2026-03-21 02:49:12
Wheelchair users with severe disabilities can often navigate tight spaces better than most robotic systems can. A wave of new smart-wheelchair research, including findings presented in Anaheim, Calif., earlier this month, is now testing whether AI-powered systems can, or should, fully close this gap.
Christian Mandel—senior researcher at the German Research Center for Artificial Intelligence (DFKI) in Bremen, Germany—co-led a research team together with his colleague Serge Autexier that developed prototype sensor-equipped electric wheelchairs designed to navigate a roomful of potential obstacles. The researchers also tested a new safety system that integrated sensor data from the wheelchair and from sensors in the room, including from drone-based color and depth cameras.
Mandel says the team’s smart wheelchairs were both semiautonomous and autonomous.
“Semiautonomous is the shared control system where the person sitting in the wheelchair uses the joystick to drive,” Mandel says. “Fully autonomous is controlled by natural-language input. You say, ‘Please drive me to the coffee machine.’ ”
This is a close-up of the wheelchair’s joystick and camera.DFKI
The researchers conducted experiments (part of a larger project called the Reliable and Explainable Swarm Intelligence for People With Reduced Mobility, or REXASI-PRO) using two identical smart wheelchairs that each contained two lidars, a 3D camera, odometers, user interfaces, and an embedded computer.
In contrast to semiautonomous mode, where the participant controls the wheelchair with a joystick, in autonomous mode, control involves the open-source ROS2 Nav2 navigation system using natural-language input. The wheelchairs also used simultaneous localization and mapping (SLAM) maps and local obstacle-avoidance motion controllers.
One scenario that Mandel and his team tested involved the user pressing a key on the wheelchair’s human-machine interface, speaking a command, then confirming or rejecting the instruction via that same interface. Once the user confirmed the command, the mobility device guided the user along a path to the destination, while sensors attempted to detect obstacles in the way and adjust the mobility device accordingly to avoid them.
According to Pooja Viswanathan, CEO & founder of the Toronto-based Braze Mobility, research in the field of mobile assistive technology should also prioritize keeping these devices readily available to everyday consumers.
“Cost remains a major barrier,” she says. “Funding systems are often not designed to support advanced add-on intelligence unless there is very clear evidence of value and safety. Reliability is another barrier. A smart wheelchair has to work not just in ideal conditions, but in the messy, variable conditions of daily life. And there is also the human factors dimension. Users have different cognitive, motor, sensory, and environmental needs, so one solution rarely fits all.”
For its part, Braze makes blind-spot sensors for electric wheelchairs. The sensors detect obstacles in areas that can be difficult for a user to see. The sensors can also be added to any wheelchair to transform it into a smart wheelchair by providing multimodal alerts to the user. This approach attempts to support users rather than replace them.
According to Louise Devinge, a biomedical research engineer from IRISA (Research Institute of Computer Science and Random Systems) in Rennes, France, the increased complexity of smart wheelchairs demands more sensing. And that requires careful management of communication and synchronization within the wheelchair’s system. “The more sensing, computation, and autonomy you add,” she says, “the harder it becomes to ensure robust performance across the full range of real-world environments that wheelchair users encounter.”
In the near term, in other words, the field’s biggest challenge is not about replacing the wheelchair user with AI smarts but rather about designing better partnerships between the user and the technology.
This image shows data representations used by the 3D Driving Assistant. These include immutable sensor percepts such as laser scans and point clouds, as well as derived representations like the virtual laser scans and grid maps. Finally, the robot shape collection describes the wheelchair’s physical borders at different heights.DFKI
Mandel says he expects to see smart wheelchairs ready for the mainstream marketplace within 10 years.
Viswanathan says the REXASI-PRO system, while out of reach of present-day smart wheelchair technologies, is important for the longer term. “It reflects the more ambitious end of the smart wheelchair spectrum,” she says. “Its strengths appear to lie in intelligent navigation, advanced sensing, and the broader effort to build a wheelchair that can interpret and respond to complex environments in a more autonomous way. From a research standpoint, that is exactly the kind of work that pushes the field forward. It also appears to take seriously the importance of trustworthy and explainable AI, which is essential in any mobility technology where safety, reliability, and user confidence are paramount.”
Mandel says he’s ultimately in pursuit of the inspiration that got him into this field years ago. As a young researcher, he says, he helped develop a smart wheelchair system controllable with a head joystick.
However, Mandel says he realized after many trials that the smart wheelchair system he was working on had a long way to go because, as he says, “at that point in time, I realized that even persons that had severe handicaps [traveling through] a narrow passage, they did very, very well.
“And then I realized, okay, there is this need for this technology, but never underestimate what [wheelchair users] can do without it.”
The DFKI researchers presented their work earlier this month at the CSUN Assistive Technology Conference in Anaheim, Calif.
This article was supported by the IEEE Foundation and a Jon C. Taenzer fellowship grant.
2026-03-21 02:00:04
The rapid ascent of artificial intelligence and semiconductor manufacturing has created a paradox: Industries are booming yet they face a critical shortage of skilled workers. Demand for data center technicians, fabrication facility workers, and similar positions is growing. There aren’t enough candidates with the right skill sets to fill the in-demand jobs.
Although those technical roles are essential, they don’t always require a four-year degree—which has paved the way for skills-based microcredentials. By partnering with higher education institutions and training providers, industry leaders are helping to design targeted skills programs that quickly turn learners into job-ready technical professionals.
Because microcredentials are relatively new, consistency is key. Through its credentialing program, IEEE serves as a bridge between academia and industry. Developed and managed by IEEE Educational Activities, the program offers standardized credentials in collaboration with training organizations and universities seeking to provide skills-based qualifications outside formal degree programs. IEEE, as the world’s largest technical professional organization, has more than 30 years of experience offering industry-relevant credentials and expertise in global standardization.
IEEE is setting the benchmark for skills-based microcredentials by establishing a framework that includes assessment methods, qualifications for instructors and assessors, and criteria for skill levels.
A recent collaboration with the University of Southern California, in Los Angeles, for example, developed microcredentials for USC’s semiconductor cleanroom program. USC heads the CA Dreams microelectronics innovation hub.
“The IEEE framework allows us to rapidly prototype training programs and adapt on the fly in a way that building new university courses—much less degree programs—won’t allow.” —Adam Stieg
IEEE worked with USC to create standardized skills assessments and associated microcredentials so that industry hiring managers can recognize the newly developed skills. The microcredentials help people with or without four-year degrees join the semiconductor industry as cleanroom technicians or as engineers with cleanroom experience.
IEEE also has partnered with the California NanoSystems Institute at the University of California, Los Angeles, to create skills-based microcredentials for its cleanroom protocol and safety program.
Based on IEEE’s work designing microcredentials with USC, UCLA, and other leading academic institutions, three best practices have emerged.
Collaborate with industry prior to starting the design process. There isn’t a one-size-fits-all approach. Workforce needs vary based on industry sector, company size, and geography. Higher education institutions and training providers build relationships with companies and industry groups to create effective microcredential programs and methods of assessment.
Traditional academic cycles can be slow, but technology moves fast. A flexible skills-based microcredentials framework allows programs to create or pivot as new breakthroughs occur.
“Setting up a credit-bearing course is not easy. And in a rapidly changing environment, you need to pivot quickly,” says Adam Stieg, research scientist and associate director at UCLA’s CNSI. “IEEE skills-based microcredentials are a flexible way to keep up our curriculum aligned with an evolving technology landscape.”
Stieg’s team worked with IEEE to build a framework to create microcredentials for its cleanroom protocol and safety program, ensuring it kept pace with the industry’s evolution.
“The IEEE framework allows us to rapidly prototype training programs and adapt on the fly,” he says, “in a way that building new university courses—much less degree programs—won’t allow.”
Many of the technical roles companies are looking to fill in emerging fields such as AI, cybersecurity, and semiconductors are still being developed or are quickly evolving. The rapidly changing landscape requires continual communications and feedback among higher education, training providers, and industry.
“We struggle to have feedback loops through the education system to the industry and back again,” says Matt Francis, president and CEO of Ozark Integrated Circuits, in Fayetteville, Ark. Francis, who has served as IEEE Region 5 director, is an IEEE volunteer who supports workforce development for the semiconductor industry.
Creating consistent feedback loops is critical for generating consensus on the skills sets needed for microcredential programs, experts say, and it allows providers to update assessments as new tools and safety protocols enter the workplace.
“If we start thinking about having training frameworks used within companies that are essentially on some sort of standard and align with a microcredential, we can start to build consensus,” Francis says.
Through its credentialing program, IEEE is helping higher education and industry work together to bridge the technical workforce skills gap. Contact its team to learn how IEEE skills-based microcredentials can help you fill your workforce pipeline.
2026-03-19 22:45:05
A growing number of Nigerian companies are turning to kit-based assembly to bring electric vehicles to market in Africa. Lagos-based Saglev Micromobility Nigeria recently partnered with Dongfeng Motor Corporation, in Wuhan, China, to assemble 18-seat electric passenger vans from imported kits.
Kit-based assembly allows Nigerian firms to reduce costs, create jobs, and develop local technical expertise—key steps toward expanding EV access. Fully assembled and imported EVs face high tariffs that put them out of reach for many African consumers, whereas kit-based approaches make electric mobility more affordable today. Saglev’s initiative reflects a broader trend: CIG Motors, NEV Electric, and regional players in Cote D’Ivoire, Ghana, and Kenya are also leveraging imported kits to build local EV ecosystems, signaling that parts of West Africa are intent on catching up with global electrification efforts.
CIG Motors operates a kit-assembly plant in Lagos producing vehicles from Chinese automakers GAC Motor and Wuling Motors. These vehicles include the Wuling Bingo, a compact five-door electric hatchback, and the Hongguang Mini EV Macaron, a microcar with roughly 200 kilometers of range aimed at ride-share operators looking for ultralow-cost urban transport. NEV Electric focuses on electric buses and three-wheelers for urban transit and last-mile delivery.
Saglev’s CEO, Olu Faleye, emphasizes that Nigeria’s EV transition addresses both practical economic needs in addition to environmental goals. Beyond passenger transport, electric vehicles could help reduce one of Nigeria’s persistent agricultural challenges: post-harvest spoilage. Nigeria loses an estimated 30–40 million tonnes of food annually because of weak logistics and limited refrigeration infrastructure, according to the Organization for Technology Advancement of Cold Chain in West Africa.
Electric vans, mini-trucks, and three-wheel cargo vehicles could help close this gap because their batteries can power refrigeration systems during transport without relying on costly diesel fuel. As EV adoption grows and charging infrastructure expands, temperature-controlled transport could become more affordable, reducing spoilage, improving farmer incomes, and helping stabilize food supplies, the organization says.
“I don’t believe that the promised land is making a fully built EV on the ground here.”
–Olu Faleye, Saglev CEO
Beyond Nigeria, Mombasa, Kenya–based Associated Vehicle Assemblers has begun assembling electric taxis and minibuses from imported kits, and Ghana’s government is spurring kit-car assembly there under its national Automotive Development Plan. In Ghana, assemblers benefit from import-duty exemptions on kits and equipment, corporate tax breaks, and access to industrial infrastructure. Saglev is already availing itself of those benefits, at its kit-assembly plant in Accra. The company says it also plans to expand its assembly operations to Cote D’Ivoire.
Despite these signs that West Africa’s EV ecosystem is gaining traction, limited grid reliability and sparse public charging infrastructure remain major barriers to widespread EV adoption. Urban households in Nigeria experience roughly six or seven blackouts per week, each lasting about 12 hours, according to Nigeria’s National Bureau of Statistics. That’s more downtime each day than the average U.S. household experiences in a year. More than 40 percent of households rely on generators, which supply about 44 percent of residential electricity, according to research by Stears and Sterling Bank.
Many early EV adopters therefore charge vehicles using gasoline or diesel generators. Faleye notes that Nigerians have long relied on such workarounds and expects fossil fuels to remain part of the EV charging equation for the foreseeable future—at least until falling costs for solar panels and battery storage make cleaner charging viable.
He acknowledges that charging EVs using hydrocarbons is fraught from an environmental perspective, but he points out that the practice at least brings other benefits of EVs, including lower maintenance costs and the EVs’ synergies with refrigeration and transportation logistics. And he points to a 2020 peer-reviewed study in the journal Environmental and Climate Technologies that compared the overall efficiency of internal combustion vehicles and electric vehicles across the full well-to-wheel energy chain. The study’s conclusion: Even after accounting for conversion losses, generating electricity with a diesel or gasoline generator to power an electric vehicle can remain just as efficient overall as burning the same fuel directly in a vehicle’s internal combustion engine.
The approach taken by Saglev and other Nigerian kit-car builders shows how local assembly can advance EV adoption even where infrastructure remains unreliable. By starting with kits, companies can deploy practical electric mobility solutions now while building the supply chains and technical expertise needed for more resource-intensive localized production.
Still, when asked whether Saglev plans to eventually move beyond kit assembly to independent design and manufacturing of EVs, Faleye calls such a move impractical.
“I don’t believe that the promised land is making a fully built EV on the ground here,” he says. “For me to do efficient vehicle manufacturing, I’d need a lot of robotics and 3D printing. That expense is unnecessary—it would just increase costs and make EVs more expensive.”
In a country where electricity can disappear for days, Nigeria’s kit-based EV strategy highlights a practical truth: incremental progress and ingenuity may matter more than perfect infrastructure. For Saglev, every kit-based vehicle rolling off the line is not just a van or bus—it’s a step toward an EV ecosystem that works for Nigeria’s realities today.
2026-03-19 20:00:05
One morning in May 2019, a cardiac surgeon stepped into the operating room at Boston Children’s Hospital more prepared than ever before to perform a high-risk procedure to rebuild a child’s heart. The surgeon was experienced, but he had an additional advantage: He had already performed the procedure on this child dozens of times—virtually. He knew exactly what to do before the first cut was made. Even more important, he knew which strategies would provide the best possible outcome for the child whose life was in his hands.
How was this possible? Over the prior weeks, the hospital’s surgical and cardio-engineering teams had come together to build a fully functioning model of the child’s heart and surrounding vascular system from MRI and CT scans. They began by carefully converting the medical imaging into a 3D model, then used physics to bring the 3D heart to life, creating a dynamic digital replica of the patient’s physiology. The mock-up reproduced this particular heart’s unique behavior, including details of blood flow, pressure differentials, and muscle-tissue stresses.
This type of model, known as a virtual twin, can do more than identify medical problems—it can provide detailed diagnostic insights. In Boston, the team used the model to predict how the child’s heart would respond to any cut or stitch, allowing the surgeon to test many strategies to find the best one for this patient’s exact anatomy.
That day, the stakes were high. With the patient’s unique condition—a heart defect in which large holes between the atria and ventricles were causing blood to flow between all four chambers—there was no manual or textbook to fully guide the doctors. The condition strains the lungs, so the doctors planned an open-heart surgery to reroute deoxygenated blood from the lower body directly to the lungs, bypassing the heart. Typically with this kind of surgery, decisions would be made on the fly, under demanding conditions, and with high uncertainty. But in this case, the plan had been tested in advance, and the entire team had rehearsed it before the first incision. The surgery was a complete success.
Such procedures have become routine at the Boston hospital. Since that first patient, nearly 2,000 procedures have been guided by virtual-twin modeling. This is the power of the technology behind the Living Heart Project, which I launched in 2014, five years before that first procedure. The project started as an exploratory initiative to see if modeling the human heart was possible. Now with more than 150 member organizations across 28 countries, the project includes dozens of multidisciplinary teams that regularly use multiscale virtual twins of the heart and other vital organs.
This technology is reshaping how we understand and treat the human body. To reach this transformative moment, we had to solve a fundamental challenge: building a digital heart accurate enough—and trustworthy enough—to guide real clinical decisions.
Now entering its second decade, the Living Heart Project was born in part from a personal conviction. For many years, I had watched helplessly as my daughter Jesse faced endless diagnostic uncertainty due to a rare congenital heart condition in which the position of the ventricles is reversed, threatening her life as she grew. As an engineer, I understood that the heart was an array of pumping chambers, controlled by an electrical signal and its blood flow carefully regulated by valves. Yet I struggled to grasp the unique structure and behavior of my daughter’s heart well enough to contribute meaningfully to her care. Her specialists knew the bleak forecast children like her faced if left untreated, but because every heart with her condition is anatomically unique, they had little more than their best guesses to guide their decisions about what to do and when to do it. With each specialist, a new guess.
Then my engineering curiosity sparked a question that has guided my career ever since: Why can’t we simulate the human body the way we simulate a car or a plane?
At a visualization center in Boston, VR imagery helps the mother of a young girl with a complex heart defect understand the inner workings of her child’s heart. Dassault Systèmes
I had spent my career developing powerful computational tools to help engineers build digital models of complex mechanical systems, using models that ranged from the interactions of individual atoms to the components of entire vehicles. What most of these models had in common was the use of physics to predict behavior and optimize performance. But in medicine today, those same physics-based approaches rarely inform decision-making. In most clinical settings, treatment decisions still hinge on judgments drawn from static 2D images, statistical guidelines, and retrospective studies.
This was not always the case. Historically, physics was central to medicine. The word “physician” itself traces back to the Latin physica, which translates to “natural science.” Early doctors were, in a sense, applied physicists. They understood the heart as a pump, the lungs as bellows, and the body as a dynamic system. To be a physician meant you were a master of physics as it applied to the human body.
As medicine matured, biology and chemistry grew to dominate the field, and the knowledge of physics got left behind. But for patients like my daughter, that child in Boston, and millions like them, outcomes are governed by mechanics. No pill or ointment—no chemistry-based solution—would help, only physics. While I did not realize it at the time, virtual twins can reunite modern physicians with their roots, using engineering principles, simulation science, and artificial intelligence.
The LHP concept was simple: Could we combine what hundreds of experts across many specialties knew about the human heart to build a digital twin accurate enough to be trusted, flexible enough to personalize, and predictive enough to guide clinical care?
We invited researchers, clinicians, device and drug companies, and government regulators to share their data, tools, and knowledge toward a common goal that would lift the entire field of medicine. The Living Heart Project launched with a dozen or so institutions on board. Within a year, we had created the first fully functional virtual twin of the human heart.
The Living Heart was not an anatomical rendering, tuned to simply replicate what we observed. It was a first-principles model, coupling the network of fibers in the heart’s electrical system, the biological battery that keeps us alive, with the heart’s mechanical response, the muscle contractions that we know as the heartbeat.
The Living Heart virtual twin simulates how the heart beats, offering different views to help scientists and doctors better predict how it will respond to disease or treatment. The center view shows the fine engineering mesh, the detailed framework that allows computers to model the heart’s motion. The image on the right uses colors to show the electrical wave that drives the heartbeat as it conducts through the muscle, and the image on the left shows how much strain is on the tissue as it stretches and squeezes. Dassault Systèmes
Academic researchers had long explored computational models of the heart, but those projects were typically limited by the technology they had access to. Our version was built on industrial-grade simulation software from Dassault Systèmes, a company best known for modeling tools used in aerospace and automotive engineering, where I was working to develop the engineering simulation division. This platform gave teams the tools to personalize an individual heart model using the patient’s MRI and CT data, blood-pressure readings, and echocardiogram measurements, directly linking scans to simulations.
Surgeons then began using the Living Heart to model procedures. Device makers used it to design and test implants. Pharmaceutical companies used it to evaluate drug effects such as toxicity. Hundreds of publications have emerged from the project, and because they all share the same foundation, the findings can be reproduced, reused, and built upon. With each application, the research community’s understanding of the heart snowballed.
Early on, we also addressed an essential requirement for these innovations to make it to patients: regulatory acceptance. Within the project’s first year, the U.S Food and Drug Administration agreed to join the project as an observer. Over the next several years, methods for using virtual-heart models as scientific evidence began to take shape within regulatory research programs. In 2019, we formalized a second five-year collaboration with the FDA’s Center for Devices and Radiological Health with a specific goal.
That goal was to use the heart model to create a virtual patient population and re-create a pivotal trial of a previously approved device for repairing the heart’s mitral valve. This helped our team learn how to create such a population, and let the FDA experiment with evaluating virtual evidence as a replacement for evidence from flesh-and-blood patients. In August 2024, we published the results, creating the first FDA-led guidelines for in silico clinical trials and establishing a new paradigm for streamlining and reducing risk in the entire clinical-trial process.
In 10 years, we went from a concept that many people doubted could be achieved to regulatory reality. But building the heart was only the beginning. Following the template set by the heart team, we’ve expanded the project to develop virtual twins of other organs, including the lungs, liver, brain, eyes, and gut. Each corresponds to a different medical domain, which has its own community, data types, and clinical use cases. Working independently, these teams are progressing toward a breakthrough in our understanding of the human body: a multiscale, modular twin platform where each organ twin could plug into a unified virtual human.
A cardiac digital twin starts with medical imaging, typically MRI, CT, or both. The slices are reconstructed into the 3D geometry of the heart and connected vessels. The geometry of the whole organ must then be segmented into its constituent parts, so each substructure—atria, ventricles, valves, and so on—can be assigned their unique properties.
At this point, the object is converted to a functional, computational model that can represent how the various cardiac tissues deform under load—the mechanics. The complete digital twin model becomes “living” when we integrate the electrical fiber network that drives mechanical contractions in the muscle tissue.
Each part of the heart, such as the left ventricle [left], is superimposed with a detailed digital mesh to re-create its physiology. These pieces come together to form an anatomically accurate rendering of the whole organ [right].Dassault Systèmes
To simulate circulation, the twin adds computational models of hemodynamics, the physics of blood flow and pressure. The model is constrained by boundary conditions of blood flow, valve behavior, and vascular resistance set to closely match human physiology. This lets the model predict blood flow patterns, pressure differentials, and tissue stresses.
Finally, the model is personalized and calibrated using available patient data, such as how much the volume of the heart chambers changes during the cardiac cycle, pressure measurements, and the timing of electrical pulses. This means the twin reflects not only the patient’s anatomy but how their specific heart functions.
When the FDA in silico clinical trial initiative launched in 2019, the project’s focus shifted from these handcrafted virtual twins of specific patients to cohorts large enough to stand in for entire trial populations. That scale is feasible today only because virtual twins have converged with generative AI. Modeling thousands of patients’ responses to a treatment or projecting years of disease progression is prohibitively slow with conventional digital-twin simulations. Generative AI removes that bottleneck.
AI boosts the capability of virtual twins in two complementary ways. First, machine learning algorithms are unrivaled at integrating the patchwork of imaging, sensor, and clinical records needed to build a high-fidelity twin. The algorithms rapidly search thousands of model permutations, benchmark each against patient data, and converge on the most accurate representation. Workflows that once required months of manual tuning can now be completed in days, making it realistic to spin up population-scale cohorts or to personalize a single twin on the fly in the clinic.
Second, enriching AI models’ training sets with data from validated virtual patients grounds the AI simulations in physics. By contrast, many conventional AI predictions for patient trajectories rely on statistical modeling trained on retrospective datasets. Such models can drift beyond physiological reality, but virtual twins anchor predictions in the laws of hemodynamics, electrophysiology, and tissue mechanics. This added rigor is indispensable for both research and clinical care—especially in areas where real-world data are scarce, whether because a disease is rare or because certain patient populations, such as children, are underrepresented in existing datasets.
On the research side, the FDA-sponsored In Silico Clinical Trial Project that we completed in 2024 opened a new world for medical innovations. A conventional clinical trial may take a decade, and 90 percent of new drug treatments fail in the process. Virtual twins, combined with AI methods, allow researchers to design and test treatments quickly in a simulated human environment. With a small library of virtual twins, AI models can rapidly create expansive virtual patient cohorts to cover any subset of the general population. As clinical data becomes available, it can be added into the training set to increase reliability and enable better predictions.
The Living Heart Project has expanded beyond the heart, modeling organs throughout the body. The 3D brain reconstruction [top] shows major pathways in the brain’s white matter connecting color-coded regions of the brain. The lung virtual twin [middle] combines the organ’s geometry with a physics-based simulation of air flowing down the trachea and into the bronchi. And the cross section of a patient’s foot [bottom] shows points of strain in the soft tissue when bearing weight. Dassault Systèmes
Virtual twin cohorts can represent a realistic population by building individual “virtual patients” that vary by age, gender, race, weight, disease state, comorbidities, and lifestyle factors. These twins can be used as a rich training set for the AI model, which can expand the cohort from dozens to hundreds of thousands. Next the virtual cohort can be filtered to identify patients likely to respond to a treatment, increasing the chances of a successful trial for the target population.
The trial design can also include a sampling of patient types less likely to respond or with elevated risk factors, thus allowing regulators and clinicians to understand the risks to the broader population without jeopardizing overall trial success. This methodology enhances precision and efficiency in clinical research, providing population-level insights previously available only after many years of real-world evidence.
Of course, though today’s heart digital twins are powerful, they’re not perfect replicas. Their accuracy is bounded by three main factors: what we can measure (for example, image resolution or the uncertainty of how tissue behaves in real life), what we must assume about the physiology, and what we can validate against real outcomes. Many inputs, like scarring, microvascular function, or drug effects are difficult to capture clinically, so models often rely on population data or indirect estimation. That means predictions can be highly reliable for certain questions but remain less certain for others. Additionally, today’s digital twins lack validation for predicting long-term outcomes years in the future, because the technology has been in use for only a few years.
Over time, each of these limitations will steadily shrink. Richer, more standardized data will tighten personalization of the models. AI tools will help automate labor-intensive steps. And the collection of longitudinal data will improve the model’s ability to reliably predict how the body will evolve over time.
Throughout modern medicine, new technologies have sharpened our ability to diagnose, providing ever-clearer images, lab data, and analytics that tell physicians what is presently happening inside a patient’s body. Virtual twins shift that paradigm, giving clinicians a predictive tool.
This “Living Lung” virtual-twin simulation shows strain patterns during breathing. Mona Eskandari/UC Riverside
Early demonstrations are already appearing in many areas of medicine, including cardiology, orthopedics, and oncology. Soon, doctors will also be able to collaborate across specialties, using a patient-specific virtual twin as the common ground for discussing potential interactions or side effects they couldn’t predict independently.
Although these applications will take some time to become the standard in clinical care, more changes are on the horizon. Real-time data from wearables, for example, could continuously update a patient’s personalized virtual twin. This approach could empower patients to understand and engage more deeply in their care, as they could see the direct effects of medical and lifestyle changes. In parallel, their doctors could get comprehensive data feeds, using virtual twins to monitor progress.
Imagine a digital companion that shows how your particular heart will react to different amounts of salt intake, stress, or sleep deprivation. Or a visual explanation of how your upcoming surgery will affect your circulation or breathing. Virtual twins could demystify the body for patients, fostering trust and encouraging proactive health decisions.
With the Living Heart Project, we’re bringing physics back to physicians. Modern physicians won’t need to be physicists, any more than they need to be chemists to use pharmacology. However, to benefit from the new technology, they will need to adapt their approach to care.
This means no longer seeing the body as a collection of discrete organs and considering only symptoms, but instead viewing it as a dynamic system that can be understood, and in most cases, guided toward health. It means no longer guessing what might work but knowing—because the simulation has already shown the result. By better integrating engineering principles into medicine, we can redefine it as a field of precision, rooted in the unchanging laws of nature. The modern physician will be a true physicist of the body and an engineer of health.
2026-03-19 18:00:05
A technical examination of the sensing, motion control, power, and thermal challenges facing humanoid robotics engineers — with component-level design strategies for real-world deployment.
What Attendees will Learn
2026-03-19 02:00:05
Happy 80th anniversary, ENIAC! The Electronic Numerical Integrator and Computer, the first large-scale, general-purpose, programmable electronic digital computer, helped shape our world.
On 15 February 1946, ENIAC—developed in the Moore School of Electrical Engineering at the University of Pennsylvania, in Philadelphia—was publicly demonstrated for the first time. Although primitive by today’s standards, ENIAC’s purely electronic design and programmability were breakthroughs in computing at the time. ENIAC made high-speed, general-purpose computing practicable and laid the foundation for today’s machines.
On the eve of its unveiling, the U.S. Department of War issued a news release hailing it as a new machine “expected to revolutionize the mathematics of engineering and change many of our industrial design methods.” Without a doubt, electronic computers have transformed engineering and mathematics, as well as practically every other domain, including politics and spirituality.
ENIAC’s success ushered the modern computing industry and laid the foundation for today’s digital economy. During the past eight decades, computing has grown from a niche scientific endeavor into an engine of economic growth, the backbone of billion-dollar enterprises, and a catalyst for global innovation. Computing has led to a chain of innovations and developments such as stored programs, semiconductor electronics, integrated circuits, networking, software, the Internet, and distributed large-scale systems.
The motivation for developing ENIAC was the need for faster computation during World War II. The U.S. military wanted to produce extensive artillery firing tables for field gunners to quickly determine settings for a specific weapon, a target, and conditions. Calculating the tables by hand took “human computers” several days, and the available mechanical machines were far too slow to meet the demand.
Birth of electronic computing
Foundation for modern computer hardware
Popular mainframe computer
Programmed Data Processor (PDP-11)
Popular 16-bit minicomputer
Beginning of the microprocessor and microcomputer era
First supercomputer
Popular 32-bit minicomputer
Personal and small-business computing
Digital communication, interaction, and transaction (e-commerce)
Beginning of the cloud computing revolution
Handheld computer/tablet
Delivered real-time decision-making, smart manufacturing, and logistics
First reprogrammable quantum computer demonstrated
Ignited interest in quantum computing
Widespread use of GenAI by individuals, businesses, and academia
80 years of computing evolution
In 1942 John Mauchly, an associate professor of electrical engineering at Penn’s Moore School, suggested using vacuum tubes to speed up computer calculations. Following up on his theory, the U.S. Army Ballistic Research Laboratory, which was responsible for providing artillery settings to soldiers in the field, commissioned Mauchly and his colleagues J. Presper Eckert and Adele Katz Goldstine, to work on a new high-speed computer. Eckert was a lab instructor at Moore, and Goldstine became one of ENIAC’s programmers. It took them a year to design ENIAC and 18 months to build it.
The computer contained about 18,000 vacuum tubes, which were cooled by 80 air blowers. More than 30 meters long, it filled a 9 m by 15 m room and weighed about 30 kilograms. It consumed as much electricity as a small town.
Programming the machine was difficult. ENIAC did not have stored programs, so to reprogram the machine, operators manually reconfigured cables with switches and plugboards, a process that took several days.
By the 1950s, large universities either had acquired or built their own machines to rival ENIAC. The schools included Cambridge (EDSAC), MIT (Whirlwind), and Princeton (IAS). Researchers used the computers to model physical phenomena, solve mathematical problems, and perform simulations.
After almost nine years of operation, ENIAC officially was decommissioned on 2 October 1955.
ENIAC in Action: Making and Remaking the Modern Computer, a book by Thomas Haigh, Mark Priestley, and Crispin Rope, describes the design, construction, and testing processes and dives into its afterlife use. The book also outlines the complex relationship between ENIAC and its designers, as well as the revolutionary approaches to computer architecture.
In the early 1970s, there was a controversy over who invented the electronic computer and who would be assigned the patent. In 1973 Judge Earl Richard Larson of U.S. District Court in Minnesota ruled in the Honeywell v. Sperry Rand case that Eckert and Mauchly did not invent the automatic electronic digital computer but instead had derived their subject matter from a computer prototyped in 1939 by John Vincent Atanasoff and Clifford Berry at Iowa State College (now Iowa State University). The ruling granted Atanasoff legal recognition as the inventor of the first electronic digital computer.
In 1987 IEEE designated ENIAC as an IEEE Milestone, citing it as “a major advance in the history of computing” and saying the machine “established the practicality of large-scale electronic digital computers and strongly influenced the development of the modern, stored-program, general-purpose computer.”
The commemorative Milestone plaque is displayed at the Moore School, by the entrance to the classroom where ENIAC was built.
“The ENIAC legacy heralded the computer age, transforming not only science and industry but also education, research, and human communication and interaction.”
A paper on the machine, published in 1996 in IEEE Annals of the History of Computing and available in the IEEE Xplore Digital Library, is a valuable source of technical information.
“The Second Life of ENIAC,” an article published in the annals in 2006, covers a lesser-known chapter in the machine’s history, about how it evolved from a static system—configured and reconfigured through laborious cable plugging—into a precursor of today’s stored-program computers.
A classic history paper on ENIAC was published in the December 1995 IEEE Technology and Society Magazine.
The IEEE Inspiring Technology: 34 Breakthroughs book, published in 2023, features an ENIAC chapter.
One of the most remarkable aspects of the ENIAC story is the pivotal role women played, according to the book Proving Ground: The Untold Story of the Six Women Who Programmed the World’s First Modern Computer, highlighted in an article in The Institute. There were no “programmers” at that time; only schematics existed for the computer. Six women, known as the ENIAC 6, became the machine’s first programmers.
The ENIAC 6 were Kathleen Antonelli, Jean Bartik, Betty Holberton, Marlyn Meltzer, Frances Spence, and Ruth Teitelbaum.
“These six women found out what it took to run this computer, and they really did incredible things,” a Penn professor, Mitch Marcus, said in a 2006 PhillyVoice article. Marcus teaches in Penn’s computer and information science department.
In 1997 all six female programmers were inducted into the Women in Technology International Hall of Fame, in Los Angeles.
Two other women contributed to the programming. Goldstine wrote ENIAC’s five-volume manual, and Klára Dán von Neumann, wife of John von Neumann, helped train the programmers and debug and verify their code.
To honor the women of ENIAC, the IEEE Computer Society established the annual Computer Pioneer Award in 1981. Eckert and Mauchly were among the award’s first recipients. In 2008 Bartik was honored with the award. Nominations are open to all professionals, regardless of gender.
Last year a group of 80 autistic students, ages 12 to 16, from PS Academy Arizona, in Gilbert, recreated the ENIAC using 22,000 custom parts. It took the students almost six months to assemble.
A ceremony was held in January to display their creation. The full-scale replica features actual-size panels made from layered cardboard and wood. Although all electronic components are simulated, they are not electrically active. The machine, illuminated by hundreds of LEDs, is accompanied by a soundtrack that simulates the deep hum of ENIAC’s transformers and the rhythmic clicking of relays.
“Every major unit, accumulators, function tables, initiator, and master programmer is present and placed exactly where it was on the original machine,” Tom Burick, the teacher who mentored the project, said at the ceremony.
The replica, still on display at the school, is expected to be moved to a more permanent spot in the near future.
ENIAC’s significance is both technical and symbolic. Technically, it marks the beginning of the chain of innovations that created today’s computational infrastructure. Symbolically, it made governments, militaries, universities, and industry view computation as a tool for improvement and for innovative applications that had previously been impossible. It marked a tectonic shift in the way humans approach problem-solving, modeling, and scientific reasoning.
The ENIAC legacy heralded the computer age, transforming not only science and industry but also education, research, and human communication and interaction.
As Eckert is reported to have said, “There are two epochs in computer history: Before ENIAC and After ENIAC.”
The remarkable evolution of computer hardware during the past 80 years has been sparked by advances in programming languages—the essential drivers of computing.
From the manual rewiring of ENIAC to the orchestration of intelligent, distributed systems, programming languages have steadily evolved to make computers more powerful, expressive, and accessible.
Computing history teaches us that flexibility, accessibility, collaboration, sound governance, and forward thinking are essential for sustained technological progress. In a recent Communications of the ACM article, Richa Gupta identified four historic shifts that led to computing’s rapid, transformative progress:
The evolution of computing will continue along multiple trajectories, with the emphasis moving from generalization to specialization (for AI, graphics, security, and networking), from monolithic system design to modular integration, and from performance-centric metrics alone to energy efficiency and sustainability as primary objectives.
Increasingly, security will be built into hardware by design. Computing paradigms will expand beyond traditional deterministic models to embrace probabilistic, approximate, and hybrid approaches for certain tasks.
Those developments will usher in a new era of computing and a new class of applications.