Wealth tax criticized by billionaires and Gavin Newsom would levy a one-time 5% tax on residents worth over $1bn

A popular proposal in California to impose a wealth tax on billionaires has gained enough signatures to qualify for the ballot in November, state officials announced on Wednesday.

The news is set to intensify an already heated debate around the tax, which has pitted tech moguls and the state’s governor, Gavin Newsom, against the labor union backing the measure.

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© Photograph: Jae C Hong/AP

© Photograph: Jae C Hong/AP

© Photograph: Jae C Hong/AP

The popular framing of algorithmic recommendation, repeated across most contemporary writing on consumer technology, is that personalised suggestions surface preferences the consumer already has. The algorithm watches what someone does, identifies a pattern, and offers more of what the pattern predicts they will like. On this account, the algorithm is a useful but essentially neutral tool, one that accelerates discovery without changing what the consumer would have chosen if left to themselves.

The peer-reviewed experimental evidence accumulated over the past twenty years has substantially complicated this picture.

What the controlled studies have repeatedly shown is that algorithmic recommendations do not merely reflect existing preferences. They actively shape them. The brain treats the recommendation itself as a piece of information, an informational anchor, that influences subsequent judgment regardless of whether the underlying algorithm is accurate or not. The same effect persists when the recommendations are deliberately randomised, when they are intentionally incorrect, and when the consumer has been explicitly told that the recommendations may be unreliable. The mechanism is not driven by the algorithm being right. The mechanism is driven by the algorithm being present.

The Adomavicius experiments

The foundational peer-reviewed study of this effect was published in 2018 by Gediminas Adomavicius at the University of Minnesota, with Jesse Bockstedt at the University of Arizona, Shawn Curley at Minnesota, and Jingjing Zhang at Indiana University, in the journal Information Systems Research. The team designed three controlled experiments to test whether personalised recommendation ratings produced by a recommender system would influence the prices that participants would be willing to pay for digital music tracks.

The first experiment showed participants algorithmic recommendation ratings for a series of songs. The ratings were presented as if they had been personalised to each participant by a recommender system trained on their listening history. The ratings were, in fact, randomly assigned. Participants were then asked how much they would be willing to pay for each song. The team found that the randomly assigned ratings substantially altered the prices participants were willing to pay, even when the team controlled for the participants’ own underlying preferences and demographics. Higher random ratings produced higher willingness-to-pay figures. Lower random ratings produced lower figures. The shift was statistically robust across the sample.

The second experiment used actual system-generated recommendations from a real recommender system, but the team intentionally perturbed the recommendations by introducing controlled errors. Participants saw recommendations that were partially based on their preferences but deliberately shifted away from what an unperturbed algorithm would have produced. The team observed substantially the same effect. The participants’ willingness to pay tracked the perturbed recommendations rather than their underlying preferences.

The third experiment reduced preference uncertainty by giving participants substantial time and information to develop firm views about the songs before they were exposed to the recommendation ratings. The hypothesis the team tested was that participants who had already made up their minds about the music would be less susceptible to algorithmic influence. The hypothesis did not hold. Even participants with stable, well-developed preferences showed the same willingness-to-pay shifts in response to randomly assigned algorithmic recommendations.

The anchoring mechanism

The Adomavicius team’s interpretation, supported by the broader peer-reviewed literature on cognitive anchoring effects, was that the recommendation operates as a numerical anchor that the brain uses as a starting point for subsequent judgment. The classic anchoring effect, identified by Daniel Kahneman and Amos Tversky in the 1970s and replicated in thousands of subsequent studies, shows that arbitrary numerical values presented before a judgment systematically shift the judgment toward the anchor, even when the people making the judgment know the anchor is irrelevant.

Algorithmic recommendations function as anchors in the same way. A recommendation of four out of five stars sets a numerical reference point that influences the consumer’s subsequent valuation of the product, regardless of whether the four-star recommendation reflects anything meaningful about the consumer’s actual preferences. The consumer who has been told a song is worth four stars, by what they believe to be an accurate personalised algorithm, will offer a price closer to what a four-star song commands than they would have offered in the absence of the recommendation. The shift occurs whether the four-star rating is generated by an accurate algorithm, an inaccurate algorithm, or a random number generator.

The implication is that the source of the algorithmic influence is not the accuracy of the algorithm. The source is the presence of the algorithm. The consumer is being influenced by a numerical informational anchor that has been generated by a system the consumer trusts to be measuring their preferences. Whether the system is actually doing that is, on the peer-reviewed evidence, largely beside the point.

The Amazon and YouTube specifics

The peer-reviewed experimental findings sit alongside a substantial body of industry data on the actual scale of algorithmic influence in major consumer platforms. Amazon’s recommendation engine, on figures the company has publicly disclosed and that have been independently analysed by McKinsey and Company since 2013, drives approximately 35 per cent of all purchases made on the platform. The figure has held roughly steady for over a decade, despite extensive evolution in the underlying algorithms and substantial growth in Amazon’s product catalogue.

YouTube’s chief product officer Neal Mohan disclosed at the Consumer Electronics Show in January 2018 that approximately 70 per cent of all viewing time on YouTube comes from videos selected by the platform’s recommendation algorithm rather than from videos that users have actively searched for. The figure has been widely confirmed by subsequent industry analysis and remains the standard reference for the role of algorithmic recommendation in video discovery. YouTube now has approximately 2.5 billion monthly users.

The figures across other major platforms follow similar patterns. Pew Research Center has found that approximately 81 per cent of YouTube users say they at least occasionally watch recommended videos, with 15 per cent reporting that they do so regularly. The recommendation algorithms on TikTok and Instagram measure user engagement to the millisecond and adjust the content stream in real time based on which videos a user has lingered on, replayed, or scrolled past quickly. The effect of all this, on the available evidence, is that the majority of the content the average person encounters in the major consumer platforms is selected by algorithms rather than chosen by the person consuming it.

The manipulation framework

A 2019 peer-reviewed article by Daniel Susser at Pennsylvania State University, Beate Roessler at the University of Amsterdam, and Helen Nissenbaum at Cornell Tech, published in the Georgetown Law Technology Review, argued that the systematic deployment of algorithmic influence in digital platforms now meets the philosophical definition of manipulation rather than persuasion. The distinction matters because manipulation, in the philosophical literature, is defined as the deliberate influence of someone’s beliefs, desires, or actions through means that bypass their rational agency. Persuasion, by contrast, addresses someone’s rational agency directly by giving them reasons to change their beliefs or actions.

The Susser team’s argument was that algorithmic recommendations, as currently deployed, systematically bypass the rational agency of consumers. The consumer is not given reasons why a particular product is being recommended. The consumer is not told what factors the algorithm has weighted, or how heavily, or what alternatives have been excluded. The consumer is presented with a numerical or categorical anchor and expected to make a decision in response to it. The cognitive mechanism the recommendation activates, on the Adomavicius experimental evidence, is the anchoring effect, which is precisely the kind of non-rational decision-making bias that the philosophical literature has long identified as the substrate of manipulation.

The Susser argument has not produced consensus. Other scholars have argued that the line between manipulation and persuasion is blurry, that consumers retain meaningful agency to ignore or reject algorithmic recommendations, and that the consumer welfare benefits of algorithmic curation outweigh the autonomy costs. The peer-reviewed dispute is genuinely ongoing. What is not in dispute is that the cognitive mechanisms identified by the Adomavicius team operate in ways the consumer is generally unaware of, and that they produce substantial behavioural effects that the consumer would not necessarily endorse if they were aware of them.

The honest limitations

Several methodological caveats apply to the literature described above.

The Adomavicius experimental work has been conducted primarily in laboratory settings using digital music as the stimulus. The generalisability of the findings to other product categories, other recommendation contexts, and the open-ended environment of actual consumer life is plausible but not perfectly established. The effect sizes observed in the lab may be larger or smaller than the effect sizes that obtain in everyday digital commerce.

The industry figures for Amazon and YouTube are derived from corporate disclosures rather than from independent peer-reviewed measurement. The 35 per cent and 70 per cent figures are widely cited and broadly consistent with the available academic analyses, but they should be treated as informed estimates rather than as precise scientific measurements.

The Susser 2019 manipulation framework is a philosophical argument rather than an empirical finding. The conceptual definition of manipulation, and the application of that definition to algorithmic recommendation systems, remain genuinely contested in the peer-reviewed legal and philosophical literature. The argument is strong but is not the only available position.

The anchoring mechanism identified by the Adomavicius team is one of several cognitive effects that have been proposed to explain algorithmic influence on consumer behaviour. Other proposed mechanisms include social proof effects, decision fatigue and the deferral of judgment to perceived authority, identity-reinforcement effects in which recommendations mirror and amplify the consumer’s existing self-concept, and choice architecture effects in which the structure of the recommendation environment itself shapes which options the consumer considers. The relative contributions of these mechanisms are not yet fully understood.

What it means

Several things follow from the peer-reviewed evidence on algorithmic influence on consumer choice that are worth saying clearly.

The first is that the popular framing of algorithmic recommendation as a neutral discovery tool is not supported by the available experimental evidence. The recommendation is not a neutral observation of what the consumer would have chosen. It is an active intervention in the consumer’s decision-making process, operating through cognitive anchoring mechanisms that are substantially independent of the recommendation’s accuracy. The consumer who receives a recommendation is being measurably influenced by the fact of the recommendation, not just by its content.

The second is that the scale of algorithmic influence in modern consumer platforms is substantial. Approximately 35 per cent of Amazon purchases, approximately 70 per cent of YouTube viewing time, and the majority of content consumption on TikTok, Instagram, and the major streaming platforms now come from algorithm-driven recommendation rather than from user-initiated search. The figure means that, for most consumers in most digital environments, what they encounter is selected by algorithms rather than by them.

The third is that the cognitive mechanism producing the algorithmic influence is one the consumer is generally not aware of, and one the consumer cannot easily resist even when they have been told the recommendation may be unreliable. The Adomavicius team’s third experiment, in which participants with firm pre-existing preferences still showed the influence effect, suggests that informed scepticism about algorithms does not, by itself, neutralise the cognitive mechanisms by which algorithms shape decisions.

The fourth, on the strongest current reading of approximately twenty years of peer-reviewed evidence in behavioural economics, decision psychology, and the philosophy of digital technology, is that the consumer who believes their choices in algorithmically curated environments are predominantly their own is, on the available evidence, mistaken in a measurable way.

The choices are partly theirs.

They are also partly the algorithm’s.

The peer-reviewed evidence has not yet established the precise weighting, but it has established that the second contribution is larger than the popular framing has assumed.

The post Approximately 35 per cent of all purchases made on Amazon are driven by its recommendation engine, approximately 70 per cent of all viewing on YouTube comes from algorithm-driven recommendations, and peer-reviewed evidence has shown that even randomly assigned recommendations measurably alter what consumers are willing to pay for a product appeared first on Space Daily.

Scaling AI takes more than adding GPUs. Enterprises need servers, storage, networking, data access, and orchestration to work together so production workloads can grow without exposing costly bottlenecks.

A new material, inspired by the frustratingly powerful strength of a tangled ball of staples, could represent the future of flexible, adaptable design.

Published in the Journal of Applied Physics, material scientists and engineers from the University of Colorado Boulder’s Paul M. Rady Department of Mechanical Engineering revealed an interlocking particle design that can be shaped into rigid structures, and then return to a pile of loose pieces with only a targeted movement or vibration.

The particles, the team says, could potentially be used as a building material with high strength and flexibility, capable of reshaping into new forms on command.

Staple Ball Strength

The difficulty of separating tangled metal, such as a cluster of standard office staples, intrigued the researchers enough to investigate how interlocking particles could be used in recyclable construction. Such techniques are found in nature, as birds weave together found objects to create strong structures to protect their young.

On the level of particles, interlocking such elements is called “entanglement,” and is a relatively new focus for material scientists.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      

“We’ve been playing around with the idea of building blocks and geometry for many years, but we started looking at interlocking, entangled particles only recently,” said Professor Francois Barthelat, the leader of the Laboratory for Advanced Materials & Bioinspiration.

“We are excited about the combination of properties we can get out of these systems, and we believe this technology has the potential to go in many directions,” Barthelat said.

Manufacturing Entanglement

While entanglement occurs naturally, as in the combination of soft protein and hard minerals that interlock to form bones, producing it artificially requires a careful understanding of how these interactions work. According to the team, particle shape is the most foundational element to natural entanglement.

“Let’s take sand as an example,” says PhD student Youhan Sohn. “Sand is smooth and convex-shaped, meaning it cannot interlock from grain to grain.”

“However, we found that if we change the shape of a grain of sand, we can drastically affect its behavior and mechanical properties, including the particle’s ability to link with other particles,” Sohn adds.

With shape decided as the researcher’s primary focus, they began running computer models, called Monte Carlo simulations, to observe how various potential shapes would interact, aiming to identify the optimal entanglement geometry. The shape their research settled on was remarkably similar to that of a staple: a particle with two legs.

Laboratory Material Experiments

After achieving success in the digital realm, the researchers fabricated the shape in the real world to test its behavior under practical conditions. During these tests, the team discovered that their optimized shape contained even more beneficial qualities than they had initially understood.

Conventional materials typically experience a trade-off between toughness and tensile strength. Typically, materials that are extremely tough and can absorb large amounts of energy are also quite brittle, lacking the ability to stretch or bend. However, this staple-like particle design exhibits an unusually high amount of both properties simultaneously.

“Our entangled granular material using the staple-like particle demonstrates both high strength and toughness at the same time,” said PhD student Saeed Pezeshki.

Additionally, they discovered that applying different vibrational patterns would change the particles’ degree of entanglement, allowing the team to assemble and disassemble a shape instantly. Applying a light vibration caused the pieces to interlock, whereas a heavy vibration untangled them as a pile of loose particles.

Material Applications

“No binder is required, so particles can be rapidly assembled in an infinite number of shapes, which can be reconfigured with appropriate mechanical stimuli, making them attractive for lightweight and reversible materials and structures and aggregate architectures,” the authors recently wrote.

“The fundamental understanding of entanglement gained in this project is also relevant to other physical systems, including biological structures, ‘living’ entangled matter, robotic materials, as well as colloidal assemblies,” the authors add.

“It’s a strange material because it’s obviously not a liquid. However, it’s also not quite solid,” Barthelat said. “This opens new and intriguing engineering possibilities.”

“Handling a bundle of these entangled particles feels very remote and exotic,” he added.

The paper, “Combined Effects of Particle Geometry and Applied Vibrations on the Mechanics and Strength of Entangled Materials,” appeared in the Journal of Applied Physics on April 14, 2026.

Ryan Whalen covers science and technology for The Debrief. He holds an MA in History and a Master of Library and Information Science with a certificate in Data Science. He can be contacted at ryan@thedebrief.org, and follow him on Twitter @mdntwvlf.

Knowledge assistants can deliver real value when they are grounded in trusted enterprise data. Production success depends on retrieval quality, source traceability, access controls, workflow integration, and ongoing governance.

Agentic AI is pushing workplace AI from assistance into action. To scale safely, enterprises need secure runtimes, governed data access, open ecosystems, and infrastructure built for production AI agents.

They claim to fix fine lines, blemishes, and redness – but which stand up to scrutiny? After consulting dermatologists and putting them to the test, we have crowned a brand new number one

• The best anti-ageing creams, serums and treatments

LED face masks are booming in popularity – despite being one of the most expensive at-home beauty products to hit the market. They claim to either reduce the appearance of fine lines, stop spots or calm redness, with some even combining different types of light to enhance the benefits.

However, it’s wise to be sceptical about new treatments that are costly and non-invasive, and to do your research before you buy. With this in mind, I interviewed doctors and dermatologists to find out whether these light therapy devices work.

Best LED mask overall:
CurrentBody Multi Light Therapy LED mask

Best budget LED face mask:
Silk’n LED face mask 100

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© Composite: PR Image

© Composite: PR Image

© Composite: PR Image

A Booming Market for Drone Defense Drone incursions are shutting down European airports and hitting oil fields across

European countries are increasing their military spending and defence capabilities not only through new orders but also through

OpenAI appears to be testing a new subscription and experience for science use cases, but it's unclear if it'll be available to everyone regardless of their background. [...]

A study I read recently has stuck with me. It found that the people who lean hardest on AI tools tend to score lowest on critical thinking, with the steepest gap among the young.

I use AI tools every day for parts of my own writing work, so this is not a doom blog about technology rotting our brains. It is more that I read the study, recognized something in my own habits, and wanted to think through what it actually says before deciding how worried to be.

I am not a psychologist or a cognitive scientist, and I am not credentialed in any of this. What follows is one writer reading the research and thinking aloud about it. The study at the center of this is observational, and a population-level pattern is not a verdict on you or on anyone in particular.

What the study actually found

The work comes from Michael Gerlich at SBS Swiss Business School, published in early 2025 in the journal Societies. He used a mixed-method design: a survey of 666  participants across a range of ages and education levels, plus 50 follow-up interviews to put some texture on the numbers.

The headline result is blunt. As Gerlich put it, “The findings reveal a strong negative correlation between frequent AI tool usage and critical thinking abilities, mediated by cognitive offloading.”

Before that sentence does too much work, the word that matters most is correlation. Gerlich is careful about this, and so should we be. He notes that “the study highlights correlations rather than direct causation, meaning that while AI usage and cognitive offloading are linked to reduced critical thinking, additional factors may also contribute to this relationship.”

The reverse direction is also possible: that people who already think less critically may simply reach for the tool more. The study cannot tell you which way the arrow points.

The cognitive offloading mechanism

Cognitive offloading is the habit of delegating a mental task to something outside your head, a calculator, a sticky note, a search bar, an AI. We have always done it. The question the study raises is what happens when the thing we offload to is good enough to do the whole job, not just hold the answer.

There is an experimental companion piece worth knowing about, though with heavier caveats. An MIT Media Lab study by Nataliya Kosmyna and colleagues had 54 people write essays in three conditions, using an AI assistant, a search engine, or nothing but their own heads, while measuring brain activity. The brain-only writers showed the strongest neural connectivity; the AI users the weakest, and they struggled to quote work they had just produced. The main sample is small, the follow-on crossover phase included only 18 participants. Treat it as a suggestive clue rather than proof. Even with those caveats, it points at the same intuition from a different angle: when the tool ends up doing most of the assembling, less of the assembling is being done by the writer.

The young show the sharpest effect

The age pattern is the part of the study that is most worrying. The youngest band, aged 17 to 25, used AI most and scored lowest on critical thinking, while those 46 and above used it least and scored highest. Higher education was protective across the board.

Gerlich’s reading of this is appropriately tentative, and I want to keep it that way. He suggests that “digital natives, who have grown up with AI-integrated technologies, might be more prone to cognitive offloading than older generations.” Note the hedges in his own sentence. The reading is softer than “young people are bad at thinking” — it is a researcher saying that people who never knew a world without these tools may lean on them more reflexively, simply because the tools were always there.

What to do with this

I don’t think the answer is to throw the tools away.

Gerlich himself lands on a balanced note: “AI tools are not inherently detrimental; rather, their impact depends on how they are used.” His own prescription, framed as his view rather than settled fact, is that “AI should complement cognitive engagement rather than replace it.”

That distinction is an important one. Whether AI extends my thinking or stands in for it has turned out to be the question that matters most in my own practice.

What I take from this is small and practical: notice which part of the task you are handing over. Outsourcing a lookup matters less than outsourcing the judgment about what the lookup means, and the second one is what I try to keep watch on.

None of this is a substitute for talking to someone qualified — a therapist or career counsellor, or a trusted person in your life — if the bigger questions here, about work, change, or where you stand, are weighing on you more than they are interesting you.

The post A 2025 study of over 600 people found a correlation between leaning on AI tools and weaker critical thinking, sharpest among the young appeared first on Space Daily.

In a groundbreaking advancement that promises to reshape the landscape of wearable technology and soft robotics, researchers at the Korea Advanced Institute of Science and Technology (KAIST) have developed a pioneering self-powered piezoelectric fiber sensor exhibiting unprecedented stretchability and resilience. This novel device, capable of enduring elongations up to 668%, heralds a new era of flexible sensors that can reliably monitor physiological signals over extended periods without relying on external power sources such as batteries.

Wearable medical devices designed to continuously track vital signs—heart rate, respiration, and joint movement—have long been limited by the mechanical and electrical degradation of their sensing components under repeated deformations. Traditional piezoelectric fiber sensors, which convert mechanical forces into electrical signals, often suffer from diminished performance as their thin electrode layers and fragile piezoelectric materials falter during routine bending and stretching. Overcoming this barrier, the KAIST research team led by Professor Miso Kim has introduced a transformative approach that stabilizes sensor function even after thousands of mechanical cycles.

Central to this breakthrough is the adoption of a meticulously engineered “Hierarchical Resilient Design,” wherein resilience is embedded across multiple structural levels of the sensor. This concept mirrors the elasticity of a rubber band, allowing the piezoelectric fibers to recover their original form and function after extensive stretching. The research team achieved this by dispersing elastic polymer microparticles within the piezoelectric nanofibers, creating a Velcro-like interlocking microstructure that enhances the sensor’s mechanical robustness and self-restorative capabilities.

The interface engineering between the sensor’s electrical components also proved critical. By enhancing the bonding between the piezoelectric layers and the electrodes, the team prevented delamination—a common failure mode under deformation—that typically degrades signal integrity. This strong adhesion at the material interfaces ensures continuous charge transfer and maintains stable electrical outputs even when the sensor is twisted, pressed, or stretched significantly.

One of the most remarkable aspects of the sensor’s design is its coil and knot structures. By coiling the piezoelectric fibers, the researchers capitalized on geometric extensions, enabling the sensor to stretch over six times its initial length without losing sensitivity. Furthermore, knot configurations were tested to validate sensor durability under complex and dynamic mechanical stresses such as sudden impacts or localized pressure, demonstrating stable electrical signals regardless of deformation mode.

Beyond mechanical resilience, the KAIST team integrated artificial intelligence techniques to interpret the electrical outputs from the sensor. Utilizing machine learning algorithms, they could accurately distinguish between distinct mechanical stimuli—such as bending, stretching, and pressing—offering a valuable platform for intuitive and precise biosignal monitoring in real-world environments. This capability opens new horizons for dynamic human-machine interfacing, where nuanced movement detection is critical.

What sets this breakthrough apart is its self-powered nature. Unlike conventional wearable sensors that depend on finite battery life, the piezoelectric polymer fibers harvest mechanical energy directly from body movements, converting it into usable electrical signals. This sustainable mechanism not only eliminates maintenance concerns but also allows for ultra-lightweight, unobtrusive devices suitable for long-term wear.

The implications of this technology extend well beyond healthcare monitoring devices. Electronic skins capable of mimicking human sensory reception, soft robotic actuators with sensitive feedback loops, and next-generation digital health tools could all benefit from the combination of stretchability, mechanical resilience, and stable self-generated electrical outputs demonstrated by this platform. Such multifunctional usability places this development firmly at the forefront of flexible electronics innovation.

Professor Miso Kim emphasized the significance of their approach: “By simultaneously achieving mechanical resilience and electrical reliability through fiber structure design combined with precise electrode interface engineering, we have laid the foundation for wearable devices capable of long-term operation under strenuous conditions,” she remarked. This robust design paradigm, she asserts, will empower a new generation of devices that continuously monitor biosignals with unprecedented accuracy and durability.

Published in the prestigious journal ACS Nano, the team’s research showcases both fundamental materials science and applied engineering excellence. The paper titled “Mechanically and Functionally Resilient Piezoelectric Fiber Coils and Knots for Reliable Self-Powered Sensing” details the synthesis of the nanofiber composites, the meticulous fabrication of coil and knot geometries, and a comprehensive analysis of the sensor’s electromechanical performance over extensive cyclical tests.

The scientific community has lauded this work for addressing long-standing issues in flexible sensor design. By merging nanoscale material innovations with macrostructural strategies, the KAIST team has created a versatile sensing platform that bridges the gap between laboratory prototypes and real-world applications. Their work sets a precedent for future interdisciplinary efforts aimed at wearable electronics that demand both high performance and durability.

Funded by multiple National Research Foundation of Korea initiatives, this research underscores Korea’s growing leadership in advanced materials and smart sensor development. The integration of elastic polymer microparticles within piezoelectric nanofibers, combined with interface science and architectural engineering, exemplifies the kind of holistic approach necessary to push boundaries in next-generation electronics.

Looking ahead, this resilient, self-powered fiber sensor technology could transform how we collect and interpret physiological data in ambulatory settings, enable more sophisticated prosthetics with tactile feedback, and empower soft robots with human-like sensory capabilities. As devices become lighter, more adaptable, and energy-autonomous, the convergence of materials science and artificial intelligence will accelerate innovations that make previously impossible applications a reality.

In conclusion, the KAIST team’s pioneering piezoelectric fiber sensor offers a robust and reliable solution to historic challenges in wearable sensory technology. Its remarkable combination of extreme stretchability, stable electricity generation without batteries, and intelligent signal analysis marks a significant leap forward that is poised to impact multiple sectors, from healthcare to robotics and beyond.

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Subject of Research: Development of highly stretchable, mechanically resilient, self-powered piezoelectric fiber sensors for wearable devices and soft robotics.

Article Title: Mechanically and Functionally Resilient Piezoelectric Fiber Coils and Knots for Reliable Self-Powered Sensing

News Publication Date: June 18, 2026

Web References: https://doi.org/10.1021/acsnano.5c19628

References: Choi, Y. J., Park, J., Nam, J., Sim, G.-D., Kim, M.-G., & Kim, M. (2026). Mechanically and Functionally Resilient Piezoelectric Fiber Coils and Knots for Reliable Self-Powered Sensing. ACS Nano.

Image Credits: KAIST

Keywords

Self-powered sensor, piezoelectric polymer, wearable medical devices, stretchable electronics, fiber coil sensor, interface engineering, mechanical resilience, electrical stability, nanofiber composites, artificial intelligence, biosignal monitoring, flexible sensors.

Physicians and researchers at the Netherlands Cancer Institute have unveiled a groundbreaking artificial intelligence (AI) model that fundamentally reshapes how treatment responses in pleural mesothelioma—a notoriously challenging cancer—are evaluated. This model, called ARTIMES, excels beyond traditional clinical methods, surpassing expert human judgment in accuracy and efficiency. By precisely measuring the entire tumor volume instead of relying on conventional diameter-based assessments, ARTIMES promises to revolutionize patient care and accelerate clinical research in this difficult-to-treat disease.

Pleural mesothelioma poses unique diagnostic challenges because it develops as a thin, irregular layer along the lining of the lungs rather than forming discrete masses. This morphology renders existing international standards like the RECIST (Response Evaluation Criteria in Solid Tumors) inadequate. RECIST depends primarily on simple diameter measures, which poorly represent the tumor’s true progression or regression in mesothelioma’s diffuse growth pattern. Clinicians have expressed frustration and uncertainty in evaluating treatment efficacy using these parameters, highlighting a pressing need for a more refined and reliable approach.

To address these limitations, an interdisciplinary team of AI scientists, radiologists, and pulmonologists collaborated at the Netherlands Cancer Institute. Leveraging an extensive dataset comprising over 11,000 computed tomography (CT) scans from more than 2,000 patients across 121 hospitals worldwide, they developed ARTIMES, an AI-driven volumetric response evaluation tool. Unlike humans, who face near-impossible challenges in manually delineating tumor boundaries at the pixel level on complex images, ARTIMES can effortlessly segment entire tumors and calculate their true volume with exceptional precision.

Pulmonologist Sjaak Burgers emphasizes that ARTIMES advances clinical practice by eliminating tedious and error-prone manual tumor assessments. While verifying the AI’s output remains essential, the review process is far less labor-intensive. This reduces interobserver variability and enables clinicians to obtain more consistent and objective insights into tumor dynamics. The ability to evaluate the full tumor burden rather than a single diameter mark dramatically increases sensitivity for detecting true positive or negative treatment responses.

The scientific community is witnessing a milestone with ARTIMES being the first AI model worldwide to demonstrably outperform clinicians in assessing treatment outcomes for pleural mesothelioma. Kevin Groot Lipman, lead author and technical physician, highlights that their study, published in The Lancet Oncology, cements AI’s potential to become an integral clinical decision support tool. Importantly, ARTIMES enhances rather than replaces physician judgment, interfacing smoothly into existing workflows while enabling rapid, data-driven decision-making.

Beyond measuring tumor volume, the researchers have undertaken the critical task of integrating ARTIMES measurements into actionable clinical guidelines. Since knowing the tumor size alone does not dictate specific treatment changes, these criteria empower pulmonologists to determine when to modify or cease therapies. This synergy ensures that patients receive individualized care tailored to their tumor behavior patterns, reducing exposure to ineffective treatments and unnecessary side effects while optimizing healthcare resources.

One of ARTIMES’s most transformative capabilities is its ability to detect non-response to therapy earlier than ever before. This timely recognition allows physicians to pivot treatment plans sooner, offering patients alternative therapeutic avenues or sparing them from futile and potentially harmful continuation of ineffective regimens. The combination of predictive accuracy and clinical oversight represents a major leap forward in precision oncology for pleural mesothelioma patients.

Currently, EU regulations restrict ARTIMES’s use exclusively to the Netherlands Cancer Institute under an in-house exemption, given that the model was developed internally. Nonetheless, the research team is actively pursuing regulatory approval to deploy ARTIMES globally in other hospitals. There is hopeful anticipation surrounding proposed EU frameworks aimed at streamlining the certification process for AI-enabled medical devices, which could accelerate widespread adoption and patient benefit.

The advent of ARTIMES is poised to deliver a shockwave across oncology fields by demonstrating the tangible superiority of AI over human evaluators in complex tumor assessments. To foster transparency and collaborative innovation, the Netherlands Cancer Institute has made the mesothelioma AI model publicly accessible online, enabling researchers worldwide to explore and extend its applications. This open science approach is expected to catalyze new studies and adaptations for other tumor types with challenging morphologies.

Already, the NKI team is extending their AI methodologies to address lung cancer and brain metastasis tumor evaluations. The success of ARTIMES signals the dawn of a new epoch in oncological imaging, where volumetric and morphological complexities that previously hindered precise quantification become tractable. Such breakthroughs unlock significant potential to enhance clinical trial design by furnishing robust, reproducible endpoints that more faithfully capture therapeutic impact.

Clinical trials for novel treatments stand to gain markedly from ARTIMES’s introduction. Using data from eight distinct trials, the research team validated that AI-guided volumetric criteria yield significantly improved accuracy relative to traditional RECIST assessments. This refined precision enables better evaluation of an investigational drug’s efficacy, ultimately accelerating regulatory approval timelines and facilitating faster patient access to promising therapies.

In summary, ARTIMES exemplifies how synergistic integration of AI and clinical expertise can surmount longstanding challenges in tumor measurement. By transitioning from simplistic unidimensional diameter metrics to comprehensive volumetric analysis, this technology brings unprecedented clarity and confidence to oncological decision-making. As this AI model becomes more widely disseminated and refined, it heralds a paradigm shift in cancer treatment evaluation, with rippling benefits for patients, clinicians, and research worldwide.

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Subject of Research: People

Article Title: Development and validation of artificial intelligence-assisted volumetric response criteria in pleural mesothelioma (ARTIMES): a retrospective, multicohort, multicentre study

News Publication Date: 17-Jun-2026

Web References:

• The Lancet Oncology: http://dx.doi.org/10.1016/S1470-2045(26)00084-7
• Mesothelioma AI model (ARTIMES): https://huggingface.co/nki-radiology/ARTIMES
• EU Medical Devices Regulation: https://health.ec.europa.eu/medical-devices-sector/new-regulations_en

References:
Groot Lipman K, Burgers S, et al. Development and validation of artificial intelligence-assisted volumetric response criteria in pleural mesothelioma (ARTIMES): a retrospective, multicohort, multicentre study. The Lancet Oncology, 2026.

Image Credits: ©Netherlands Cancer Institute

Keywords: Cancer treatments, Artificial intelligence, Imaging analysis, Pleural mesothelioma, Tumor volumetrics, Clinical trials, Precision oncology

In a remarkable leap forward in food safety technology, researchers at the University of California, Berkeley, have developed a groundbreaking “electronic nose” capable of pinpointing spoiled food and detecting allergens with unprecedented accuracy. This innovative device combines a miniature array of gas sensors with sophisticated machine learning algorithms to outperform the human nose, which, while remarkably sensitive, often fails to detect early signs of food spoilage and allergen presence. The implications of this technology are vast, promising to revolutionize how consumers, retailers, and food producers monitor food freshness and safety.

The team’s new creation is composed of 16 ultra-sensitive gas sensors, each responding uniquely to specific volatile compounds released by different foods. These sensors act much like digital taste buds, capable of identifying complex chemical signatures through changes in electrical signals. By mapping these responses to known gas fingerprints, the electronic nose can distinguish between fresh and spoiled food items, along with categorizing various allergens including walnuts, peanuts, and other nuts known to trigger severe allergic reactions.

What makes this achievement especially notable is the integration of carbon nanotube technology in the sensors. Unlike traditional metal oxide sensors, carbon nanotubes offer extraordinary surface area relative to their size and maintain sensitivity at room temperature. This is crucial because many sensing materials degrade or require heating to function effectively, limiting their applications. The Berkeley team leveraged the thin, nanometer-scale layers of carbon nanotubes to create a sensor surface that is both highly selective and stable under everyday environmental conditions.

Central to the power of this electronic nose is machine learning. Lead author Carla Bassil developed models trained to interpret the nuanced electrical patterns emanating from the sensor array when exposed to gases released by seven different foods, including strawberry, blueberry, banana, walnuts, hazelnuts, cashews, and peanuts. The algorithm also discerns the freshness of common perishables such as raw chicken, milk, and eggs by detecting subtle changes in their gaseous emissions over time, reflecting degrees of spoilage after 24 and 48 hours at room temperature.

A key challenge in creating an effective gas sensor lies in the complexity of food odors, which consist of many overlapping volatile organic compounds. This electronic nose overcomes this hurdle by relying on the relative selectivity of each sensor and the pattern recognition capabilities of machine learning, enabling the system to sort through these chemical signatures and recognize distinct gas “fingerprints.” The result is a highly sensitive, objective, and scalable detection method that could transform food safety practices.

The development of multiplexed sensor arrays has historically been complex, with issues arising from manufacturing difficulties and material limitations. However, by utilizing a fabrication process known as drop casting, the team managed to deposit different sensing films onto a single chip in a single step. This scalability is pivotal for real-world adoption, allowing potential mass production of these chips at a reasonable cost and size suitable for use in household appliances or portable devices.

In terms of applications, the researchers envision integration within “smart” kitchen appliances such as refrigerators equipped with wireless connectivity and app-based interfaces. Imagine a refrigerator that could alert you via your smartphone, warning that your broccoli is nearing spoilage or that your chicken is at its last safe day for consumption. Such timely notifications could drastically reduce food waste and prevent foodborne illness.

The sensitivity of the device is particularly striking. The electronic nose demonstrated the ability to detect as little as 0.05 grams of walnut, which corresponds to approximately one hundredth of a shelled nut. This level of detection sensitivity is crucial not only for spoilage monitoring but also for allergen detection, where even trace amounts can have life-threatening consequences for sensitive individuals.

Although these findings are promising, challenges remain. For instance, the device’s performance amidst complex food environments—such as detecting walnuts hidden in salad or cakes, or isolating spoilage gases when multiple food items are stored together—is yet to be rigorously tested. Future developments will focus on enhancing sensitivity, reducing false positives, and ensuring reliability in real-world, cluttered atmospheres.

Furthermore, a portable version of the electronic nose, operable via an iPhone application, has been developed, offering exciting possibilities for on-the-go freshness and allergen detection. Such a compact and user-friendly device empowers consumers to make informed decisions at grocery stores, restaurants, and home kitchens, fostering safer, healthier food habits.

The senior author of the study, Ali Javey, emphasizes the importance of combining novel nanomaterials with advanced machine learning to solve complex sensing problems. This convergence of disciplines paves the way for a new era of digital olfaction technologies that are not only sensitive and selective but also accessible and affordable for everyday use.

Published in the journal Science Advances on June 17, 2026, this study marks a significant milestone in the fields of applied sciences and engineering, with a particular emphasis on food science and sensor technology. Supported by funding from the U.S. Department of Energy and the National Science Foundation, the research team comprises experts from UC Berkeley and collaborators from KAIST in South Korea.

As this technology continues to mature, it holds the promise of making kitchens safer and reducing the global burden of foodborne illnesses. By translating the chemical complexity of food odors into digital data streams, the electronic nose could soon become an indispensable tool for consumers worldwide, enhancing food quality and saving lives.

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Subject of Research: Not applicable

Article Title: Scalable multiplexed machine learning gas sensor chips for food classification

News Publication Date: 17-Jun-2026

Web References: http://dx.doi.org/10.1126/sciadv.aec7965

References:

Bassil, C., Lee, K., Liao, X., Krishnan, D., Zhan, Y., Wijaya, T. J., Hester, E., Kim, M., Kim, I.-D., Park, I., & Javey, A. (2026). Scalable multiplexed machine learning gas sensor chips for food classification. Science Advances. https://doi.org/10.1126/sciadv.aec7965

Image Credits: Brandon Sánchez-Mejia/UC Berkeley

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Keywords

Food science, Sensors, Emission detectors, Electronic nose, Machine learning, Carbon nanotubes, Food safety, Food spoilage detection, Allergen detection, Smart appliances, Gas sensors, Nanotechnology

Once Russia invaded Ukraine, it was buried under a pile of international sanctions. Furthermore, as the years of

There’s an urgent need to study how artificial intelligence makes ethical choices because it soon will make many such decisions in our stead, says computer scientist Iyad Rahwan

In a remarkable leap forward for medical technology, a team of researchers led by Zou, X., Zhang, J., and Chan, C.P.Y. have unveiled a groundbreaking advancement in haptic feedback systems designed specifically for endoscopic teleoperation and remote palpation. Detailed in their recent publication in npj Flexible Electronics, this innovative approach employs dense elastomeric liquid metal microcoil-based interfaces to transform how surgeons and medical professionals interact with remote internal environments. The intersection of flexible electronics, smart materials, and precision control opens new vistas for minimally invasive procedures and telemedicine.

At the heart of this breakthrough lies the integration of liquid metal microcoils encapsulated within elastomeric substrates, forming a highly sensitive yet mechanically robust haptic interface. These microcoils are capable of generating precise electromagnetic fields which can be finely tuned to mimic various tactile sensations encountered during palpation of organic tissues. The elastomeric medium ensures that the device remains soft and compliant, paralleling the flexibility of biological tissues, a critical characteristic for interfacing with delicate anatomical structures during endoscopic maneuvers.

Traditionally, the lack of tactile feedback in endoscopic teleoperation has been a significant barrier limiting the surgeon’s ability to perform nuanced examinations and manipulations remotely. Without real-time haptic cues, operators must rely heavily on visual data, often constraining them to less precise interventions. The dense microcoil architecture introduced here circumvents this limitation, delivering graded force feedback that enhances situational awareness and operational dexterity. This certainly augurs well for improved patient outcomes and expanded applicability of robotic-assisted minimally invasive surgeries.

The engineering challenge of embedding electrically conductive liquid metals within stretchable matrices is particularly formidable due to factors such as metal leakage, durability under repeated mechanical deformation, and signal fidelity. By leveraging advanced microfabrication techniques, the research team has managed to arrange multiple liquid metal microcoils in closely packed networks, maintaining electrical continuity while allowing for substantial skin-like elasticity. The resulting elastomeric platform simultaneously exhibits excellent mechanical endurance and electromagnetic responsiveness, making it highly suitable for continuous use in clinical settings.

Further advancing the technology, the microcoils’ electromagnetic emissions can be dynamically modulated to emulate various textures and firmness levels characteristic of different tissue types. Thus, the interface can replicate sensations ranging from firm nodules to soft cystic formations, invaluable during diagnostic remote palpation. This capacity to differentiate subtle differences in tissue compliance could revolutionize tele-diagnostic methodologies and enable the virtual presence of expert clinicians regardless of their physical location.

Additionally, the researchers demonstrated that the compactness and biocompatibility of their design allow seamless integration with existing endoscopic instruments. Since these haptic interfaces are fabricated on flexible substrates that conform to complex geometries, retrofitting current devices for enhanced tactile perception becomes feasible without significant hardware overhaul. This compatibility introduces a pragmatic pathway for rapid clinical adoption and accelerates the translation from lab prototype to bedside tool.

Significantly, the device’s exceptional spatial resolution stems from the microcoil density that surpasses conventional haptic arrays by an order of magnitude. This density enables highly localized force feedback, empowering surgeons to discern minute anatomical features and manipulative pressures, akin to natural touch. Enhanced spatial acuity not only minimizes inadvertent tissue damage but also refines the precision of surgical gestures performed remotely, a vital enhancement for robotic endoscopy.

The durability tests conducted reveal that the elastomeric microcoil arrays withstand thousands of mechanical cycles without degradation in electrical or tactile function. This endurance is crucial for clinical reliability, as surgical procedures often extend over hours and necessitate consistent tactile feedback. The elastomer’s resistance to bodily fluids and sterilization processes further underscores its practicality for repeated use within sterile operating room environments.

Beyond endoscopy, the principles underpinning this technology have vast implications across medical robotics and wearable health devices. From remote rehabilitation to advanced prosthetics, densely packed liquid metal microcoil arrays embedded in soft elastomers could deliver highly intuitive haptic sensations that bridge the gap between man and machine. Their ability to accurately simulate touch and force presents a paradigm shift in direct human-machine interaction technologies.

Expanding the scope of teleoperation, this innovation paves the way for real-time remote palpation performed by specialists thousands of miles away. The combination of high-fidelity haptic feedback with tactile sensation mapping can enable remote diagnosis with unprecedented accuracy. This is particularly transformative for geographically isolated healthcare facilities, where access to expert practitioners is limited, enhancing healthcare equity on a global scale.

Further insights from the study highlight that the electromagnetic microcoils can operate efficiently with low power consumption, vital for portable and battery-operated surgical tools. Efficient energy usage not only extends operational time but reduces heat generation, an important factor in preserving tissue safety and user comfort. The smart material approach balances functionality with operational pragmatism, addressing key limitations in current haptic technologies.

From a materials science perspective, the marriage of liquid metal and elastomer illustrates a masterclass in multifunctional composite design. The liquid metal component, inherently conductive and deformable, harmonizes with the elastomer’s resilience to achieve electro-mechanical transduction without compromising softness. This synergy redefines the possibilities of flexible electronics by providing a multi-physics platform capable of sensing, actuating, and adapting simultaneously.

Another notable aspect lies in the scalability of the fabrication method. This enables mass production of the microcoil arrays with reproducible quality and design flexibility tailored to varied endoscopic devices. Scalability is essential for widespread clinical deployment and cost-effectiveness. By providing a reliable route for industrial manufacturing, the researchers have addressed one of the critical barriers to translating cutting-edge soft electronics into real-world medical accessories.

In conclusion, Zou et al.’s pioneering contribution in developing dense elastomeric liquid metal microcoil-based haptic interfaces represents a landmark achievement with far-reaching consequences for telemedicine and minimally invasive surgery. Their meticulous integration of fluid mechanics, materials engineering, and electromagnetic control offers a new standard for tactile interaction in remote medical procedures. This breakthrough invigorates the field of flexible electronics and sets a blueprint for next-generation human-machine interfaces enhancing surgical precision and patient care worldwide.

As the medical community faces the continuing challenge of providing expert care in increasingly complex and constrained environments, innovations such as these provide a beacon for safely extending human touch beyond physical boundaries. Dense elastomeric liquid metal microcoil haptics are poised to redefine surgical telepresence, promising a future where distance no longer limits the healing power of human touch.

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Subject of Research: Dense elastomeric liquid metal microcoil-based haptic interfaces for improving tactile feedback in endoscopic teleoperation and remote palpation.

Article Title: Dense elastomeric liquid metal microcoil-based haptic interfaces for endoscopic teleoperation and remote palpation.

Article References:
Zou, X., Zhang, J., Chan, C.P.Y. et al. Dense elastomeric liquid metal microcoil-based haptic interfaces for endoscopic teleoperation and remote palpation. npj Flex Electron (2026). https://doi.org/10.1038/s41528-026-00608-7

Image Credits: AI Generated

Physical therapy for stroke survivors has long relied on the physical presence and hands-on expertise of therapists walking alongside their patients. Now, a remarkable technological leap is reshaping this dynamic. Researchers at Northwestern University in collaboration with Shirley Ryan AbilityLab have unveiled a revolutionary rehabilitation system that forges a direct, real-time link between therapists and patients via robotic exoskeletons. This novel system, known as Therapist-Exoskeleton-Patient Interaction (TEPI), ushers in a new era of highly adaptive, personalized post-stroke gait therapy.

At the heart of TEPI lies a pair of lower-limb exoskeletons worn by the therapist and the stroke survivor, virtually coupled at the hips and knees through a sophisticated control interface. The virtual connection mimics the behavior of springs and shock absorbers, dynamically transmitting forces and movements between the two exoskeletons. This bidirectional, compliant coupling enables therapists to intuitively influence patient gait patterns in real time, offering a level of interaction that is vastly more nuanced than conventional robotic or manual therapies.

Traditional stroke rehabilitation often involves therapists physically assisting patients through limited aspects of movement, constrained by human strength and availability. In contrast, TEPI leverages robotics to augment the therapist’s capacity, delivering whole-body gait training that adjusts fluidly to the patient’s evolving performance. The hands-on adaptability of therapists is thus preserved but enhanced with robotic precision and scalability, facilitating more comprehensive, sustained walking practice without requiring multiple caregivers.

The core engineering breakthrough enabling TEPI is a control framework that models the mechanical interaction between therapist and patient exoskeletons as a coupled spring-damper system. This design imparts compliance and responsiveness, harmonizing the biomechanical inputs from both participants while preventing unnatural or jarring forces. By embedding this interactive virtual linkage, TEPI provides continuous haptic feedback to therapists, allowing them to modulate support, resistance, and corrective assistance in tune with patient movement intentions.

In a recent clinical evaluation published in Science Robotics, TEPI demonstrated superior outcomes compared to conventional therapist-guided treadmill training. Stroke survivors using the system achieved significantly greater joint range of motion, executed longer and higher stepping patterns, and maintained muscle activation at levels comparable to or exceeding standard therapy regimes. Importantly, patient motivation and enjoyment remained consistently high, underlining the system’s potential to foster engagement and adherence during demanding rehabilitation processes.

The TEPI platform addresses prominent limitations of current rehabilitation exoskeletons, many of which rely on rigid, preprogrammed gait cycles that lack real-time adaptability. By contrast, TEPI integrates therapist expertise directly into the robotic control loop, enabling instantaneous behavioral adjustments that match the patient’s unique recovery trajectory. This synergy of human judgment and machine assistance embodies a new paradigm in rehabilitation robotics, where technology serves to amplify, not replace, therapeutic skill.

José L. Pons, the project’s visionary leader and a professor at Northwestern, emphasizes that TEPI’s promise lies in its ability to unify the therapeutic closeness of manual training with the replicability and intensity of robotic interventions. Such hybrid systems are poised to transform stroke recovery protocols by bridging gaps between efficacy, accessibility, and individualized care. They not only alleviate therapist physical burden but also enhance the precision and personalization of rehabilitation exercises.

The development team included multidisciplinary experts spanning mechanical engineering, physical medicine, biomedical engineering, and robotics, fostering innovation at the interface of human-machine interaction and clinical practice. This collaboration ensured that TEPI’s control algorithms and mechanical design were continuously refined based on real-world clinical feedback, resulting in a system deeply attuned to both biomechanical and therapeutic demands.

Looking ahead, the research group plans to expand TEPI’s application beyond treadmill walking, exploring its integration into functionally critical daily activities such as overground ambulation, stair climbing, and sit-to-stand transitions. Longitudinal studies are also underway to assess the benefits of repeated therapy sessions over extended recovery periods. Additionally, efforts are progressing to miniaturize and streamline the technology for potential deployment in home environments, promising scalable remote rehabilitation solutions that can transcend geographic and logistical barriers.

TEPI’s pioneering approach was made possible by funding from the U.S. National Science Foundation’s National Robotics Initiative, reflecting the strategic importance of robotics in advancing healthcare outcomes. By embedding robotic exoskeletons within a responsive therapeutic framework, the system exemplifies how cutting-edge engineering can catalyze transformative changes in the treatment of neurological impairments.

This breakthrough not only advances rehabilitation science but also redefines the human role in therapy. TEPI empowers therapists to become active collaborators with their patients through enhanced, augmented physical connection, promoting a more effective and responsive recovery journey for stroke survivors. It stands as a shining example of innovation that is as empathetic as it is technological — a true step forward in restoring mobility and quality of life.

Subject of Research:
Robotic exoskeleton-assisted gait therapy for stroke rehabilitation through therapist-patient real-time interaction.

Article Title:
Therapist-exoskeleton-patient interaction for gait therapy

News Publication Date:
17-Jun-2026

Web References:
http://dx.doi.org/10.1126/scirobotics.adz9628

Image Credits:
Shirley Ryan AbilityLab

Keywords

Robotic exoskeletons, Robotics, Robotic gaits, Robotic walking, Physical therapy, Physical rehabilitation, Brain damage, Brain injuries, Brain ischemia, Neuromuscular diseases, Neurological disorders

Qualcomm CEO Cristiano Amon says AI agents could replace apps as the main way people complete tasks on phones, PCs, cars, and wearables.

Each year, nearly 800,000 Americans survive a stroke, embarking on a challenging journey toward recovery marked by relearning foundational motor skills such as walking. The aftermath of a stroke often results in muscle weakness, impaired coordination, and compromised leg control, making even the simplest of movements daunting. Traditional rehabilitation protocols heavily depend on intensive, therapist-led sessions where patients progressively regain mobility, independence, and confidence. However, the physical demands on therapists and the limitations of conventional methods necessitate innovative approaches that can enhance therapy effectiveness while mitigating therapist fatigue.

A groundbreaking study led by researchers from Shirley Ryan AbilityLab and Northwestern University introduces a novel paradigm that integrates lower-limb exoskeletons to enhance therapist-patient interaction during functional gait training. Published in the prestigious journal Science Robotics, the study unveils a transformative approach termed Therapist-Exoskeleton-Patient Interaction (TEPI). This system connects a therapist and a stroke survivor physically via complementary lower-limb exoskeletons, linked through virtual spring-damper elements at the hips and knees. This design facilitates real-time bidirectional physical interaction, where the therapist’s movements dynamically guide and respond to the patient’s gait by modulating forces through the exoskeleton interface.

Conventional physical therapy for gait rehabilitation involves therapists providing hands-on corrective assistance to patients, focusing typically on isolated aspects of walking mechanics due to their ability to assist only a limited number of joints or movements simultaneously. Complex, whole-body retraining often demands multiple therapists, posing logistical and physical constraints. While robotic rehabilitation devices can increase therapy intensity by enabling prolonged walking practice, most systems operate on preprogrammed, fixed movement trajectories that lack the flexibility to adapt instantaneously to patient performance or allow meaningful therapist input. This gap limits personalized care and the nuanced adaptability essential in neurorehabilitation.

TEPI fundamentally redefines this intervention landscape by leveraging the dexterous control capabilities of therapists in conjunction with robotic consistency and scalability. Through the exoskeleton-mediated connection, therapists can impose finely tuned guidance, resistance, or assistance, tailored in real time to the patient’s biomechanical status and response patterns. This synergy allows the therapy to encompass intricate whole-body dynamics within a single therapeutic session, eliminating the need for multiple practitioners in many cases. Moreover, the system’s responsiveness facilitates continuous adjustment throughout the gait cycle, enhancing motor learning through immediate feedback.

During the pilot evaluation involving eight stroke survivors, TEPI was contrasted with conventional therapist-guided treadmill walking sessions. Quantitative motion analysis revealed that TEPI training elicited significantly greater joint range of motion, augmented step length, and increased step height compared to standard therapy. Electromyographic assessments indicated comparable muscle activation patterns between the two modalities, affirming that TEPI does not compromise neuromuscular engagement while augmenting kinematic outcomes. Subjective evaluations reflected high patient motivation and enjoyment, underscoring the system’s potential to enhance adherence and therapeutic enthusiasm.

The TEPI framework also addresses a critical occupational health issue: therapist fatigue and injury risk associated with manual gait rehabilitation. By externalizing some physical effort to the exoskeleton-mediated interaction, therapists can guide patient movements more sustainably and ergonomically. This innovative method not only preserves the expertise of hands-on care but also reduces the biomechanical strain on providers, potentially extending the longevity and well-being of rehabilitation professionals.

From an engineering perspective, the system employs a sophisticated control architecture that harmonizes the exoskeleton’s stiffness and damping properties with the therapist’s voluntary movements. The virtual spring-damper coupling dynamically modulates mechanical impedance at key lower-limb joints, enabling a naturalistic yet controlled interaction paradigm. This technical advancement permits the system to act almost like an intelligent mechanical extension of the therapist’s own legs, enabling seamless transfer of movement intent and corrective forces.

Looking ahead, the researchers plan to expand the TEPI model beyond treadmill walking to include overground ambulation, stair navigation, and sit-to-stand transitions—functional tasks that more holistically represent daily living activities. The capacity to apply this approach across varied locomotor challenges promises to broaden its clinical applicability and deepen its therapeutic impact. Furthermore, scaling the technology into more accessible and user-friendly configurations could facilitate home-based rehabilitation, supporting remote care delivery and extending therapeutic supervision beyond clinical settings.

Such remote rehabilitation capability aligns with emerging trends in teletherapy and digital health, addressing barriers to access and continuity of care, especially for patients in underserved regions or with mobility limitations. By integrating sensor networks, adaptive control algorithms, and connectivity protocols, future iterations of TEPI could enable therapists to remotely guide patients through personalized gait training regimens with real-time haptic feedback and data monitoring.

This pioneering work symbolizes a major step forward in merging human expertise and robotic technology to optimize post-stroke rehabilitation. By synergizing the adaptability and intuitive understanding of therapists with the precision and endurance of robotic exoskeletons, TEPI opens a new frontier for restoring functional mobility—ultimately enhancing quality of life for millions of stroke survivors.

The collaborative research team behind this innovative study includes José L. Pons, PhD, scientific chair at Shirley Ryan AbilityLab and professor at Northwestern University; postdoctoral researchers Lorenzo Vianello, PhD, and Matthew R. Short, PhD; and co-first author Emek Barış Küçüktabak, PhD. Their collective expertise spans neurorehabilitation, robotics, and biomechanical engineering, driving the development of this cutting-edge, translational technology.

As the field advances, the TEPI approach may serve as a blueprint for next-generation rehabilitation robotics, where human-robot collaboration is optimized to support recovery from complex neurological impairments. The blend of immersive therapist control, real-time adaptability, and patient-centered design embodied by TEPI heralds a new era in rehabilitation science—one poised to transform care standards and patient outcomes.

Subject of Research: People
Article Title: Therapist-exoskeleton-patient interaction for gait therapy
News Publication Date: 17-Jun-2026
Web References: DOI link
Image Credits: Shirley Ryan AbilityLab
Keywords: Health care, Diseases and disorders, Biomedical engineering