Computers Math

Infections caused by fungi, such as Candida albicans, pose a significant global health risk due to their resistance to existing treatments, so much so that the World Health Organization has highlighted this as a priority issue.
Although nanomaterials show promise as antifungal agents, current iterations lack the potency and specificity needed for quick and targeted treatment, leading to prolonged treatment times and potential off-target effects and drug resistance.
Now, in a groundbreaking development with far-reaching implications for global health, a team of researchers jointly led by Hyun (Michel) Koo of the University of Pennsylvania School of Dental Medicine and Edward Steager of Penn’s School of Engineering and Applied Science has created a microrobotic system capable of rapid, targeted elimination of fungal pathogens.
“Candidae forms tenacious biofilm infections that are particularly hard to treat,” Koo says. “Current antifungal therapies lack the potency and specificity required to quickly and effectively eliminate these pathogens, so this collaboration draws from our clinical knowledge and combines Ed’s team and their robotic expertise to offer a new approach.”
The team of researchers is a part of Penn Dental’s Center for Innovation & Precision Dentistry, an initiative that leverages engineering and computational approaches to uncover new knowledge for disease mitigation and advance oral and craniofacial health care innovation.
For this paper, published in Advanced Materials, the researchers capitalized on recent advancements in catalytic nanoparticles, known as nanozymes, and they built miniature robotic systems that could accurately target and quickly destroy fungal cells. They achieved this by using electromagnetic fields to control the shape and movements of these nanozyme microrobots with great precision.

“The methods we use to control the nanoparticles in this study are magnetic, which allows us to direct them to the exact infection location,” Steager says. “We use iron oxide nanoparticles, which have another important property, namely that they’re catalytic.”
Steager’s team developed the motion, velocity, and formations of nanozymes, which resulted in enhanced catalytic activity, much like the enzyme peroxidase, which helps break down hydrogen peroxide into water and oxygen. This directly allows the generation of high amounts of reactive oxygen species (ROS), compounds that have proven biofilm-destroying properties, at the site of infection.
However, the truly pioneering element of these nanozyme assemblies was an unexpected discovery: their strong binding affinity to fungal cells. This feature enables a localized accumulation of nanozymes precisely where the fungi reside and, consequently, targeted ROS generation.
“Our nanozyme assemblies show an incredible attraction to fungal cells, particularly when compared to human cells,” Steager says. “This specific binding interaction paves the way for a potent and concentrated antifungal effect without affecting other uninfected areas.”
Coupled with the nanozyme’s inherent maneuverability, this results in a potent antifungal effect, demonstrating the rapid eradication of fungal cells within an unprecedented 10-minute window.
Looking forward, the team sees the potential of this unique nanozyme-based robotics approach, as they incorporate new methods to automate control and delivery of nanozymes. The promise it holds for antifungal therapy is just the beginning. Its precise targeting, rapid action suggest potential for treating other types of stubborn infections.
“We’ve uncovered a powerful tool in the fight against pathogenic fungal infections,” Koo says. “What we have achieved here is a significant leap forward, but it’s also just the first step. The magnetic and catalytic properties combined with unexpected binding specificity to fungi open exciting opportunities for an automated ‘target-bind-and-kill’ antifungal mechanism. We are eager to delve deeper and unlock its full potential.”
This robotics approach opens up a new frontier in the fight against fungal infections and marks a pivotal point in antifungal therapy. With a new tool in their arsenal, medical and dental professionals are closer than ever to effectively combating these difficult pathogens. More

The first protein-based nano-computing agent that functions as a circuit has been created by Penn State researchers. The milestone puts them one step closer to developing next-generation cell-based therapies to treat diseases like diabetes and cancer.
Traditional synthetic biology approaches for cell-based therapies, such as ones that destroy cancer cells or encourage tissue regeneration after injury, rely on the expression or suppression of proteins that produce a desired action within a cell. This approach can take time (for proteins to be expressed and degrade) and cost cellular energy in the process. A team of Penn State College of Medicine and Huck Institutes of the Life Sciences researchers are taking a different approach.
“We’re engineering proteins that directly produce a desired action,” said Nikolay Dokholyan, G. Thomas Passananti Professor and vice chair for research in the Department of Pharmacology. “Our protein-based devices or nano-computing agents respond directly to stimuli (inputs) and then produce a desired action (outputs).”
In a study published in Science Advances today (May 26) Dokholyan and bioinformatics and genomics doctoral student Jiaxing Chen describe their approach to creating their nano-computing agent. They engineered a target protein by integrating two sensor domains, or areas that respond to stimuli. In this case, the target protein responds to light and a drug called rapamycin by adjusting its orientation, or position in space.
To test their design, the team introduced their engineered protein into live cells in culture. By exposing the cultured cells to the stimuli, they used equipment to measure changes in cellular orientation after cells were exposed to the sensor domains’ stimuli.
Previously, their nano-computing agent required two inputs to produce one output. Now, Chen says there are two possible outputs and the output depends on which order the inputs are received. If rapamycin is detected first, followed by light, the cell will adopt one angle of cell orientation, but if the stimuli are received in a reverse order, then the cell adopts a different orientation angle. Chen says this experimental proof-of-concept opens the door for the development of more complex nano-computing agents.
“Theoretically, the more inputs you embed into a nano-computing agent, the more potential outcomes that could result from different combinations,” Chen said. “Potential inputs could include physical or chemical stimuli and outputs could include changes in cellular behaviors, such as cell direction, migration, modifying gene expression and immune cell cytotoxicity against cancer cells.”
The team plans to further develop their nano-computing agents and experiment with different applications of the technology. Dokholyan, a researcher with Penn State Cancer Institute and Penn State Neuroscience Institute, said their concept could someday form the basis of the next-generation cell-based therapies for various diseases, such as autoimmune diseases, viral infections, diabetes, nerve injury and cancer.
Yashavantha Vishweshwaraiah, Richard Mailman and Erdem Tabdanov of Penn State College of Medicine also contributed to this research. The authors declare no conflicts of interest.
This work was funded by the National Institutes of Health (grant 1R35GM134864) and the Passan Foundation. More

Miniaturization is progressing rapidly in just any field and the trend towards the creation of ever smaller units is also prevalent in the world of robot technology. In the future, minuscule robots used in medical and pharmaceutical applications might be able to transport medication to targeted sites in the body. Statistical physics can contribute to the foundations for the development of such technologies. A team of researchers at Johannes Gutenberg University Mainz (JGU) has now taken a new approach to the issue by analyzing a group of robots and how they behave as collectives of motile units based on the model of active Brownian particles. The team’s findings demonstrating that there may be an alternative route to realize programmable active matter have been published in Science Advances.
Collectives of robotic units could solve tasks that a single machine can not solve on its own
Researchers are looking for new ways to perform tasks on the micro- and nanoscale that are otherwise difficult to realize, particularly as the miniaturization of devices and components is beginning to reach physical limits. One new option being considered is the use of collectives of robotic units in place of a single robot to complete a task. “The task-solving capabilities of one microrobot are limited due to its small size,” said Professor Thomas Speck, who headed the study at Mainz University. “But a collective of such robots working together may well be able to carry out complex assignments with considerable success.” Statistical physics becomes relevant here in that it analyzes models to describe how such collective behavior may emerge from interactions, comparable to bird behavior when they flock together.
The research team studied the collective behavior of a number of small, commercially available robots. These so-called walkers are propelled through internal vibrations transmitted to two rows of tiny legs. Because the length, shape, and stiffness of the legs differ slightly from robot to robot, they follow circular orbits with a radius that is specific to each individual walker. Looking and moving like little beetles, these robots have an elliptical form and are sent off in a new direction when they happen to collide with each other.
“Our aim was to examine and describe the collective behavior of these robots and determine whether it might be possible to derive potential uses from this,” added Frank Siebers, lead author of the paper. “At the same time, we as physicists were also interested in the phenomena per se.” The researchers were able to observe two effects when the collective of robots has variations in terms of their orbits, i.e., in a group showing greater diversity. Firstly, the walkers required less time to explore the space they were placed in. And secondly, when contained within an enclosed space, they began to undergo self-organized sorting. Depending on their orbital radius, the robots either accumulated at the confining wall or began to gather within the interior of the space.
Statistical physics provides insights into the behavior of collectives
“It would be possible to exploit this kind of activity to get robots to transport a load and to interact with that load, for example. The speed with which they would be able to traverse spaces would increase, meaning that the load would be delivered sooner,” said Professor Thomas Speck, outlining one potential application. “Statistical physics can help to uncover new strategies that may be utilized by collectives of robots.”
The field of active matter models and robotics covers many realms of the living and the nonliving world in which collective behavior or collective movement can be observed, one prominent example being the way that flocks of birds move in unison. “What we have done here is to apply the theory underlying our understanding of clustering and swarming to robotic systems,” said Frank Siebers of JGU.
The research was funded under the aegis of the Collaborative Research Center/TRR 146 on Multiscale Simulation Methods for Soft Matter Systems, a cooperative project involving Johannes Gutenberg University Mainz, TU Darmstadt, and the Max Planck Institute for Polymer Research. The researchers based their conclusions on the outcome of their experiments as well as on model computations performed on JGU’s supercomputer MOGON II. Principal investigator Professor Thomas Speck held a professorship at the JGU Institute of Physics from 2013 to 2022. He is now head of the Institute for Theoretical Physics IV of the University of Stuttgart. More

Philosophers and legal scholars have explored significant aspects of the moral and legal status of robots, with some advocating for giving robots rights. As robots assume more roles in the world, a new analysis reviewed research on robot rights, concluding that granting rights to robots is a bad idea. Instead, the article looks to Confucianism to offer an alternative.
The analysis, by a researcher at Carnegie Mellon University (CMU), appears in Communications of the ACM, published by the Association for Computing Machinery.
“People are worried about the risks of granting rights to robots,” notes Tae Wan Kim, Associate Professor of Business Ethics at CMU’s Tepper School of Business, who conducted the analysis. “Granting rights is not the only way to address the moral status of robots: Envisioning robots as rites bearers — not a rights bearers — could work better.”
Although many believe that respecting robots should lead to granting them rights, Kim argues for a different approach. Confucianism, an ancient Chinese belief system, focuses on the social value of achieving harmony; individuals are made distinctively human by their ability to conceive of interests not purely in terms of personal self-interest, but in terms that include a relational and a communal self. This, in turn, requires a unique perspective on rites, with people enhancing themselves morally by participating in proper rituals.
When considering robots, Kim suggests that the Confucian alternative of assigning rites — or what he calls role obligations — to robots is more appropriate than giving robots rights. The concept of rights is often adversarial and competitive, and potential conflict between humans and robots is concerning.
“Assigning role obligations to robots encourages teamwork, which triggers an understanding that fulfilling those obligations should be done harmoniously,” explains Kim. “Artificial intelligence (AI) imitates human intelligence, so for robots to develop as rites bearers, they must be powered by a type of AI that can imitate humans’ capacity to recognize and execute team activities — and a machine can learn that ability in various ways.”
Kim acknowledges that some will question why robots should be treated respectfully in the first place. “To the extent that we make robots in our image, if we don’t treat them well, as entities capable of participating in rites, we degrade ourselves,” he suggests.
Various non-natural entities — such as corporations — are considered people and even assume some Constitutional rights. In addition, humans are not the only species with moral and legal status; in most developed societies, moral and legal considerations preclude researchers from gratuitously using animals for lab experiments. More

Using an artificial intelligence algorithm, researchers at MIT and McMaster University have identified a new antibiotic that can kill a type of bacteria that is responsible for many drug-resistant infections.
If developed for use in patients, the drug could help to combat Acinetobacter baumannii, a species of bacteria that is often found in hospitals and can lead to pneumonia, meningitis, and other serious infections. The microbe is also a leading cause of infections in wounded soldiers in Iraq and Afghanistan.
“Acinetobacter can survive on hospital doorknobs and equipment for long periods of time, and it can take up antibiotic resistance genes from its environment. It’s really common now to find A. baumannii isolates that are resistant to nearly every antibiotic,” says Jonathan Stokes, a former MIT postdoc who is now an assistant professor of biochemistry and biomedical sciences at McMaster University.
The researchers identified the new drug from a library of nearly 7,000 potential drug compounds using a machine-learning model that they trained to evaluate whether a chemical compound will inhibit the growth of A. baumannii.
“This finding further supports the premise that AI can significantly accelerate and expand our search for novel antibiotics,” says James Collins, the Termeer Professor of Medical Engineering and Science in MIT’s Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering. “I’m excited that this work shows that we can use AI to help combat problematic pathogens such as A. baumannii.”
Collins and Stokes are the senior authors of the new study, which appears today in Nature Chemical Biology. The paper’s lead authors are McMaster University graduate students Gary Liu and Denise Catacutan and recent McMaster graduate Khushi Rathod.

Drug discovery
Over the past several decades, many pathogenic bacteria have become increasingly resistant to existing antibiotics, while very few new antibiotics have been developed.
Several years ago, Collins, Stokes, and MIT Professor Regina Barzilay (who is also an author on the new study), set out to combat this growing problem by using machine learning, a type of artificial intelligence that can learn to recognize patterns in vast amounts of data. Collins and Barzilay, who co-direct MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health, hoped this approach could be used to identify new antibiotics whose chemical structures are different from any existing drugs.
In their initial demonstration, the researchers trained a machine-learning algorithm to identify chemical structures that could inhibit growth of E. coli. In a screen of more than 100 million compounds, that algorithm yielded a molecule that the researchers called halicin, after the fictional artificial intelligence system from “2001: A Space Odyssey.” This molecule, they showed, could kill not only E. coli but several other bacterial species that are resistant to treatment.
“After that paper, when we showed that these machine-learning approaches can work well for complex antibiotic discovery tasks, we turned our attention to what I perceive to be public enemy No. 1 for multidrug-resistant bacterial infections, which is Acinetobacter,” Stokes says.

To obtain training data for their computational model, the researchers first exposed A. baumannii grown in a lab dish to about 7,500 different chemical compounds to see which ones could inhibit growth of the microbe. Then they fed the structure of each molecule into the model. They also told the model whether each structure could inhibit bacterial growth or not. This allowed the algorithm to learn chemical features associated with growth inhibition.
Once the model was trained, the researchers used it to analyze a set of 6,680 compounds it had not seen before, which came from the Drug Repurposing Hub at the Broad Institute. This analysis, which took less than two hours, yielded a few hundred top hits. Of these, the researchers chose 240 to test experimentally in the lab, focusing on compounds with structures that were different from those of existing antibiotics or molecules from the training data.
Those tests yielded nine antibiotics, including one that was very potent. This compound, which was originally explored as a potential diabetes drug, turned out to be extremely effective at killing A. baumannii but had no effect on other species of bacteria including Pseudomonas aeruginosa, Staphylococcus aureus, and carbapenem-resistant Enterobacteriaceae.
This “narrow spectrum” killing ability is a desirable feature for antibiotics because it minimizes the risk of bacteria rapidly spreading resistance against the drug. Another advantage is that the drug would likely spare the beneficial bacteria that live in the human gut and help to suppress opportunistic infections such as Clostridium difficile.
“Antibiotics often have to be administered systemically, and the last thing you want to do is cause significant dysbiosis and open up these already sick patients to secondary infections,” Stokes says.
A novel mechanism
In studies in mice, the researchers showed that the drug, which they named abaucin, could treat wound infections caused by A. baumannii. They also showed, in lab tests, that it works against a variety of drug-resistant A. baumannii strains isolated from human patients.
Further experiments revealed that the drug kills cells by interfering with a process known as lipoprotein trafficking, which cells use to transport proteins from the interior of the cell to the cell envelope. Specifically, the drug appears to inhibit LolE, a protein involved in this process.
All Gram-negative bacteria express this enzyme, so the researchers were surprised to find that abaucin is so selective in targeting A. baumannii. They hypothesize that slight differences in how A. baumannii performs this task might account for the drug’s selectivity.
“We haven’t finalized the experimental data acquisition yet, but we think it’s because A. baumannii does lipoprotein trafficking a little bit differently than other Gram-negative species. We believe that’s why we’re getting this narrow spectrum activity,” Stokes says.
Stokes’ lab is now working with other researchers at McMaster to optimize the medicinal properties of the compound, in hopes of developing it for eventual use in patients.
The researchers also plan to use their modeling approach to identify potential antibiotics for other types of drug-resistant infections, including those caused by Staphylococcus aureus and Pseudomonas aeruginosa. More

Modern cars and autonomous vehicles use millimeter wave (mmWave) radio frequencies to enable self-driving or assisted driving features that ensure the safety of passengers and pedestrians. This connectivity, however, can also expose them to potential cyberattacks.
To help improve the safety and security of autonomous vehicles, researchers from the lab of Dinesh Bharadia, an affiliate of the UC San Diego Qualcomm Institute (QI) and faculty member in the university’s Jacobs School of Engineering Department of Electrical and Computer Engineering, and colleagues from Northeastern University devised a novel algorithm designed to mimic an attacking device. The algorithm, described in the paper “mmSpoof: Resilient Spoofing of Automotive Millimeter-wave Radars using Reflect Array,” lets researchers identify areas for improvement in autonomous vehicle security.
“The invention of autonomous systems, like self-driving cars, was to enable the safety of humanity and prevent loss of life,” said Bharadia. “Such autonomous systems use sensors and sensing to deliver autonomy. Therefore, safety and security rely on achieving high-fidelity sensing information from sensors. Our team exposed a radar sensor vulnerability and developed a solution that autonomous cars should strongly consider.”
Defending Against Cyberattacks
Autonomous cars detect obstacles and other potential hazards by sending out radio waves and recording their reflections as they bounce off surrounding objects. By measuring the time it takes for the signal to return, as well as changes in its frequency, the car can detect the distance and speed of other vehicles on the road.
Like any wireless system, however, autonomous cars run the risk of cyberattacks. Attackers driving ahead of an autonomous unit can engage in “spoofing,” an activity that involves interfering with the vehicle’s return signal to trick it into registering an obstacle in its path. The vehicle may then brake suddenly, increasing the risk of an accident.

To address this potential chink in autonomous cars’ armor, Vennam and colleagues devised a novel algorithm designed to mimic a spoofing attack. Previous attempts to develop an attacking device to test cars’ resistance have had limited feasibility, either assuming the attacker can synchronize with the victim’s radar signal to launch an assault, or assuming both cars are physically connected by a cable.
In its new paper, presented by Vennam at the IEEE Symposium on Security and Privacy in San Francisco on May 22, the team describe a new technique that uses the victim vehicle’s radar against itself. By subtly changing the received signal’s parameters at “lightspeed” before reflecting it back, an attacker can disguise their sabotage and make it much harder for the vehicle to filter malicious behavior. All of this can be done “on the go” and in real-time without knowing anything about the victim’s radar.
“Automotive vehicles heavily rely on mmWave radars to enable real-time situational awareness and advanced features to promote safe driving,” said Vennam. “Securing these radars is of paramount importance. We — mmSpoof — uncovered a serious security issue with mmWave radars and demonstrated a robust attack. What’s alarming is that anyone can build the prototype using off-the-shelf hardware components.”
To counter this type of attack, Vennam suggests, researchers seeking to improve the safety of autonomous vehicles can use a high-resolution radar capable of capturing multiple reflections from a car to accurately identify the true reflection. Researchers might also create backup options for radar by incorporating cameras and “light detecting and ranging” (LiDAR), which records the time it takes for a laser pulse to hit an object and return to measure its surroundings, into their defense.
Alternately, the team presents mmSpoof as a means of preventing dangerous tailgating. By placing an mmSpoof device on the back of their car, drivers can trick a tailgating car into registering a decelerating car in front of them and activating the brakes.
In addition to Vennam and Bharadia, “mmSpoof: Resilient Spoofing of Automotive Millimeter-wave Radars using Reflect Array” was authored by Ish Kumar Jain, Kshitiz Bansal, Joshua Orozco and Puja Shukla of the UC San Diego Wireless Communication, Sensing and Networking Group and Jacobs School of Engineering, and Aanjhan Ranganathan of Northeastern University.
The research was partially supported by grants from the National Science Foundation. More

Cage structures made with nanoparticles could be a route toward making organized nanostructures with mixed materials, and researchers at the University of Michigan have shown how to achieve this through computer simulations.
The finding could open new avenues for photonic materials that manipulate light in ways that natural crystals can’t. It also showcased an unusual effect that the team is calling entropy compartmentalization.
“We are developing new ways to structure matter across scales, discovering the possibilities and what forces we can use,” said Sharon Glotzer, the Anthony C. Lembke Department Chair of Chemical Engineering, who led the study published today in Nature Chemistry. “Entropic forces can stabilize even more complex crystals than we thought.”
While entropy is often explained as disorder in a system, it more accurately reflects the system’s tendency to maximize its possible states. Often, this ends up as disorder in the colloquial sense. Oxygen molecules don’t huddle together in a corner — they spread out to fill a room. But if you put them in the right size box, they will naturally order themselves into a recognizable structure.
Nanoparticles do the same thing. Previously, Glotzer’s team had shown that bipyramid particles — like two short, three-sided pyramids stuck together at their bases — will form structures resembling that of fire ice if you put them into a sufficiently small box. Fire ice is made of water molecules that form cages around methane, and it can burn and melt at the same time. This substance is found in abundance under the ocean floor and is an example of a clathrate. Clathrate structures are under investigation for a range of applications, such as trapping and removing carbon dioxide from the atmosphere.
Unlike water clathrates, earlier nanoparticle clathrate structures had no gaps to fill with other materials that might provide new and interesting possibilities for altering the structure’s properties. The team wanted to change that.

“This time, we investigated what happens if we change the shape of the particle. We reasoned that if we truncate the particle a little, it would create space in the cage made by the bipyramid particles,” said Sangmin Lee, a recent doctoral graduate in chemical engineering and first author of the paper.
He took the three central corners off each bipyramid and discovered the sweet spot where spaces appeared in the structure but the sides of the pyramids were still intact enough that they didn’t start organizing in a different way. The spaces filled in with more truncated bipyramids when they were the only particle in the system. When a second shape was added, that shape became the trapped guest particle.
Glotzer has ideas for how to create selectively sticky sides that would enable different materials to act as cage and guest particles, but in this case, there was no glue holding the bipyramids together. Instead, the structure was completely stabilized by entropy.
“What’s really fascinating, looking at the simulations, is that the host network is almost frozen. The host particles move, but they all move together like a single, rigid object, which is exactly what happens with water clathrates,” Glotzer said. “But the guest particles are spinning around like crazy — like the system dumped all the entropy into the guest particles.”
This was the system with the most degrees of freedom that the truncated bipyramids could build in a limited space, but nearly all the freedom belonged to the guest particles. Methane in water clathrates rotates too, the researchers say. What’s more, when they removed the guest particles, the structure threw bipyramids that had been part of the networked cage structure into the cage interiors — it was more important to have spinning particles available to maximize the entropy than to have complete cages.
“Entropy compartmentalization. Isn’t that cool? I bet that happens in other systems too — not just clathrates,” Glotzer said.
Thi Vo, a former postdoctoral researcher in chemical engineering at U-M and now an assistant professor of chemical and biomolecular engineering at the Johns Hopkins University, contributed to the study.
This study was funded by the Department of Energy and Office of Naval Research, with computing resources provided by the National Science Foundation’s Extreme Science and Engineering Discovery Environment and the University of Michigan.
Glotzer is also the John Werner Cahn Distinguished University Professor of Engineering, the Stuart W. Churchill Collegiate Professor of Chemical Engineering, and a professor of materials science and engineering, macromolecular science and engineering, and physics. More

Mobility limitation is an initial stage of human mobility disability and an early sign of functional decline. It can manifest as muscle weakness, loss of balance, unsteady gait, and joint pain. Long-term and continuous monitoring of joint motion may potentially prevent or delay decline by allowing the early diagnosis, prognosis, and management of mobility-related conditions.
This long-term and continuous monitoring is made possible by analysis systems that are either non-wearable or wearable. Non-wearable systems are reliable, but require a laboratory environment and trained individuals and are therefore impractical for daily use. On the other hand, wearable systems are portable, cheaper, and much easier to use. Unfortunately, typical wearable sensors tend to be inflexible and bulky.
A relatively new player to the wearable systems field are wearables made from conductive fabric (CF), which are soft, lightweight, malleable, and non-invasive. These sensors are comfortable and suitable for long-term monitoring. However, most CF-based wearables become error-prone if displaced from their intended location and rely on external components that restrict the sensitivity and working range of the sensors.
To overcome these limitations, a research team created a wearable with a high degree of functional and design freedom. Associate Professor Low Hong Yee and her colleagues from the Singapore University of Technology and Design (SUTD) collaborated with Dr Tan Ngiap Chuan of SingHealth Polyclinics and published their research paper, ‘All knitted and integrated soft wearable of high stretchability and sensitivity for continuous monitoring of human joint motion’ in Advanced Healthcare Materials.
According to Associate Professor Low, their key considerations when designing the wearable were sensor data accuracy and reliability and for the sensor to rely on as few external components as possible. The result was a highly stretchable, fully functional sensing circuit made from a single fabric. Because the knee joint is important for lower limb mobility, the wearable was designed for the knee.
To develop this single-fabric circuit, the team mechanically coupled an electrically conductive yarn with a dielectric yarn of high elasticity in various stitch patterns. Dimensions were customised according to the subject’s leg. The functional components — sensors, interconnects, and resistors — formed a stretchable circuit on the fully knitted wearable that allowed real-time data to be obtained.

However, putting together sensors, interconnects, and resistors in a single stretchable knit is difficult. Associate Professor Low mentioned that “the synergy of yarns with different electrical and mechanical properties to achieve high signal sensitivity and high stretchability” was challenging, as the desired properties for each component were vastly different.
Sensors need to produce a large change in resistance for enhanced sensitivity, while interconnects and resistors need fixed resistances of the highest and lowest values, respectively. As such, the researchers optimised yarn composition and stitch type for each component before connecting the functional circuit to a circuit board contained in a pocket of the wearable, allowing for wireless transmission of real-time data.
With a soft knee wearable developed, its components functional, and data transmission possible, it was time to test the performance of the wearable. The team assessed the wearable through extension-flexion, walking, jogging, and staircase activities. Subjects wore the knee wearable together with reflective markers that were detected by a motion capture system, allowing the comparison between sensor data and actual joint movement.
The sensor response time was less than 90 milliseconds for a step input, which is fast enough to monitor the human movements included in the study. Additionally, the smallest change in joint angle that the sensors could detect was 0.12 degrees. The sensor data showed strong correlation with joint movement data acquired from the motion capture system, demonstrating reliability of the sensor data.
The potential impact of such device in the medical field is huge. Long-term continuous monitoring of joint motion is important to track mobility-related conditions. Often, people ignore early signs of mobility decline as they are not deemed serious enough to seek help. Wearable technology solves this problem by assessing a user’s mobility directly in real-time.
Embedding a user-friendly sensor circuit into a soft and comfortable fabric may increase the public’s adoption of wearable technology, especially among athletes and the elderly. Data can be gathered in real-time and translated into indicators that can detect mobility decline. When signs of mobility decline are found, preventive care, prognosis, and management of the healthcare condition can be given.
Building on this work, the team intends to study the effect of sweat and humidity on sensor signals and to extend the research to include subjects from both healthy and unhealthy populations in the future. “We have started working on extending the wearable to special user groups and to monitor other body joints, such as the shoulder,” stated Associate Professor Low. “We’re also looking at securing an incubation fund to explore the commercialisation potential of the wearable.”
Video: https://youtu.be/KPlSPtDVs2k More