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People often take walking for granted. We just move, one step after another, without ever thinking about what it takes to make that happen. Yet every single step is an extraordinary act of coordination, driven by precise timing between spinal cord, brain, nerves, muscles, and joints. Historically, people have used stopwatches, cameras, or trained eyes to assess walking and its deficits. However, recent technological advances such as motion capture, wearable sensors, and data science methods can record and quantify characteristics of step-by-step movement. We are researchers who study biomechanics and human performance. We and other researchers are increasingly applying this data to improve human movement. These insights not only help athletes of all stripes push their performance boundaries, but they also support movement recovery for patients through personalized feedback. Ultimately, motion could become another vital sign. From motion data to performance insights Researchers around the world combine physiology, biomechanics, and data science to decode human movement. This interdisciplinary approach sets the stage for a new era where machine learning algorithms find patterns in human movement data collected by continuous monitoring, yielding insights that improve health. Its the same technology that powers your fitness tracker. For example, the inertial measurement unit in the Apple Watch records motion and derives metrics such as step count, stride length, and cadence. Wearable sensors, such as inertial measurement units, record thousands of data points every second. The raw data reveals very little about a persons movement. In fact, the data is so noisy and unstructured that its impossible to extract any meaningful insight. That is where signal processing comes into play. A signal is simply a sequence of measurements tracked over time. Imagine putting an inertial measurement unit on your ankle. The device constantly tracks the ankles movement by measuring signals such as acceleration and rotation. These signals provide an overview of the motion and indicate how the body behaves. However, they often contain unwanted background noise that can blur the real picture. With mathematical tools, researchers can filter out the noise and isolate the information that truly reflects how the body is performing. Its like taking a blurry photo and using editing tools to make the picture clear. The process of cleaning and manipulating the signals is known as signal processing. After processing the signals, researchers use machine learning techniques to transform them into interpretable metrics. Machine learning is a subfield of artificial intelligence that works by finding patterns and relationships in data. In the context of human movement, these tools can identify features of motion that correspond to key performance and health metrics. For example, our team at the Human Performance and Nutrition Research Institute at Oklahoma State University estimated fitness capacity without requiring exhaustive physical tests or special equipment. Fitness capacity is how efficiently the body can perform physical activity. By combining biomechanics, signal processing, and machine learning, we were able to estimate fitness capacity using data from just a few steps of our subjects walking. Beyond fitness, walking data offers even deeper insights. Walking speed is a powerful indicator of longevity, and by tracking it, we could learn about peoples long-term health and life expectancy. From performance to medicine The impact of these algorithms extends far beyond tracking performance, such as steps and miles walked. They can be applied to support rehabilitation and prevent injuries. Our team is developing a machine learning algorithm to detect when an athlete is at an elevated risk of injury just by analyzing their body movement and detecting subtle changes. Other scientists have used similar approaches to monitor motor control impairments following a stroke by continuously assessing how a patients walking patterns evolve, determining whether motor control is improving, or if the patient is compensating in any way that could lead to future injury. Similar tools can also be used to inform treatment plans based on each patients specific needs, moving us closer to true personalized medicine. In Parkinsons disease, these methods have been used to diagnose the condition, monitor its severity, and detect episodes of walking difficulties to prompt cues to the patients to resume walking. Others have used these techniques to design and control wearable assistive devices such as exoskeletons that improve mobility for people with physical disabilities by generating power at precisely timed intervals. In addition, researchers have evaluated movement strategies in military service members and found that those with poor biomechanics had a higher risk of injury. Others have used wrist-worn wearables to detect overuse injuries in service members. At their core, these innovations all have one goal: to restore and improve human movement. Motion as a vital sign We believe that the future of personalized medicine lies in dynamic monitoring. Every step, jump, or squat carries information about how the body functions, performs, and recovers. With advances in wearable technology, AI, and cloud computing, real-time movement monitoring and biofeedback are likely to become a routine part of everyday life. Imagine an athletes shoe that warns them before an injury occurs, clothing for the elderly that detects and prevents a fall before it occurs, or a smartwatch that detects early signs of stroke based on walking patterns. Combining biomechanics, signal processing, and data science turns motion into a vital sign, a real-time reflection of your health and well-being. Azarang Asadi is a data scientist at Oklahoma State University. Collin D. Bowersock is a principal scientist at the Human Performance and Neuromechanics Research Institute at Oklahoma State University.
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E-Commerce
You might not have noticed if youre the type to upgrade your smartphone frequently, but the main cameras that they use have been getting wider and wider in their field of view throughout the years. While phones are now indisputably the most popular cameras in the world, most manufacturers have settled on a type of lens that used to be considered quite exotic and challenging to use in the camera space. The main camera on the iPhone 17 Pro, for example, has the same field of view as a 24mm lens on a full-frame camera, which is the general photographic standard for measuring focal lengths. This is a perspective that few companies would have considered using on a point-and-shoot camera in the pastits compositionally awkward for a non-zooming lens. Nonetheless, it is clearly now a new standard of its own. {"blockType":"creator-network-promo","data":{"mediaUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/03\/multicore_logo.jpg","headline":"Multicore","description":"Multicore is about technology hardware and design. It's written from Tokyo by Sam Byford. To learn more visit multicore.blog","substackDomain":"https:\/\/www.multicore.blog","colorTheme":"salmon","redirectUrl":""}} Another way But what if theres another way? Recently, Ive been using the Z80 Ultra from Nubia, a relatively niche consumer brand owned by the Chinese telecoms giant ZTE. Nubias core philosophy around smartphone cameras is that weve gone way too far out with 24mm lensesinstead, theres a lot to be gained by bringing things back to 35mm. For much of photographic history, the 24mm-ish lenses were all so used to now were considered pretty wide. Fabled German camera maker Leica, for example, didnt start designing 24mm lenses until the 90s; its classic focal lengths throughout much of the 20th century were 50mm, 35mm, 28mm, and 21mm. Anything wider than 24mm was typically referred to as ultrawide, while 35mm was at the longest end of the wide-angle spectrum. And 35mm lenses on smartphones arent newin fact, most devices in the early days used that kind of lens. This held for every iPhone all the way through to the iPhone 5S in 2013, which came in slightly longer than 28mm-equivalent. By the time of the iPhone XS in 2018, the field of view had widened to around 26mm, and 2022s iPhone 14 Pro went wider still, to about 28mm. More stuff in view Why the shift? One obvious advantage to a wider lens is that you can simply fit more stuff in the shot. The 28mm focal length is easier to use than 35mm for shooting groups of people, for example. The field of view also tends to be easier to design physically shorter lenses for, which was critical as phones started to get thinner. And if you want a 35mm-equivalent field of view, you can always crop in from the wider focal length; Apple has been actively promoting this as a built-in feature in recent years with 1.2x (28mm) and 1.5x (35mm) options in the iPhone camera app. But you do lose the qualities of a native 35mm-equivalent lens when you do this. Cropping your image will always compromise on quality to some extent, and you dont get the same compressed perspective that comes from a longer focal length. 35mm is a natural perspective that offers more subject isolation with blurrier backgrounds than if you were using a 28mm-equivalent lens on the same sensor. Theres a reason Fujifilm opted for 35mm on its ultra-popular X100 line of enthusiast compact cameras. A worthy option While I wouldnt say the Nubia Z80 Ultra has the worlds greatest camera systemits image processing leaves a lot to be desired when compared to the likes of Oppo and Xiaomithe shooting experience is good enough to convince me that 35mm is a worthy route to pursue. Coupled with a genuinely useful two-stage shutter button, the 35mm lens on the Z80 Ultra just feels more like a real camera than most other phones. Of course, sometimes you will want a wider perspective. Nubias answer to that is simply to provide an 18mm-equivalent ultrawide camera thats capable enough for you to crop into 24mm and get passable results. Even the highest-end phones have been compromising on ultrawide camera hardware in recent years, but the Z80 Ultras ultrawide has a relatively huge 1/1.56 inch sensorthats as big as the main camera on many upper midrange phones. The 24mm results arent going to blow you away, but theyre more than serviceable. Refreshing choices Camera design is always about trade-offs, so its refreshing to see a phone that makes different choices; the 35mm main lens on the Z80 Ultra is just one of them. Nubia also opted for an almost-invisible under-display selfie camera, for example, which gives you a genuinely full-screen image when watching videoat the expense of, well, selfie quality. While the execution isnt fully there just yet, I really think Nubia is onto something with this 35mm design. Coupled with a strong 18mm ultrawide, a solid 70mm telephoto, and a real shutter button, the Z80 Ultra presents a photographer-forward system that feels meaningfully different to other phone cameras. When it comes to photography, whats not in the frame is just as important as what is. Smartphone cameras have come to dominate the world, so its worth considering the trade-offs when it comes to their wider perspective. {"blockType":"creator-network-promo","data":{"mediaUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/03\/multicore_logo.jpg","headline":"Multicore","description":"Multicore is about technology hardware and design. It's written from Tokyo by Sam Byford. To learn more visit multicore.blog","substackDomain":"https:\/\/www.multicore.blog","colorTheme":"salmon","redirectUrl":""}}
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E-Commerce
In 2010, Phil Gilbert was a longtime startup entrepreneur when IBM acquired the software company he ran. The slower, process-oriented culture was a struggle for someone who was used to the faster pace of startup life, he writes in his new book, Irrestible Change: A Blueprint for Earning Buy-In and Breakout Success. When IBM tapped him to lead a transformation of the company, it was a daunting task. Over the next few years, Gilbert guided IBMs shift toward design-thinking and re-trained thousands of employees to work differently, all without mandating a thing. Today, he sees corporate mandates as pointless: They dont work, he says. And yet, theyre ubiquitoustake the RTO mandates that companies are enforcing, often to the frustration of their employees. At Paramount, about 600 workers took a voluntary buyout rather than accept the companys 5-day RTO mandate. But change is inevitable, whether its about remote work or AI integration. So how do companies get employees on board? Gilbert, now a leading culture change expert, spoke with Fast Company about the lessons he learned from his undertaking at IBM and what company leaders should know about getting employee buy-in for their own change initiatives. Were in a time when companies are undergoing and implementing lots of changes, from RTO to AI to DEIall the acronyms. In your book, you talk about the importance of treating change like a product. What do you mean by that? My predisposition, based on years of experience, is that mandating changes in the workplace is hugely inefficient and hugely ineffective. Cultures drive outcomes. Mandating culture changes to achieve different outcomes doesn’t work. [At IBM], I had to start thinking, Okay, if those two things are true, how do I change a culture without mandating it? And it hit me that this is very much the same problem that any startup faces: bringing a new product to market. You have a new solution to a problem, and nobody knows who you are. It struck me that what I was really doing was constructing a new product for the marketplace that was IBM. I had to make this product so desirable that the teams would choose to adopt it. And in doing so, they would work better together and deliver better outcomes. That was an aha moment of, Oh, I’ve done this before. I know how to build products, I know how to deliver products. If you’re thinking about introducing this [change] as a product, you have to understand that a product is bigger than a technology. A product is much more holistic than just a single tool. We have to name it. We have to put the brand values into it. You have to prove value. RTO is something so many companies are struggling with. You talk about making change desirable, but what advice do you have for leaders when the change they want to implement is getting pushback? I’m telling leaders today, If you are getting pushback from people returning to the office, don’t think it’s on themit’s on you. If you introduce something that people reject after giving it a try, there’s one of two reasons: The first one is that it’s not actually a good idea. The second one, which is more common, is that it’s not a bad idea, but you have not executed it very well. I’m a big believer in people being at the office, but not for the reason most leaders are saying today. I’ve come across company after company where the CEO will say, Get back to the office because collaboration is better. And then when you get to the office, you find out that three-quarters of your team is not even in that location. Collaboration is actually happening very well over Zoom and Teams and Webex. Its all the other stuff that makes up a persons career and a persons wisdom, the collaborations that are not happening via Zoom, [that were missing]. Those are the experiences we should be majoring on in our physical spaces, and they should be apparently valuable. Thats what irresistible change is all about. Its about reversing the ownership of noncompliance. In the old model, noncompliance was a failure of the employee: They dont get it. Im going to start looking at the badge readers every day and find out who badged in and who badged out and when they did it. Thats the old model, and it engenders resentment from day one. The irresistible change model says, If folks arent coming back to the office and staying willingly, why is that? And what can I do to make that environment so valuable to them that they want to be there? What surprised you most during the transformation at IBM? I believed in this thing called the frozen middle. I thought middle management was resistant to changethat had been my experience. So when I designed the program, [I thought], Ive got to keep the very top engagedthat meant our CEO, her directs, and their directs. And I have to keep the workers at the edge very engaged. Theyre the canaries in the coal mine. My assumption was that we would get to the middle over time. A couple years into the program, [our] research showed that middle managers did not resist change. In fact, they were almost as rabid about change as the people at the edge, the earlier-career people. But middle managers do the hardest job in the business. They’re the translators. They’ve got to translate the high-level strategy and communications to the very senior people. And they’ve got to rationalize the chaos of what’s going on on the ground. This role of translation is very hard, and we had just made it exponentially harder because we introduced new teams under their purview that were operating in radically different ways from their old teams. We hadn’t given them the tools to manage teams that were using these new practices. Once we acknowledged that and gave them the tools, their ability to manage these teams was greatly expanded. That was a huge accelerant. Had we had that at the beginning, we would have shaved at least a year, if not two, off the program. If people could take one lesson from your book, what would you want that to be? The first question I ask every CEO when I’m approachedunfortunately, I’m not approached as often as I’d like to be before the transformation starts; I’m typically approached after it’s failedis, Tell me about the teams youve put through the program. And almost always, I hear something like this: Oh, our best people. We pulled them off their projects. A tiger team. Getting those first teams correct is a huge part of winning or losing. These are not cherry-picked employees. These are teams that are funded to do what theyre going to do, whether you transform them or not. These are not innovation teams in some cool office in San Francisco with bricks and exposed ductwork and VW buses sticking out of the wall. These are teams in your mainstream businesswhoever is on them. Virtually everybody gets that wrong.
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E-Commerce
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