Friday, July 03, 2026

Custom Fit: FAU Engineers Prosthetic Hand That Learns, Adapts to Each User

Florida Atlantic University

Robotic Hand — image:
The trained AI model converts the user’s forearm muscle movements into real-time commands that control a robotic hand, allowing it to perform the intended gestures.
view more
Credit: Alex Dolce, Florida Atlantic University

Most prosthetic hands today still struggle with a fundamental problem: no two amputees are the same, yet most devices are designed as if they are. That mismatch makes natural, intuitive control difficult, often turning what should feel like a seamless extension of the body into something that requires constant learning and adjustment.

Even with advanced technology, users are frequently left to interpret faint muscle signals that can shift with sweat, skin changes, or everyday movement – creating a gap between intention and control that can be frustrating and, in some cases, lead people to abandon the device altogether.

Researchers have made progress by improving how muscle signals are interpreted, but the core challenge remains: the signals are often unstable and hard to translate into natural movement.

To address this challenge, Erik Engeberg, Ph.D., is leading research to shift the focus from standardized devices to truly personalized systems that adapt to each individual. Engeberg is a professor in FAU’s College of Engineering and Computer Science, with appointments in the Department of Ocean and Mechanical Engineering and the Department of Biomedical Engineering. He is also a member of the FAU Stiles-Nicholson Brain Institute and the FAU Center for Complex Systems within the Charles E. Schmidt College of Science.

The approach begins with 3D scanning a person’s residual limb to create a custom 3D-printed wearable sleeve embedded with soft, flexible magnetic sensors. These sensors sit comfortably against the skin and capture subtle changes in muscle shape and pressure as the user attempts hand and wrist movements, allowing the system to interpret intent in real time.

The design is tailored to each individual, with sensor arrays configured with either 18 or 24 modules depending on limb size and anatomy and paired with an individualized artificial intelligence model that learns each person’s unique muscle patterns rather than relying on a generalized dataset.

In testing with 10 participants, including three upper-limb amputees, the system classified 19 hand and wrist gestures in real time, translating intent into control of a dexterous robotic hand. Results, published in IEEE Transactions on Neural Systems and Rehabilitation Engineering, show the system performed consistently and reliably under repeated use.

To assess durability, researchers applied more than 7,500 robotic force cycles over several hours while precisely measuring sensor response. The system showed a strong, stable relationship between applied force and output, accurately capturing pressure without loss of performance.

Even after thousands of cycles, signals remained clear and stable, with strong separation between signal and noise and only minor variation over time. Overall, the sensors showed no meaningful drift or degradation, maintaining accuracy, repeatability and responsiveness essential for real-world prosthetic control.

“Prosthetic control is not one-size-fits-all. Every individual brings a distinct movement signature shaped by their anatomy, injury history and how their remaining muscles function,” said Engeberg, senior author. “If we want these systems to truly work in everyday life, they have to be custom fit. By combining 3D-printed wearable sensors with individualized AI models, we’re moving closer to prosthetic systems that can respond naturally and in real time to a person’s intent, rather than forcing users to adapt to the limitations of the device.”

Findings also showed there is no single best sensor configuration for all users. Some participants achieved higher accuracy with fewer sensors, while others required more, with optimal setups varying based on individual anatomy and differences in injury history and remaining muscle function. In several cases, participants achieved more than 90% accuracy across multiple gestures only when the sensor layout was tailored to their residual muscles.

“Our results highlight that prosthetic performance is highly dependent on how well sensor placement and quantity are matched to the individual,” said Engeberg. “This suggests a future in which prosthetists can fine-tune sensor configurations much like a prescription, balancing both function and comfort for each user.”

The research also produced a shared dataset from all participants, including amputees and non-amputees, providing a valuable resource for the broader scientific community.

“This work speaks to something very practical: improving quality of life in a very direct way,” said Stella Batalama, Ph.D., dean of the College of Engineering and Computer Science. “When we close the gap between engineering innovation and what people actually need in their daily lives, especially for individuals who depend on prosthetic devices for independence, the impact goes far beyond the lab. It’s about restoring function, confidence and the ability to engage with the world more naturally.”

In the United States alone, an estimated 2.1 million people are living with limb loss, with around 185,000 amputations occurring each year. Globally, more than 50 million people are affected, a number expected to grow due to diabetes, vascular disease, trauma and conflict-related injuries. Upper-limb amputations are among the most challenging to restore functionally because of the complexity of natural hand and finger movement.

Study co-author is Wen-Yu “Marty” Cheng, a graduate student and Ph.D. candidate in FAU’s College of Engineering and Computer Science.

- FAU -

A custom-fit wearable device using 3D printing designed specifically for each individual. The device contains built-in magnetic sensors that detect subtle muscle activity.

Erik Engeberg, Ph.D., is leading research to shift the focus from
standardized devices to truly personalized systems that adapt to each individual.
Credit
Alex Dolce, Florida Atlantic University

Custom-Fit Prosthetic Hand [VIDEO]

The process begins with a quick 3D scan of a person's arm to capture its exact shape. That scan is then used to create a custom-fit wearable device using 3D printing. Designed specifically for each individual, the lightweight two-piece device fits comfortably around the forearm without limiting movement. It also contains built-in magnetic sensors that detect subtle muscle activity, allowing the system to capture movement signals with greater accuracy.

Credit

Florida Atlantic University

Journal

IEEE Transactions on Neural Systems and Rehabilitation Engineering

DOI

10.1109/TNSRE.2026.3702598

Method of Research

Experimental study

Subject of Research

People

Article Title

Compliant Magnetic Sensor Arrays Enable Real-Time Force Myogram Pattern Recognition for Dexterous Hand Control by Amputees

3 discoveries spark awareness of fireworks’ environmental impact

Research sheds light on how these celebratory displays’ residues may affect wildlife, water, and air quality.

American Chemical Society

A burst of new data has ignited a better understanding of how fireworks impact the environment. Three recent studies comb through pyrotechnics’ fallout, analyzing litter, particulate matter, and airborne compounds. These papers, published in ACS journals, provide insights into what happens after the sparkles fade and explain the potential impacts on human health and the environment. Reporters can request free access to these papers by emailing newsroom@acs.org.

Firecracker litter kindles change in water chemistry. After firecrackers flare out, they leave behind residue containing partly burned fuel and additives, metal salts, and charred packaging. A lab study published in Environmental Science & Technology found that this residue released substantial amounts of metal ions (e.g., potassium and manganese) and dissolved organic matter (e.g., simple phenols and sulfur-containing compounds) into lake and river water. Simultaneously, the solid residues adsorbed existing dissolved substances, such as larger, more complex compounds, from the water. The researchers say these chemical changes could disturb microbial activity and aquatic life, especially if litter washes into rivers or lakes from intensive or recurring festive events. Simply cleaning up spent firecrackers properly can help reduce these impacts.
Celebratory emissions come from multiple sources. Communities often have fireworks at major events, but how these displays affect air quality compared to other sources isn’t clear. So, researchers reporting in ACS ES&T Air monitored particulate matter at a large, multi-day U.K. athletic competition. They observed significant short-term increases in coarse and fine particles and traced them to cooking aerosols from vendors and dust kicked up by vehicles. The opening and closing ceremonies each produced two spikes in fine particles: one from dust as attendees arrived and a second, slightly smaller one from firework displays. The researchers estimate that people who attended all the events exceeded recommended limits for air pollutants set by the World Health Organization, highlighting celebratory events as a route of exposure to fine particulate matter.
Post-burst haze is more than just ash. Some firework formulations contain compounds called amines that can react in the air to form aerosols that contribute to haze and poor air quality. So, a group of scientists wanted to investigate whether amines are consumed during firework explosions or spewed into the air. They measured the compounds in gases and particles during Lunar New Year celebrations in a suburban area in China. These initial findings, reported in Environmental Science & Technology Letters, showed substantial increases in several amines compared to a non-celebratory period — especially during the biggest displays — along with other firework-related pollutants such as fine particulate matter and sulfate and potassium ions. The researchers say the results indicate how fireworks may contribute more than just smoke to post-celebration haze.

A recent video by ACS in collaboration with the Science History Institute outlines the chemical history of fireworks. Additionally, the ACS Webinars’ production “Flash! Bang! Boom!” provides a longer overview of the same topic.

###

The American Chemical Society (ACS) is a nonprofit organization founded in 1876 and chartered by the U.S. Congress. ACS is committed to improving all lives through the transforming power of chemistry. Its mission is to advance scientific knowledge, empower a global community and champion scientific integrity, and its vision is a world built on science. The Society is a global leader in promoting excellence in science education and providing access to chemistry-related information and research through its multiple research solutions, peer-reviewed journals, scientific conferences, e-books and news periodical Chemical & Engineering News. ACS journals are among the most cited, most trusted and most read within the scientific literature; however, ACS itself does not conduct chemical research. As a leader in scientific information solutions, its CAS division partners with global innovators to accelerate breakthroughs by curating, connecting and analyzing the world’s scientific knowledge. ACS’ main offices are in Washington, D.C., and Columbus, Ohio.

Registered journalists can subscribe to the ACS journalist news portal on EurekAlert! to access embargoed and public science press releases. For media inquiries, contact newsroom@acs.org.

Note: ACS does not conduct research but publishes and publicizes peer-reviewed scientific studies.

Journal

Environmental Science & Technology

Development of weather intervention methods supporting future disaster mitigation

Hiroshima University

image:
Weather control modeling assesses the effectiveness of weather intervention to reduce damage and loss from disasters. (Yuta Higuchi / Hiroshima University)
view more
Credit: Yuta Higuchi / Hiroshima University

Black-box optimization, particularly Bayesian optimization, is a practical approach for weather-intervention design, achieving meaningful rainfall reduction even under a very limited budget of expensive weather simulations, finding effective actions with only a small number of trials.

In recent years, the frequency of weather-related natural disasters – cyclones, torrential rains, floods – has increased as a consequence of global warming. These disasters cause billions of dollars in damage and losses every year. As a result, there is great interest in weather control, the process by which human intervention can deliberately alter the weather.

Modeling weather interventions via computational modeling is the primary means by which weather interventions are studied. However, as weather is a vast and complex system, state-of-the-art numerical weather prediction (NWP) models of interventions are highly limited, requiring enormous computational resources.

A team of researchers led by Hiroshima University have applied black-box optimization algorithms to rainfall control and have demonstrated that they can be used to model rainfall minimization through weather control.

Their findings were published in the Journal of Computational Science on April 10, 2026.

Modeling weather control is an application of control theory, an interdisciplinary field of engineering and mathematics that deals with the behavior of systems. NWP modeling of weather control takes the input, models every step of the control process, and yields the output. It is here that the need for enormous computational resources arises, as weather is a highly complex system that is very difficult to accurately model in full.

“Our study asks how to design effective weather interventions for reducing rainfall when weather simulations are highly nonlinear, expensive, and do not provide reliable gradient information,” says Masaki Ogura, professor at Hiroshima University’s Graduate School of Advanced Science and Engineering and corresponding author of the study. “This is important because ... any practical intervention method must work under strict computational limits.”

The researchers chose to use black box optimization to tackle this issue. Black-box optimization is the process of optimizing a system or function where the internal workings are not known or are too complex to be modeled accurately. The term "black-box" signifies that the system's input-output behavior is the only information available for optimization.

The researchers chose four black-box optimization algorithms: Bayesian optimization, random search, particle swarm optimization, and genetic algorithms, and applied them to two experimental settings with different scales and levels of complexity. They used the NWP model Scalable Computing for Advanced Library and Environment Regional Model (SCALE-RM), specifically developed for climate research, as the base model for their simulations. The two experimental settings were the smaller and limited warm bubble experiment and the larger and more complex real atmosphere experiment. Specifically, they tested how a weather intervention modifying wind-fields would reduce rainfall over a target region. Their models attempted the intervention to a small portion of the simulation, either only at the start of the simulation (one-step) or every 600 or 3600 seconds (multi-step).

“Black-box optimization is a practical approach for weather-intervention design, and Bayesian optimization performed best among the tested methods under the studied settings,” says Ogura, describing the results. “One striking aspect is that meaningful rainfall reduction was achieved even under a very limited budget of expensive weather simulations. This is notable because each candidate intervention had to be evaluated through a full numerical weather simulation, so the optimizer had to find effective actions with only a small number of trials.”

Further, Bayesian optimization exhibits sensitivity to hyperparameters (parameters that define any configurable part of a model's learning process). This indicates that Bayesian optimization can be adjusted, potentially leading to flexibility and adaptability to different atmospheric conditions.

By demonstrating the potential to design effective weather interventions even under limited computational resources, this study accelerates future research and development in disaster-prevention technologies and climate engineering.

The researchers caution that their findings may not generalize to all scenarios of weather interventions. “The next step is to test the framework in more diverse atmospheric scenarios and better understand why the methods perform differently,” Ogura concludes. “The ultimate goal is to build a reliable computational basis for future weather-intervention design for disaster mitigation.”

Yuta Higuchi & Yang Bai at Hiroshima University; Rikuto Nagai & Naoki Wakamiya at The University of Osaka; Atsushi Okazaki at Chiba University co-authored the study.

This work was supported by the Japan Science and Technology Agency (JST) Moonshot R&D Program (JPMJMS2284).

###
About Hiroshima University
Since its foundation in 1949, Hiroshima University has striven to become one of the most prominent and comprehensive universities in Japan for the promotion and development of scholarship and education. Consisting of 12 schools for undergraduate level and 5 graduate schools, ranging from natural sciences to humanities and social sciences, the university has grown into one of the most distinguished comprehensive research universities in Japan. English website: https://www.hiroshima-u.ac.jp/en

Illustration of black box optimization for weather intervention design to reduce rainfall (Yuta Higuchi / Hiroshima University)