predictions from that, and then alters behavior.
So let’s say that model is then used to do things differently, then it may no longer actually accurately predict outcomes in the setting of the change behavior. And so you need to put and model has to be constantly updated with new data to make sure that, you know, in the current context, whether it’s affected by the model itself or it’s affected by new treatments that are available or different clinician practices.
You know, maybe there’s a new landmark study that then changes how things are treated, that all needs to be constantly updated. And that’s a huge challenge as well.
Yeah. Like you said, like the AI has to be constantly learning and evolving to not really stay accurate, but then hopefully improve over time as well. You talked about having a human in the loop, and obviously that’s a big difference when you talk about, you know, having an AI that’s just you know, making a decision versus kind of being assistants and helping prevent or suggests and make decisions, and then it’s a physician who’s kind of just taking that as one data point and making the final call. Right?
David Burns (00:29.43)
Absolutely. And it’s important to realize that what we’re calling AI today actually has a lot in common with what historically we would call a statistical model. We’ve had statistical models that predict, let’s say, heart attack risk from things like age and smoking and sex, and that that type of statistical model is often or can be used to inform what type of medications might be prescribed in a preventative fashion or what sort of lifestyle modifications might be suggested.
And so we’ve been using these statistical models based on retrospective data to inform decision making for individual patients. For many years. And this is something that doesn’t require regulatory approval per se to make that decision. But where things are changing now is that with what we call AI technology, we can process much more complex data streams, which can include imaging. It can include time series data like sensor data.
And it can be sort of more broadly applied and help with more sophisticated decisions. And so there has to be some thought into when these models, especially if they’re opaque, where the clinician who’s interpreting the output of the model isn’t able to understand how or why that is making a certain recommendation, then that becomes more risky.
Whereas like historically a statistical model that’s just based on heuristic variables or specific variables like age, and it’s a formula that you can calculate. It’s very clear to see why that recommendation is being made and for that to be validated. But if it’s an image, there’s unfortunately not a good way to know necessarily why the AI is making a certain prediction that’s predicting unless the models are made in a very thoughtful way.
Right. And of course, the more complex the are, the harder it is to make invisible. You know, how those calculations and insights are being arrived at.
David Burns (00:31.59)
Yeah. And there’s many examples of in the literature, for instance, where this has occurred and led to some unfortunate sort of behavior decisions.
Interesting. So maybe to switch gears for a little bit. You’re a trainee as a physician and you have a company on the go as well. I’m interested to hear, you know, what does your day to day look like? Must be a very busy.
David Burns (00:32.32)
Busy busy day. The company that I have, Halterix, As the chairman of the board of directors. And so I’m not really involved in the day to day operations of the company. I meet with different team members there multiple times a week. So really my main focus right now is actually clinical as a orthopedic surgery trainee. The day is already very busy even if you don’t have your own company.
So I typically wake up around five in the morning. I get to the hospital usually before six. We hand over the team that was on the night before where whatever patients had come in overnight needed surgery the next day. They’re all discussed amongst the incoming and outgoing group. We do teaching rounds every day between seven and eight, and then we’re in the OR doing your first operation starting at 8 a.m. and then that often goes till six or beyond.
And so anything to do with Halterix kind of has to have happen after that in the evening, I guess.
David Burns (00:33.37)
That’s right. Yeah, it has to happen either in the evenings or on weekends.
Well it’s very impressive that you’ve been able to bring in as far as you have with all those other treatments for sure.
David Burns (00:33.51)
Maybe like on a personal level, what’s been the most rewarding part of the journey for you so far?
David Burns (00:34.04)
It’s really been a great experience building Halterix. I think the first most rewarding part of it is having the opportunity to work with this team that I built, since I was the founder, I was the first founder of this. All the other founders I recruited into it and then over hires as well. And I made, I guess, a controversial choice of recruiting some of my existing friends as co-founders, which some people will recommend as a good idea and others not so great idea.
But these two co-founders who I recruited, Thomas Mak and Devon Stopps, we’ve been friends now for almost 20 years, and by building this company together, we’ve got spend more time with one another, which I’ve really enjoyed. And these two guys are so talented. They have so much knowledge that I’ve really learned a ton from them in the process and I think vice versa.
And then as we go on and build out our team together and sort of created from scratch this vision, it’s been really rewarding to do that.
Yeah, I agree. There’s nothing like working with a great team and then seeing that progress and seeing the results come to life and be able to celebrate those wins as a team as well.
David Burns (00:35.26)
So what does the team look like. You mentioned, you know your friends, you don’t have to say all the names and everything, but what are the different positions and roles within the team?
David Burns (00:35.40)
Sure. So and we’re still a very small group. We’re at our sort of pre-seed level of funding. And so we have a CEO right now. His name’s Russ. He’s a previous eBay executive who we recruited now a year and a half ago. He’s really talented in building teams and recruiting. He does a lot of our engagement with potential customers fundraising strategy.
So he’s involved the day to day operation. The company works full time. Thomas Mak is our chief of operations. He’s previously a sensor analyst in the oil and gas sector. He’s also an engineer, professional engineer, and he’s involved again, in building out some of our technology, as well as overall strategy. Working with Russ directly.
We have a new machine learning developer His name is Colin, who’s recently graduated from U of T, his graduate degree. He actually started off working primarily imaging space or imaging, but it turns out that translates really well to sensors. And now we’re working more in the imaging space as well, too. So I mentor him and we’ve worked together on bringing some of the technology ideas to fruition.
And then Devin is a serial entrepreneur who really helped us build the business initially. Understands the legal aspects of running a company, appropriate procedures and protocols and things like taxes that are not necessarily that interesting, but someone has to do and do well.
Yes, Yes, of course, of course. I’ve had the pleasure to work with a lot of those team members directly as well. And certainly it’s been great and I have a lot of respect for them. So what do you see next for Halterix? What’s in the short term or long term future for you.
David Burns (00:37.54)
Halterix made a pivot just under a year ago or maybe six months ago, where we decided that our sole focus would not be just on physical therapy and rehabilitation for a variety of reasons, we are still very committed to working in this space. But we found that as we were talking to potential customers, many of them, rather than interested, are being interested in buying our technology.
We’re actually interested in integrating our technology into the product that they already have, but they just don’t have the expertise to do it. And so, you know, through this process of building out our first set of applications and all the research we did, we realized we actually have something that we can share with many others, both in the physical therapy, rehabilitation space, but also in other spaces as well, like health and wellness or sports.
And so we’re now this business company where we help other companies use sensor data or video data for human motion tracking for various purposes. We can build up these systems from scratch or provide an API type service where it integrates with someone’s existing technology and applications.
Excellent. Well, certainly lots of opportunities there and exciting things to look forward to. So thank you. Dr. David Burns, for joining us today. If you like this episode, please subscribe to the MindSea newsletter and you’ll be notified about future episodes. Thanks a lot. Have a good day.