From smart footwear to haptic gloves: how Human-Machine Augmentation technology is reshaping ground-up innovation Let’s imagine three everyday situations. A warehouse worker finishes a full shift of lifting without hurting their back. A stroke patient walks through a rehab clinic with a powered frame that moves with him. An engineer working on a car design grabs a virtual door handle and feels it push back. In all three cases, a device worn on the body helps a person do something that would be difficult to do alone. The person is still doing the work. The machine simply helps them do more. This is the idea behind human augmentation. It is different from traditional automation. For decades, automation meant taking work away from people. Assembly lines, factory robots, and conveyor belts did more of the work so people had to do less. Augmentation works in the opposite way. Wearable machines aren’t new. What’s new is that they can now sense and compute enough to follow what you’re doing and react in the moment. The capability picked up a name this year that made it from research papers to the CES stage: physical AI. The last wave of AI generated text, images and video. This one acts — reading a real environment, working out what’s happening, and doing something about it through motors and sensors, close to a person and without hurting them. The market numbers show how physical AI is becoming more common across different industries. Wearable robots and exoskeletons are worth about $6.8 billion in 2026 and could grow to more than $24 billion by 2031. Physical AI is smaller today, but it is growing faster: from less than $1 billion in 2025 to more than $15 billion by 2032. Two key changes made this possible: batteries became cheap enough to power devices for a full shift, and AI chips became small enough to be worn on the body. So, where can this be used now? In hospitals and rehab clinics, on factory and warehouse floors, and in the tools designers and trainees work with. Why it’s happening now: key forces driving human augmentation Here are four reasons companies are starting to put augmentation in their budgets: Ageing populations, and rising demand for rehab and mobility tech. By 2030, one in six people worldwide will be 60 or older. By 2050 that group doubles to 2.1 billion, and the over-80 population triples to 426 million, according to the WHO. Older adults already outnumbered children under five as of 2020, and in Japan over 30% of people are past 60. That drives up demand for assistive, rehabilitative and mobility devices: more strokes and neurological conditions to treat, more people who want to stay independent while there aren’t enough carers and therapists to do all of it by hand. Increasing focus on workforce safety and productivity. In industry, manufacturing and logistics, augmentation solves two problems at once. The first is injury: musculoskeletal disorders (strains, back damage, the wear from lifting and repetitive motion) cause over a million US workplace injuries a year, cost employers around $20 billion in workers’ compensation, and run from $13 billion to more than $50 billion once lost productivity is counted. About half the cases are back injuries, and a single serious one costs $15,000 to $85,000. Moreover, XR helps practicing a dangerous job before doing it for real, which cuts mistakes on the floor and speeds up training. The second is output: with labor shortage, companies want the people they have to get through a full shift without fading or getting hurt. A wearable that takes load off the spine does both: fewer claims and steadier performance. The new generation of immersive technologies. XR and haptic interfaces have moved past gaming demos into training, simulation and product design. Force-feedback gloves now reproduce weight, resistance and texture well enough that someone can handle a virtual part like a real one: rehearse a procedure, test a prototype, walk a trainee through a task with the physical constraints intact. It’s being funded like a serious category, too. AI small enough to run on the device itself. This is the quiet one under the other three. Sensors, sensor fusion and on-device processing let a wearable read what’s happening and adapt in milliseconds, without sending anything to the cloud, which is what makes it responsive, and increasingly connected to the systems around it. Ten years ago that kind of processing needed a server room. Let’s now take a closer look at where physical AI is currently being used. Healthcare: giving people their movement back In medicine, augmentation is about getting back something ordinary that illness or injury took away: standing up, walking across a room, holding a cup, tapping out a message. It usually starts in the rehab clinic. A powered exoskeleton holds a patient upright and moves their legs through the walking motion, so someone recovering from a stroke or a spinal injury can practise the real thing far sooner than they otherwise could. Ekso Bionics’ EksoNR is used this way for stroke, spinal cord injury and brain injury. The therapist dials how much help the device gives from a touchscreen, and it records every step and how the person is progressing. Ekso says its machines have supported hundreds of millions of assisted steps. The evidence is promising: in one review of exoskeleton training after spinal cord injury, 76% of patients could walk with no physical help by the end of a program, and no serious adverse events turned up. Virtual reality solves another problem in the same clinics: boredom. Getting arm and hand movement back after a stroke takes hundreds of repetitions, and the standard exercises are dull enough that people stop pushing, which slows them down. VR turns this into a game, reaching, grabbing and sorting things in a scene that responds, so patients do more of them and stay interested. The headset also tracks every rep, so the difficulty can shift to match how the patient is…

Next-generation Extended Reality tech is raising the standard for industrial 3D visualization. Complex equipment becomes much easier to show customers and walk them through, without hauling a physical unit to every meeting. For industrial machinery manufacturers, that’s real business value: by reducing their dependency on physical equipment, companies can improve sales demonstrations and make customer education more visual, scalable, and effective. The value is clear at trade fairs. Industrial machinery manufacturers can usually show only one machine in one configuration. On top of that, the most important parts are often hidden inside, and the product is displayed without the environment it was designed for. So the exhibitor arrives at the show unable to demonstrate the product they’re selling. This is where a new generation of Virtual Showrooms comes in. We’ll look at how it solves that problem below. The problems manufacturers face at trade shows But first, let’s look at the main challenges manufacturers face when relying on traditional trade show methods. The product does not fit. What a manufacturer sells is often far too big for any booth. A single production line can fill half a plant, and one machine can weigh five tons. But the trade show offers thirty square meters of rented floor at a high price. Exhibit space is the single largest line in the entire exhibiting budget: for U.S. B2B shows it accounts for 40.5% of an exhibitor’s total spend, more than any other category. At Hannover Messe (Germany), a modest 18 to 36 square meter stand costs €18,000 to €45,000, and a large one, 72 to 200 square meters, can pass €250,000. The equipment was never built to be lifted onto a stand, so a company pays the highest price in its budget for the one thing that still cannot hold the product it came to sell. Shipping machinery is expensive. The heavier the machinery, the higher the sales costs. Transportation, on-site assembling, and travel expenses can run into six figures per event. On average, construction, logistics and assembly add up to around €100,000 to get a single product onto the floor, and the largest, most impressive machines, the ones a company most wants buyers to see, are precisely the ones that push that figure highest. Buyers see a sample, not the range. Manufacturers have to choose which models to bring, and buyers never see the full portfolio. They are also limited by demo capabilities: system flow, scale, and engineering remain hidden. The value is hidden under the housing. Much of what a manufacturer charges for happens inside the machine, out of sight. Visitors stand in front of a closed housing and see only the outside of a product whose real engineering is sealed away. There is no context. Buyers need to understand how it fits into their own production line, works with existing equipment, and supports their process. That context cannot be brought to the booth, so much of the product’s value has to be imagined. The product may not exist yet. Sometimes the product a company needs to sell is still on the roadmap, or only half-built in a workshop. Industrial sales cycles are long, and buyers often need to commit before a machine physically exists. You are competing for the same buyer as everyone else. A trade show gathers the entire industry into one hall, which means every rival is only a few steps away. At IMTS 2024, visitors had to work through 1,737 exhibitors spread across more than 1.2 million square feet. Hannover Messe 2025 was larger, with 3,694 exhibitors from 62 countries and more than 123,000 visitors. In a room like that, you have to stand out to get noticed. How 3D solves each problem So, how can 3D visualization help? Problem: The product is too large to ship, or it does not exist yet. With XR, prospects can walk around a full-scale model of a product, explore different options for an engineered-to-order build, and see exactly what they are buying months before the first unit is produced. This can shorten the sales cycle. The format changes, but the product remains in front of the customer. Problem: Shipping and floor space eat the largest share of the budget. Extended Reality replaces the heaviest line items in the industrial sales budget with a single portable asset. Each of the costs for equipment transport, exhibition stand rental, assembly crew, technical team travel recurs with every event. Over a year, they compound into one of the largest budget lines in industrial sales. A 3D model is created once and can be presented on a screen, in AR, or in VR using equipment that occupies only a small part of the stand. It can be updated when the product line changes and redeployed without additional logistics. Problem: A physical demo can show only one configuration. Let each visitor build their own version. For configurable machines built to order, each customer can preview their exact configuration in detail rather than a generic prototype. A configurator on a touchscreen, or the same system in AR or VR, lets them choose components and options, see the result update instantly, and leave with a build that matches their plant. One asset covers all two hundred configurations in the catalog. Problem: The most important parts of the product are hidden inside. Exploded views, cutaways and animations show what happens beneath the surface. On a screen, a visitor grasps how the system works within seconds; in AR or VR, they can pull it apart and study the internals in detail.  Problem: Buyers can’t easily picture how the product fits their own plant. VR can put a buyer inside a virtual production line. AR can drop the equipment at full scale into the room right in front of them, so they can walk around it. This way, they can see how it fits their facility instead of guessing. Problem: You have a few minutes to catch attention, and competitors are right next to you. Big screens with motion are…

Today’s imaging systems are more powerful than ever. A single CT scan generates hundreds of cross-sections. An MRI cardiac study captures the heart in four dimensions. A full-body PET produces a dense volumetric map of metabolic activity across every organ system. And yet, in most hospitals today, clinicians consume all of that data the same way they did in the 1990s: as 2D slices, scrolled one frame at a time, with the third dimension reconstructed entirely in the radiologist’s head. That gap between the data that exists and the data that gets used is what 3D medical visualization is closing. Progress hasn’t been uniform. The specialties with the highest spatial stakes have moved fastest. In oncology, where tumour margins and vascular relationships determine whether a resection is safe, 3D visualization is now routine. In cardiology, where structural defects live in three dimensions that 2D echo can only approximate, volumetric review has become standard practice for complex case planning. For these teams, rotating a segmented model or flying through a volume-rendered vessel is part of the reading workflow. Within healthcare, oncology drives roughly 34% of total 3D imaging spend: 52% of cancer centers already use 3D imaging as part of their standard workflow, and 44% of cardiology departments do the same. For much of medicine, the shift is still underway. But the direction is clear. The market reflects it. The global 3D medical imaging market was valued at $21.43B in 2025 and $23.39B in 2026 and is projected to reach $42.75B by 2032  at a compound annual growth rate of 10.36%. Healthcare has become the largest adopter of 3D imaging technology overall. In this article, we break down what 3D medical visualization actually means technically and where it creates measurable clinical value. The imaging data problem Begin with the scanners, because they don’t produce data the same way: CT measures X-ray absorption, so dense tissue like bone reads strongly while soft tissue stays faint: the default for trauma, lung, and skeletal work. MRI reads tissue magnetic properties instead of density, trading speed and bone detail for soft-tissue contrast nothing else matches. PET maps metabolic activity rather than structure, and almost always travels fused to a CT or MRI so the active regions have anatomy to sit against. Ultrasound produces a live volume but depends heavily on probe angle and operator skill. Cone-beam CT gives a tight, high-resolution field at the cost of coverage, which is why it dominates dental and interventional suites. All of these imaging methods capture a 3D volume of the body. Yet in most cases, doctors still review that data as a series of 2D slices. At first glance, this seems surprising: why collect rich 3D data only to view it in 2D? Part of the answer is habit and established workflows, but there are also practical reasons why 2D slices remain the standard in medical imaging. Raw data, nothing interpreted. A slice shows the scan as acquired. Every 3D rendering is the product of decisions which densities to display, which to hide, where to set the threshold and any of those can suppress a real finding or manufacture one that isn’t there. Full coverage of the dataset. Scrolling slices walks the eye across every voxel in the study. A 3D view by definition hides whatever sits behind the surface it shows, and for catching a small lesion or a faint ground-glass opacity, seeing everything matters. 3D earns its place once the task moves past detection: Spatial relationships. 3D visualization makes it easier to understand how anatomical structures relate to one another. Instead of mentally reconstructing anatomy from dozens of 2D slices, clinicians can view organs, vessels, and abnormalities as a single 3D model. Change over time. Tracking changes across multiple scans becomes much easier in 3D. By measuring the volume of a structure over time, clinicians can quickly identify trends that may be difficult to spot in individual slices. Communication. A 3D model is something a patient, a referring physician, or a multidisciplinary team can read at a glance, where a slice stack means little to anyone outside radiology. So, 3D visualization is most valuable when understanding spatial relationships is difficult or time-consuming in 2D. What complicates this in practice is the format the data arrives in. Most medical imaging is still stored as DICOM, a standard built around 2D-image workflows. DICOM is the backbone of medical imaging, but several of its legacy choices make 3D visualization and analysis harder to build on top of it. Gathering everything a full analysis needs is one problem: a careful read of a pathology usually draws on prior scans and the patient’s imaging history, and that data sits scattered across separate studies and series rather than in one place. Interoperability is another. DICOM has to exchange data with the hospital’s other systems, such as PACS, RIS, and the electronic health record, and every connection point adds friction. The input itself is uneven too: scans vary in quality and completeness depending on how and where they were acquired, so a tool built for real cases has to hold up across that range. We’ve written separately about why DICOM is stuck in the ’90s. What “3D medical visualization” actually means There are five techniques in common use. Most clinical software uses two or three of them together. Segmentation comes first, because the others depend on it. Segmentation. Something has to label what is in the scan before the rest can work. It needs to know which voxels are liver, which are tumour, which are vessel wall. This used to be manual work. A radiologist drew outlines on each slice, which for a complex case could take close to an hour. Two radiologists rarely produced identical outlines. AI tools changed this. TotalSegmentator and similar models label most organs in a CT scan in under a minute. The clinician checks and corrects the result instead of drawing it. This is what makes the other four techniques practical for routine use. Multiplanar reformatting (MPR)….