Sensor Fundamentals & Applications: How AI Baseball Turns the Body into Visible Signals
Before we talk about AI and data analysis, there is a more fundamental question:
Where does the data come from? One answer is sensors.
What Is an IMU? One of the Most Common Sensors in Sports Science
One of the most common types is the IMU (Inertial Measurement Unit).
It is a relatively small sensor capable of detecting changes in acceleration and rotational motion. When installed on a bat or worn by an athlete, the previously invisible “process” of movement begins to become a series of measurable digital signals.
What Information Can an IMU Sensor Detect?
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Changes in three-axis acceleration, reflecting movement speed and force
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Changes in three-axis angular velocity, reflecting rotation and orientation
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Movement trajectories and timing sequences that can be estimated through algorithms
Sensor Fundamentals and the Data Acquisition Process
How does a sensor detect movement, and how is the data collected?
Generally, a sensor first captures raw physical signals, then converts them into digital data through internal circuitry before transmitting the data to a backend system for processing and analysis.
This process typically involves signal amplification, noise filtering, and analog-to-digital conversion. Each stage can affect the accuracy of the final data.
The Practical Role of Sensors in AI Baseball Training
In AI Baseball applications, sensors often act as the first-line data collectors.
An IMU installed in the handle of a bat can continuously record changes in acceleration and angular velocity throughout a swing. After these raw signals are processed by algorithms, they can be used to derive key indicators such as swing speed and swing path.
Sensors vs. Imaging Technology: Two Different Ways to Collect Data
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Sensors: Compact, relatively low cost, can be installed directly on equipment or the body, and provide high responsiveness
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Imaging Technology: Does not require equipment to be attached to the athlete, but generally requires more complex computation to reconstruct movement details
In practice, many AI Baseball systems combine sensors and imaging technology, using the strengths of each to obtain more complete and accurate movement data.
Raw Data Is Not the Same as Meaningful Information
Here is a detail that is often overlooked:
Sensor data is only raw data. It is not yet meaningful information.
A collection of acceleration values cannot tell you by itself whether a swing was good or bad. The real value lies in how that data is cleaned, transformed, and interpreted so that coaches and athletes can understand it.
That is why sensor technology often needs to be combined with backend data processing and analysis methods to deliver its full value.
Conclusion
Sensor technology is the first piece of the data foundation supporting Sports Science and AI Baseball.
First understand where the data comes from, then understand how the data can be used.