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Scientists Discover Fish Muscles Can Sense Water Not Just Power Swimming Could Lead To More Adaptive Underwater Robots

In the quiet depths of rivers and oceans, fish glide effortlessly through water, navigating currents, obstacles, and turbulence with remarkable precision. For years, scientists believed that fish muscles were simply engines of movement—structures designed to generate force and enable swimming. But new research is now challenging that idea in a profound way.

A team of researchers from the Peking University has uncovered something far more fascinating: fish muscles are not just motors—they are also sensors. This discovery is reshaping how we understand aquatic life and opening exciting possibilities for the future of underwater robotics.

Led by Professor Xie Guangming at the Intelligent Biomimetic Design Lab, along with researchers Waqar Hussain Afridi and Rahdar Hussain Afridi, the team conducted a series of groundbreaking studies. Their work reveals how electrical signals from fish muscles can decode movement, sense environmental conditions, and even guide the design of intelligent robotic systems.

From Movement to Meaning: Decoding Fish Muscle Signals

The first study, published in Advanced Intelligent Systems, explored how muscle activity relates to movement and surrounding water conditions.

To investigate this, researchers developed a specialized 16-channel device capable of recording electromyography (EMG) signals directly from fish muscles. EMG measures the electrical activity produced when muscles contract. By combining these signals with high-speed motion tracking, the team created a detailed picture of how fish move in different flow environments.

They then used a deep neural network to analyze the data. The results were striking.

The system could:

  • Reconstruct the fish’s body posture with high accuracy

  • Predict joint angles during swimming

  • Identify water flow conditions, such as smooth (laminar) flow and swirling vortex patterns

  • Estimate swimming speed

This means that muscle signals alone contain rich information—not just about movement, but also about the environment.

In simple terms, by “listening” to the electrical activity of muscles, scientists could tell how a fish is moving and what kind of water it is swimming through.

A Surprising Discovery: Muscles That Can Sense

The second study, published in Proceedings of the Royal Society B, took this idea even further.

The researchers wanted to answer an intriguing question:
Can fish muscles actually sense the environment, not just respond to it?

To test this, they studied koi and carp swimming in two types of water conditions:

  • Smooth, steady flow

  • Turbulent flow with swirling patterns known as Kármán vortices

In smooth water, the results were expected. Muscle activity occurred before body movement, meaning the muscles were actively driving motion.

But in turbulent water, something unexpected happened.

In some cases, the sequence reversed:

  • The fish’s body moved first due to external water forces

  • Muscle activation followed afterward

This suggests that the fish’s body is being influenced by the environment first, and then the muscles respond.

In other words, the muscles are not just controlling movement—they are also detecting changes in the surrounding flow.

This phenomenon is known as muscle proprioception, where muscles provide feedback about the body’s position and external forces. The findings strongly support the idea that fish muscles act as both:

  • Actuators (producing movement)

  • Sensors (detecting environmental changes)

This dual function is incredibly efficient and could inspire new designs in engineering.

From Biology to Machines: Building Smarter Robotic Fish

The third study, also published in Advanced Intelligent Systems, focused on applying these biological insights to robotics.

Instead of simply copying the shape of fish, the researchers aimed to replicate something deeper: the sensorimotor intelligence that allows fish to adapt so effectively to their environment.

They developed an interpretable system identification model, trained using synchronized data from real fish—both muscle signals and motion patterns.

This model captured key relationships between muscle activity and tail movement. It also extracted important physical parameters, including:

  • Delay (timing between signal and motion)

  • Gain (strength of response)

  • Damping (resistance to motion)

  • Natural frequency (oscillation behavior)

What makes this achievement remarkable is what happened next.

The model, trained only on biological data, was applied to a robotic fish.

Without any additional retraining, it successfully:

  • Predicted the robot’s tail motion

  • Controlled movement accurately

  • Outperformed traditional deep learning models

This demonstrates a powerful concept: biological intelligence can be directly transferred to machines.

Why This Research Matters

These three studies together represent a major shift in how scientists think about both biology and robotics.

1. A New Way to Study Animals

Instead of relying only on visual observation, researchers can now use muscle signals to understand behavior and environment. This opens new possibilities in:

  • Biological telemetry

  • Animal behavior research

  • Environmental monitoring

2. Smarter Underwater Robots

Most underwater robots today rely on external sensors and rigid control systems. By learning from fish, future robots could:

  • Adapt naturally to changing water conditions

  • Use fewer sensors

  • Move more efficiently

This could be especially useful in:

  • Ocean exploration

  • Environmental protection

  • Search and rescue missions

3. Energy Efficiency and Design Innovation

Fish are extremely energy-efficient swimmers. Understanding how their muscles combine sensing and movement could help engineers design systems that:

  • Consume less power

  • Respond faster to environmental changes

  • Operate more autonomously

A New Paradigm: Intelligence from Within

One of the most important ideas to emerge from this research is that intelligence in biological systems is not just in the brain.

In fish, intelligence is distributed throughout the body. Muscles themselves play an active role in sensing and decision-making.

This challenges the traditional engineering approach, where sensing, processing, and action are separate components.

Instead, the future may lie in systems where:

  • Sensing and action are integrated

  • Materials themselves carry intelligence

  • Machines behave more like living organisms

Looking Ahead

The work from Peking University is just the beginning. As researchers continue to explore the hidden capabilities of biological systems, we can expect even more breakthroughs at the intersection of biology and technology.

Imagine underwater robots that:

  • Swim like real fish

  • Feel currents without sensors

  • Adapt instantly to their environment

Or medical devices that use similar principles to sense and respond inside the human body.

What once seemed like science fiction is quickly becoming reality.

Conclusion

Fish muscles are no longer seen as simple engines of motion. They are complex systems capable of sensing, adapting, and guiding behavior in ways we are only beginning to understand.

By decoding these natural mechanisms, scientists are not just learning about life underwater—they are building the foundation for a new generation of intelligent machines.

This research reminds us of a powerful truth:
Nature is not just something to observe—it is something to learn from.

References: (1) Rahdar Hussain Afridi et al. EMG‐Driven Telemetry and Inference System for Fish: Pose Reconstruction and Flow Sensing, Advanced Intelligent Systems (2026). DOI: https://doi.org/10.1002/aisy.202501085  (2) Rahdar Hussain Afridi et al. Beyond propulsion: muscle proprioception enables hydrodynamic sensing in fish body, Proceedings of the Royal Society B: Biological Sciences (2025). DOI: https://doi.org/10.1098/rspb.2025.0474 (3) Waqar Hussain Afridi et al, Bio‐to‐Robot Transfer of Fish Sensorimotor Dynamics via Interpretable Model, Advanced Intelligent Systems (2025). DOI: 10.1002/aisy.202501117

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