Tech Talks Daily
00:00:04 · Neil C. Hughes
[00:00:04] S0: What has to happen after an AI system detects a fall for the alert to be classed as care? The answer involves people, connectivity, privacy and an understanding of exactly what happened before somebody even reached the floor. Well, today, I'm joined by Mike Link, chief operating officer at KamiVision, to discuss computer vision in residential care and the work involved in adapting it for our own homes. We're going to talk about how the company combines device detection, cloud filtering and human review along with the questions buyers should ask about accuracy claims in any system. And he will explain why room layout, lighting and everyday behavior can change what a system needs to recognize. For care providers and business leaders assessing physical AI, today's conversation is about the process surrounding the technology. An alert on the screen is a starting point, but the value depends on whether the people receiving it can understand the event and respond appropriately in practice. But enough for me. Let me introduce you to my guest. Now we're gonna talk about technology, its role in residential care homes, and how it can improve looking after the elderly. So thank you for joining me on the podcast today. Can you tell everyone listening a little about who you are and what you do?
00:01:28 · Mike Link, Neil C. Hughes
[00:01:28] S1: Yeah. My name is Mike Link. I'm the COO of KamiVision, and we operate a AI vision business, which means AI and cameras. We started probably ten years ago with home security using AI to do, you know, people detection, dog detection, package detection, and that thing grew pretty quickly and then allowed us to build other things. One of which what we'll talk about today is the elder care business where we can detect falls and let families know, staff know when somebody's on the ground in a room, which is pretty cool. And then the other thing which we're building is enterprise security. So, you know, in retail stores, in commercial real estate, using existing cameras to layer AI over to do either security or intelligence, maybe in, like, a grocery store, seeing where people spend the most time, or maybe in a a warehouse to do detections of breaking and entering or maybe inventory management or possibly even, like, warehouse shelving deteriorating and breaking. So, yeah, we we we kinda do it all. [00:02:38] S0: Incredibly cool. And I've gotta ask because I always love diving into the origin story of my guests. You talk about home security, package detection, and retail store security. What was the fur... What was it that first showed you that Eldercare needed a different approach to monitoring safety and and also supporting independence? There's gotta be a story there. Was there a moment that inspired that?
00:03:02 · Mike Link
[00:03:02] S1: Not necessarily. So I never intended to get into this. I actually studied neuroscience and wanted to become a neurologist or, like, a surgeon or something, and ended up in India for a while studying alternative medicine called Ayurveda. And when I came back to The States, I'd always done sales and I'd always had businesses. And I needed some money, so I ended up consulting a different company that was doing AI in the elder care space. And that's how I first learned about the challenge with elder care and that, you know, people fall and nobody finds out about it till, like, three, four hours later often even in a facility. And it was an interesting problem. And then, you know, we had some success there, and then Kami had hit me up, and we're doing something similar. And I thought that it was pretty cool that they were able to do similar technology or build similar technology, but for a fraction of the the price and and modify the technology very quickly because we didn't have any VC or any, you know, outside investor to answer to because the home security business had done so well. We had... I wouldn't say, like, infinite money, but, you know, basically millions of dollars that we can invest and then not have to answer to a VC, which is pretty unique. A lot of the companies in the space, you know, the way that technology typically gets built... And and now it's a little different with this whole AI credits thing, but, you know, a VC or an outside investor will come in, give some money unless the founder has some cash. So in this case, the founder had some cash and was able to build the technology super quickly. And then because nobody else was, you know, knocking on the door for the check, we could have a much lower price than some of our competitors. So the challenge was that, you know, the technology in elder care is very expensive because of the reasons I I just said. And the other challenge was that it was hard to install and actually took a lot of capital from a lot of the communities and facilities upfront to actually install the technology. The other thing was that in home, there was nothing that existed for consumer. So, you know, even though there was a lot of these companies selling into communities and facilities where, like, mom might go to a nursing home, there wasn't any technology for somebody at home.
00:05:27 · Mike Link, Neil C. Hughes
I think the big shift was people wanting to not go into a facility because it's not the nicest thing when you have to leave home and, you know, move all your things, and then you're basically in, like, an adult day care. Mhmm. [00:05:41] S0: Yeah. And and anybody that's got an elderly parent, grandparent, or family member, they will know about fall detection systems, things that could be worn on the wrist or or around the neck. But for... From what your experience here, where do those current traditional fall detection systems, where do they tend to fail in homes and residential care settings?
00:05:41 · Neil C. Hughes, Mike Link
[00:05:41] S0: Yeah. And and anybody that's got an elderly parent, grandparent, or family member, they will know about fall detection systems, things that could be worn on the wrist or or around the neck. But for... From what your experience here, where do those current traditional fall detection systems, where do they tend to fail in homes and residential care settings? [00:06:02] S1: Well, those are definitely good options, but the problem is is if somebody doesn't remember to use them or if they need to be charged, then they're kind of useless. And the idea is that, you know, somebody who's older doesn't have to remember to press something or ask for help in an emergency because, you know, I don't know if you have a mom or dad or had grandparents who maybe needed extra help. But Yeah. I know for sure my grandmother would not remember. You know? In an emergency situation, she's not gonna think, oh, let me press my button for help. You know? She's gonna freak out and be panicking and then be stuck. So, yeah, there's nothing wrong with them. I think it's a good... They were a good start, but I think now the technology has evolved to a point where it's requiring less human assistance from the person in need. And that's the idea is that, you know, the person who needs extra care doesn't need to say anything, doesn't need to do any... They could just exist, which prevents a lot of, like, user error. And then we can see based on how they live, what kind of help they need.
00:06:02 · Mike Link, Neil C. Hughes
[00:06:02] S1: Well, those are definitely good options, but the problem is is if somebody doesn't remember to use them or if they need to be charged, then they're kind of useless. And the idea is that, you know, somebody who's older doesn't have to remember to press something or ask for help in an emergency because, you know, I don't know if you have a mom or dad or had grandparents who maybe needed extra help. But Yeah. I know for sure my grandmother would not remember. You know? In an emergency situation, she's not gonna think, oh, let me press my button for help. You know? She's gonna freak out and be panicking and then be stuck. So, yeah, there's nothing wrong with them. I think it's a good... They were a good start, but I think now the technology has evolved to a point where it's requiring less human assistance from the person in need. And that's the idea is that, you know, the person who needs extra care doesn't need to say anything, doesn't need to do any... They could just exist, which prevents a lot of, like, user error. And then we can see based on how they live, what kind of help they need. [00:07:03] S0: And, of course, KamiVision cites 99.5% accuracy, which is incredibly impressive. But how is that measured, and what should buyers ask before trusting any accuracy claim in a a real care environment?