What if a small device inspired by the shape of a starfish could monitor your heart while you walk, exercise, or go about your normal day?
Researchers at the University of Missouri have developed an experimental wearable cardiac monitoring system that combines flexible electronics, advanced motion sensors, and artificial intelligence to address one of the continuing challenges in wearable health technology: accurately recording cardiac signals while the body is moving.
The starfish-inspired device features five flexible arms that attach to the chest, allowing it to measure both the electrical activity and mechanical movement of the heart.
In research published in Science Advances on April 2, 2025, scientists demonstrated that the system could record relatively clear cardiac signals while engaging in physical activity and use machine learning to classify several heart conditions.
The research team reported classification correctness exceeding 91% for distinguishing among healthy cardiac signals, atrial fibrillation, myocardial infarction, and heart failure in its experimental dataset.
Unlike a typical smartwatch that mainly relies on optical pulse sensing and may offer a single-lead ECG, the new device combines three different methods of measuring heart function.
The result is a potentially more extensive view of cardiac health.
However, the technology stays experimental. Its reported performance comes from a relatively small research study, and the findings do not mean it can reliably diagnose heart attacks or replace professional cardiac examinations in everyday clinical practice.
New research activity in 2026 suggests that the University of Missouri team is continuing to investigate the system’s potential for longer-term monitoring.
Why Is Continuous Heart Monitoring Important?
Cardiovascular disease remains one of the leading causes of death worldwide.
According to the World Health Organization, cardiovascular diseases include conditions affecting the heart and blood vessels, such as coronary heart disease, cerebrovascular disease, and other circulatory disorders.
Some heart problems develop gradually, while others cause intermittent abnormalities that may be difficult to detect during a short medical appointment.
For example, a person with paroxysmal atrial fibrillation may experience an irregular heartbeat that appears and disappears.
A conventional ECG recorded during a symptom-free period might fail to detect the abnormal rhythm.
This is why ambulatory cardiac monitoring, including Holter monitors and other wearable ECG systems, plays an important role in cardiology.
By recording signals for longer periods, clinicians may identify abnormalities that would otherwise be missed.
Nevertheless, continuously collecting reliable cardiac data creates technical challenges, especially when a patient is physically active.
The Problem With Conventional Wearable Heart Monitors
Modern heart-monitoring technologies have made considerable progress.
Smartwatches, adhesive ECG patches, and ambulatory monitoring devices can record important physiological information.
However, each technology has limitations.
A primary challenge is motion artifacts.
Motion artifacts are unwanted disturbances in recorded signals caused by movement of the body, changes in skin contact, muscle activity, or movement of the sensor itself.
When a person walks or runs, these disturbances may overlap with the signals generated by the heart.
As a result, the recorded waveform may become difficult to interpret.
This challenge is especially important when researchers attempt to measure the subtle mechanical vibrations produced by the heartbeat.
For example, body movement during running can produce much larger accelerations than those generated by cardiac contractions.
The University of Missouri research team sought to address this problem through a combination of bioinspired structural design, motion compensation, and artificial intelligence.
Importantly, existing medical-grade ECG monitors can already provide clinically useful ambulatory recordings. The new system constitutes an experimental approach to improving multimodal monitoring rather than proof that conventional monitoring methods are unreliable.
What Is the Starfish-Inspired Wearable Heart Monitor?
The device was developed by an international research team led by scientists including Sicheng Chen and Zheng Yan at the University of Missouri.
The researchers published their findings in a paper titled:
Starfish-Inspired Wearable Bioelectronic Systems for Physiological Signal Monitoring During Motion and Real-Time Heart Disease Diagnosis.
The study appeared in Science Advances, volume 11, issue 14, with the article identifier eadv2406.
The design takes inspiration from a starfish’s five-arm radial structure.
Rather than placing all sensors on a single rigid or unified patch, the researchers developed a flexible system with five separate sensing arms connected to a central electronic hub.
This arrangement enables the sensors to respond to body movement while reducing some of the mechanical interference that affects cardiac recordings.
The device combines electronic sensors, flexible conductive pathways, specialized adhesive gels, wireless communication, and machine learning.
Original research: Chen et al., Science Advances (2025)
Open-access paper: PubMed Central, Full Research Article
Why Did Scientists Choose a Starfish-Inspired Design?
Starfish have a distinctive body structure organized around five arms.
The University of Missouri team drew inspiration from the animal’s radial symmetry and its ability to coordinate movement across multiple limbs.
This led to a wearable design featuring five flexible, serpentine arms extending from a central hub.
The researchers used this geometry to separate different sensing locations mechanically.
That separation is important because a single continuous structure may transmit movement-related disturbances across the entire device.
With the starfish-inspired configuration, individual sensing pads can collect information from different regions of the chest.
The signals from multiple pads can then be combined to estimate and reduce movement-related noise.
How the Five-Arm Design Works
The device’s fifth sensing pad is positioned near the heart, where cardiac mechanical signals can be detected more strongly.
The remaining four sensing pads primarily help measure body movement and related disturbances.
Through comparing these recordings, the system can estimate the movement-related components affecting the primary cardiac signal.
Software then subtracts some of the interference.
Additional adaptive filtering further improves signal quality.
This integration of physical design and software-based processing is one of the study’s main technological contributions.
Three Ways the Device Measures Heart Activity
One of the most interesting aspects of the wearable is that it does not rely exclusively on conventional ECG.
Instead, it simultaneously records three types of cardiac signals.
1. Electrocardiography (ECG): Measuring Electrical Activity
An electrocardiogram records electrical signals associated with the activation and recovery of the heart muscle.
ECG delivers valuable information about heart rhythm, conduction, and other cardiac characteristics.
It can help clinicians identify abnormalities such as atrial fibrillation and certain patterns associated with heart disease.
However, ECG primarily measures electrical activity rather than directly measuring how effectively the heart muscle contracts.
2. Seismocardiography (SCG): Measuring Mechanical Vibrations
Seismocardiography measures small chest-wall accelerations associated with the mechanical activity of the heart.
These vibrations reflect events such as cardiac contraction and the movement of blood and heart valves.
SCG may provide useful information about the timing of mechanical cardiac events.
However, the signals are small and vulnerable to interference from ordinary body movements.
3. Gyrocardiography (GCG): Measuring Rotational Movement
Gyrocardiography records subtle rotary motions associated with the beating heart.
It uses gyroscopic sensors to measure angular motion.
Like SCG, gyrocardiography provides mechanical information that is different from the electrical signals recorded by ECG.
By combining ECG, SCG, and GCG, researchers can examine both the electrical activation of the heart and its mechanical reactions.
This multimodal approach may provide more information than any one signal can supply alone.
How Does Artificial Intelligence Reduce Motion Interference?
The system uses machine learning for several distinct tasks.
First, it identifies the wearer’s current activity.
Second, it selects filtering strategies suited to that activity.
Third, it analyzes processed cardiac signals to classify patterns associated with particular heart conditions.
Recognizing Different Types of Movement
The researchers trained a machine learning model to identify four activity states:
- Sitting.
- Walking.
- Jogging.
- Running.
For the motion-recognition experiments, the study collected data from 16 human participants.
The system used information from accelerometers and gyroscopes across the five sensing arms.
A transformer-driven model analyzed the movement patterns and classified the activity state.
The reported activity-recognition accuracies were:
| Activity | Reported classification accuracy |
| Sitting | 100% |
| Walking | 99.87% |
| Jogging | 99.42% |
| Running | 99.31% |
These results show promising performance in the researchers’ controlled dataset.
They do not mean the device will identify every movement correctly in all populations or practical environments.
Applying Adaptive Signal Filtering
Once the device identifies the user’s activity, its software adjusts how it processes the cardiac signals.
For example, the motion sequences generated during walking differ from those produced during running.
The system applies filters adapted to these changing conditions rather than relying on a single fixed processing method.
The researchers combined this flexible filtering with motion compensation using recordings from the different sensor pads.
Their results showed that these techniques increased the clarity of cardiac mechanical signals during movement.
Sustaining Signal Quality While Running
The research reported an ECG signal-to-noise ratio of approximately 35 decibels during running.
Signal-to-noise ratio measures the relative strength of the desired signal compared with ambient interference.
A higher value generally indicates a clearer recording.
The researchers also demonstrated that important features of the SCG and GCG signals could be recovered while engaged in physical activity.
Nevertheless, a high signal-to-noise ratio does not by itself prove diagnostic validity or clinical effectiveness.
It is primarily a measure of recording quality.
How Accurate Was the Wearable Heart Monitor in the first Study?
The headline result was the machine learning model’s ability to classify patterns associated with different heart conditions.
The researchers collected cardiac recordings from 18 patients:
- Six with atrial fibrillation.
- Six with myocardial infarction.
- Six with heart failure.
These recordings provided more than 20 hours of cardiac data.
The team combined the patient recordings with data from healthy participants and trained machine machine-learning models to classify the different signal profiles.
The strongest results were obtained when all three signal types ECG, SCG, and GCG were analyzed together.
Reported Classification Results
| Cardiac category | Accuracy reported in the research |
| Normal cardiac signals | 91.31% |
| Atrial fibrillation | 94.03% |
| Myocardial infarction | 91.67% |
| Heart failure | 91.66% |
These results are encouraging because they suggest that electrical and mechanical cardiac information can provide complementary diagnostic features.
However, the reported percentages are classification results from the study’s experimental dataset, not clinically established sensitivity nor specificity for diagnosing these diseases in the general population.
The study’s patient sample was small, and the recordings were segmented into multiple data windows.
Those data windows do not represent hundreds or thousands of independent patients.
The results therefore require larger, independent clinical validation.
Which Heart Conditions Could the Technology Help Identify?
The researchers examined three major cardiovascular conditions.
Atrial Fibrillation
Atrial fibrillation, commonly called AFib, is a heart rhythm disorder characterized with irregular electrical activity in the upper chambers of the heart.
It can cause symptoms such as palpitations, fatigue, shortness of breath, or dizziness.
Some people have few or no noticeable symptoms.
Atrial fibrillation is clinically important because it can increase the risk of stroke and other complications.
Longer-term monitoring can help identify intermittent episodes that may not appear during a brief ECG examination.
The wearable’s combination of electrical and mechanical signals could be useful for upcoming research into AFib monitoring.
However, an AI-generated AFib alert would require appropriate clinical interpretation and confirmation.
Myocardial Infarction
Myocardial infarction is the medical term for a heart attack.
It usually occurs when blood flow to part of the heart muscle becomes severely restricted or blocked, causing injury.
The researchers included recordings from patients diagnosed with myocardial infarction in their classification study.
The system showed an ability to distinguish patterns in these recordings from those of other groups.
However, this does not establish that the device can reliably detect a new or ongoing heart attack as it happens.
Diagnosing an acute myocardial infarction generally requires urgent medical evaluation, including ECG findings, cardiac troponin testing, clinical history, and sometimes imaging.
A wearable algorithm should not be used to rule out a heart attack.
Heart Failure
Heart failure occurs when the heart cannot pump blood or fill effectively enough to meet the body’s needs.
It can cause fatigue, shortness of breath, fluid retention, and reduced exercise tolerance.
Because heart failure can affect both electrical and mechanical cardiac function, it is a particularly interesting application for multimodal monitoring.
In one example from the research, the team recorded cardiac signals from a patient with heart failure while walking.
The measurements showed differences in contraction-related signals compared with a healthy participant.
Such findings show that mechanical sensing may supply valuable additional information.
However, this technology has not been established as a replacement for echocardiography or other standard heart failure assessments.
What Makes the Device Different From a Smartwatch?
Consumer smartwatches and experimental chest-worn cardiac devices are designed for different purposes.
Smartwatches offer accessible health monitoring, usually using optical sensors and, in some models, ECG electrodes.
The starfish-inspired system is a research platform focused on obtaining multiple synchronized cardiac signals during movement.
| Feature | Typical consumer smartwatch | Starfish-inspired research device |
| Main location | Wrist | Chest |
| Heart rate monitoring | Commonly available | Supported through recorded cardiac signals |
| ECG capability | Available in selected models | Included |
| SCG measurement | Not commonly a primary cardiac feature | Included |
| GCG measurement | Not commonly a primary cardiac feature | Included |
| Motion compensation | Varies by manufacturer and model | Specialized multi-sensor system |
| AI heart disease classification | Limited to specific supported features | Experimental classification of several conditions |
| Clinical availability | Commercial products available | Research-stage technology |
This comparison does not establish that the starfish-inspired system is more accurate than every smartwatch or medical monitor.
Such a conclusion would require direct, appropriately designed comparative studies.
A Lightweight, Flexible Design for Everyday Wear
Continuous assessment becomes much more useful when a device is comfortable enough to wear for extended periods.
The researchers designed the device around a thin, flexible polyimide substrate.
The sensing arms use serpentine conductive pathways that allow them to bend and accommodate body movement.
According to the first paper, the wearable system weighs approximately 1.7 grams, including its battery.
Its flexible structure helps it conform to the chest while the sensing elements maintain contact through a conductive adhesive gel.
Wireless Communication
The system uses Bluetooth to transmit collected biological signals to a connected smart device.
Machine learning algorithms can then process the data locally, an approach occasionally called edge computing.
This design could potentially enable users to view cardiac information without continuously transmitting raw recordings to a remote server.
It may also support future remote-monitoring workflows.
However, data security, reliability, and clinical integration would need to be assessed before widespread medical use.
Wireless Charging
The researchers incorporated wireless power and charging capabilities.
This could make extended monitoring more practical by lessening the need to remove the device for conventional cable charging.
Nevertheless, laboratory demonstration of wireless charging does not establish unrestricted continuous use or extended reliability.
Skin Comfort and Breathability
The first device uses conductive and nonconductive biogels to attach its components to the skin.
While this approach supports stable sensing, prolonged skin contact can create concerns about irritation and comfort.
The University of Missouri team acknowledged these problems and described ongoing work to improve wearable materials.
This distinction matters because a flexible research prototype is not automatically suitable for uninterrupted wear over weeks or months.
Why Could This Technology Matter for Remote Cardiac Care?
One possible application is monitoring people outside hospitals and clinics.
Conventional cardiovascular testing frequently captures information over a limited period.
A wearable sensor that records electrical and mechanical cardiac signals during normal daily activity could deliver additional insights.
Future applications might include investigating intermittent rhythm abnormalities, examining changes in mechanical heart function, and supporting remote assessment of selected patients.
Such monitoring may be especially useful when symptoms occur unpredictably.
However, several requirements must be met before the technology can support routine remote care.
Clinicians need reliable recordings, clinically validated alerts, manageable false-positive frequency, secure patient data, and workflows for responding to abnormal findings.
A device that collects more data does not automatically improve outcomes unless those data lead to appropriate decisions.
April 2026 Update: University of Missouri Expands Human Research
An important development occurred on April 3, 2026, when the University of Missouri approved a research study titled:
Multimodal, Porous, Starfish-Like Soft Wearable Bioelectronic Systems for Motion-Artifact-Resilient, Real-Time and High-Accuracy Cardiac Monitoring.
According to the university’s participant recruitment information, the project aims to investigate an improved starfish-inspired wearable capable of recording ECG, SCG, and GCG signals during daily activities.
What Is the New Study Testing?
The research plan includes monitoring healthy volunteers and people with atrial fibrillation.
Participants are expected to wear the device continuously for approximately one week.
Researchers plan assessments on days 1, 3, and 7, including activities such as sitting, walking, and jogging.
These assessments could provide information about:
- Signal quality during repeated everyday movements.
- Performance over a longer monitoring period.
- Device adhesion and wearability.
- Reliability of simultaneous ECG, SCG, and GCG measurements.
- Differences between healthy volunteers and individuals with atrial fibrillation.
The university’s public study description indicates that this work is intended to validate the system’s performance for continuous cardiac monitoring.
Importantly, study approval and recruitment do not mean the researchers have already demonstrated successful results.
The recruitment information does not provide final performance outcomes from the planned one-week monitoring study.
Official 2026 research listing: University of Missouri, Starfish-Like Wearable Cardiac Monitoring Study
What Are the Limitations of the Original Research?
Although its innovative design, several shortcomings must be addressed.
1. The Patient Sample Was Small
Only 18 patients with cardiac conditions contributed to the first disease-classification dataset.
This is insufficient to establish reliable performance over various ages, medical histories, disease severities, and clinical settings.
2. Algorithm Accuracy May Not Generalize
Machine learning models sometimes perform better on controlled research datasets than on new patients encountered in clinical practice.
Independent testing is needed across multiple hospitals and populations.
3. More Testing Is Needed During Everyday Activities
Walking and running tests show promising motion resistance.
However, real-world use also involves sleeping, sweating, bathing, irregular movements, prolonged wear, and changes in sensor placement.
These factors may affect recording quality.
4. The Device Is Not a Proven Emergency Diagnostic Tool
The study’s classification of myocardial infarction signals must not be interpreted as proof that the device can detect or exclude an acute heart attack.
Emergency cardiac evaluation requires established diagnostic procedures.
5. Long-Term Comfort and Safety Need Evaluation
Adhesive materials may cause irritation in some users.
Researchers must also establish the durability of the electronics, battery safety, and stable performance over long periods.
6. Regulatory and Clinical Validation Continue to Be Necessary
Before a research device can be marketed for specific medical diagnostic purposes, its manufacturer may need to demonstrate safety and effectiveness under applicable medical-device regulations.
The first study does not establish that the device is authorized as a clinical diagnostic system.
Could a Starfish-Inspired Monitor Replace Hospital Heart Tests?
Not at present.
The device offers an experimental way to collect cardiac electrical and mechanical information while people move.
That may complement medical evaluation in the future.
However, standard diagnostic tools such as clinical ECG, echocardiography, ambulatory ECG monitoring, cardiac biomarkers, and other investigations serve important purposes that cannot be replaced solely by a new wearable sensor.
For example, echocardiography provides information about cardiac anatomy, blood flow, valve function, and pumping performance that a wearable chest sensor cannot directly reproduce.
Similarly, confirming a suspected heart attack requires more than identifying an abnormal wearable waveform.
The most realistic potential role for the technology is as an additional source of physiological information that clinicians could use together with established assessments.
What Does This Research Mean for the Future of Wearable Health Technology?
The starfish-inspired design demonstrates a larger trend in medical engineering: combining nature-inspired structures, flexible electronics, and machine learning.
Instead of focusing only on developing smaller sensors, researchers are redesigning how sensors interact mechanically with the human body.
This approach can influence future technologies beyond cardiac monitoring.
Other wearable devices may benefit from improved management of movement-related interference when measuring breathing, muscle activity, or other biological signals.
Another important direction is multimodal detection.
By combining different kinds of biological measurements, machine learning systems may detect patterns that are difficult to identify using one sensor alone.
However, demonstrating technical innovation is only one step.
The general success of medical wearables will depend on their ability to improve decisions, reduce unnecessary testing, identify clinically important problems, and ultimately benefit patients.
Conclusion
The University of Missouri’s starfish-inspired wearable heart monitor serves as an innovative advance in experimental bioelectronics.
Its five-arm design, combined with motion compensation and artificial intelligence, yields a promising approach to recording cardiac signals during ordinary physical activity.
The research team’s 2025 study demonstrated encouraging classification results for atrial fibrillation, myocardial infarction, and heart failure using combined electrical and mechanical measurements.
The initiation of further human research in 2026 suggests that scientists are continuing to investigate the technology’s longer-term surveillance potential.
However, the first patient dataset was small, and the device has not been established as a clinically validated replacement for conventional cardiac monitoring or emergency diagnostic testing.
The real significance of this research is not that a starfish-shaped wearable can already diagnose heart disease reliably in everyday life, but that bioinspired engineering may make future cardiac monitoring more informative and less vulnerable to movement.
If larger clinical studies verify its safety, signal quality, and diagnostic usefulness, this approach could eventually contribute to more practical and customized remote cardiac care.
References
- Chen, S., et al. (2025). Starfish-Inspired Wearable Bioelectronic Systems for Physiological Signal Monitoring During Motion and Real-Time Heart Disease Diagnosis. Science Advances, 11(14), eadv2406.
https://doi.org/10.1126/sciadv.adv2406 - PubMed Central (2025). Full Open-Access Research Article: Starfish-Inspired Wearable Bioelectronic Systems.
https://pmc.ncbi.nlm.nih.gov/articles/PMC11963991/ - University of Missouri (April 2, 2025). Scientists Unveil Starfish-Inspired Wearable Tech for Heart Monitoring.
https://showme.missouri.edu/2025/scientists-unveil-starfish-inspired-wearable-tech-for-heart-monitoring/ - University of Missouri (April 3, 2026). Multimodal, Porous, Starfish-Like Soft Wearable Bioelectronic Systems for Motion-Artifact-Resilient, Real-Time and High-Accuracy Cardiac Monitoring. Human research listing.
https://studies.orcsapps.missouri.edu/studies/208 - PubMed (2025). Bibliographic Record for the First Science Advances Study. PMID: 40173233.
https://pubmed.ncbi.nlm.nih.gov/40173233/ - World Health Organization. Cardiovascular Diseases: Health Information and Prevention.
https://www.who.int/health-topics/cardiovascular-diseases - American Heart Association. What Is Atrial Fibrillation?
https://www.heart.org/en/health-topics/atrial-fibrillation/what-is-atrial-fibrillation-afib-or-af - American Heart Association. Heart Attack: Symptoms and Warning Signs.
https://www.heart.org/en/health-topics/heart-attack/warning-signs-of-a-heart-attack