Biomimetic Five-Armed Wearable Sensor Achieves High-Fidelity Heart Monitoring During Movement

Engineers at the University of Missouri have developed an innovative wearable heart monitor inspired by starfish biology. This device addresses a key limitation in current medical wearable technology: maintaining accurate readings during physical activity.

The device, described in a recent paper published in Science Advances, features a pentaradial design with five flexible arms that create multiple contact points with the skin near the heart. This configuration allows for more stable monitoring than traditional single-contact wearables like smartwatches.

“Similar to a starfish, our device has five arms, each equipped with sensors that simultaneously capture both electrical and mechanical heart activity,” explained Sicheng Chen, a postdoctoral fellow and lead author of the study. “Most current devices focus on capturing only one signal or require separate devices to track multiple signals simultaneously.”

Engineering Design Based on Natural Mechanics

The team’s approach was directly inspired by studying how starfish flip themselves over in nature. When a starfish needs to reorient itself, it coordinates its five arms strategically—contracting one arm to pivot while using the others to adjust its position.

Finite element analysis simulations demonstrated that this five-arm configuration significantly reduces mechanical interference between sensing elements. The pentaradial design achieved a stress coupling coefficient of only 15.7%, compared to 73.1% in traditional monolithic wearable designs, 35.6% in four-arm designs, and 23.9% in six-arm configurations.

This lower coupling means that movement of one part of the device is less likely to disrupt readings from other parts—a critical advantage for maintaining signal quality during physical activity.

Technical Components and Multi-Signal Acquisition

The starfish-shaped device is built on a 25-μm-thick polyimide substrate with copper traces serving as conductive pathways. Each of the five arms contains serpentine structures that enhance flexibility and maintain skin contact during movement.

The device captures three types of cardiac signals simultaneously:

• Electrocardiogram (ECG): Electrical heart signals
• Seismocardiogram (SCG): Translational chest vibrations caused by heartbeats
• Gyrocardiogram (GCG): Rotational chest movements during cardiac cycles

Each sensing pad has a gold-plated copper electrode on the backside for ECG recording, while high-performance commercial accelerometer-gyroscope units (BMI270) capture the mechanical signals. The skin interface uses conductive bagels for the sensing pads and non-conductive bagels for the central hub, while the arms remain free-standing.

The system operates at a sampling rate of 200 Hz across all channels, with data processed by a 32-bit microcontroller before wireless transmission. Despite weighing only 1.7 grams (including battery), the device maintains robust functionality during various physical activities.

Advanced Signal Processing and Motion Artifact Reduction

A significant innovation in this system is its approach to processing signals during movement. The device employs two complementary strategies to maintain signal quality:

First, it uses signal compensation. Four of the sensing pads primarily monitor human motion, while the fifth pad (positioned near the heart) captures both cardiac signals and motion artifacts. Vector synthesis methods predict motion components from the four peripheral pads, which are then subtracted from the central pad’s signal.

Second, the system employs machine learning to recognize motion states and adaptively filter the signals. Artificial intelligence classifies activity as sitting, walking, jogging, or running and then applies tailored filters to optimize signal clarity for each condition.

“This is also a benefit over traditional clinical heart tests such as the Doppler ultrasound, which usually requires patients to stay still to get accurate results,” Chen noted.

Testing and Clinical Applications

The researchers tested the device on both healthy subjects and patients with various heart conditions, including atrial fibrillation, myocardial infarction, and heart failure. The system demonstrated consistent performance across different activity levels, from sitting to running.

Using all three signal types (ECG, SCG, and GCG) as inputs to machine learning models significantly improved diagnostic accuracy compared to individual signals. With the combined inputs, the system correctly identified heart conditions with over 91% accuracy.

The recorded data can be analyzed to extract critical cardiac parameters, including:

• Electromechanical delay (EMD): The timing offset between electrical and mechanical peaks
• Pre-ejection period (PEP): How quickly the heart initiates mechanical action after electrical activity
• Left ventricular ejection time (LVET): Duration of blood ejection from the left ventricle

These parameters provide deeper insights into heart function than ECG readings alone, potentially enabling earlier detection of cardiac abnormalities.

Future Development

While demonstrating significant advantages over existing wearables, the current prototype still has limitations the team is addressing.

“A big challenge with wearable devices is that they can cause skin irritation when worn for long periods,” said Zheng Yan, an associate professor at Mizzou’s College of Engineering who led the research.

The team is developing more comfortable, skin-friendly materials for longer-term wear. The device adheres to the skin using a special gel, but future versions will use breathable materials to reduce irritation. The prototype can be charged wirelessly while still being worn, ensuring continuous monitoring without removal.

The researchers envision this technology expanding beyond cardiac monitoring to track other physiological signals, potentially creating more comprehensive health monitoring systems for preventative care.

This research effectively combines biomimicry, flexible electronics, and machine learning to overcome longstanding limitations in wearable medical devices. More accurate data collection during daily activities could potentially improve the early detection and management of heart conditions.