Knowledge Resources Why is a low-pass digital filter applied to marker point data during gait analysis? Enhance Your Biomechanical Accuracy
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Tech Team · 3515

Updated 1 week ago

Why is a low-pass digital filter applied to marker point data during gait analysis? Enhance Your Biomechanical Accuracy


The primary purpose of applying a low-pass digital filter during gait analysis is to separate true human motion from the electronic noise and high-frequency vibrations inherent in motion capture systems. By isolating low-frequency signals—which represent actual limb movement—researchers can generate smooth kinematic curves and ensure the accuracy of complex calculations.

Core Takeaway A low-pass filter acts as a critical gatekeeper, removing non-physical artifacts while preserving the data that represents natural movement. Without this step, high-frequency noise would dramatically distort derived metrics like velocity and acceleration, rendering the analysis unreliable.

Distinguishing Signal from Noise

The Frequency of Human Movement

Human gait is biomechanically constrained. The physical movement of limbs occurs at relatively low frequencies, meaning the changes in position happen smoothly and gradually over time.

The Nature of Motion Capture Artifacts

Conversely, raw data from motion capture cameras often contains "jitter" or high-frequency vibrations. These are not physical movements made by the subject but are artifacts generated by the capture hardware or marker vibration.

The Role of the Cutoff Frequency

A low-pass filter works by setting a specific threshold, known as the cutoff frequency. For general gait analysis, a cutoff of approximately 7 Hz is commonly used. This allows the slower, genuine movement signals to pass through while blocking the rapid, non-physical noise.

The Impact on Derived Parameters

Smoothing Kinematic Curves

Raw trajectory data is often jagged due to noise. Filtering creates smooth kinematic curves, allowing for a clearer visual and mathematical representation of the subject's path of motion.

Improving Velocity Calculations

Velocity is calculated by determining how quickly position changes. If the position data is noisy, the calculated velocity will fluctuate wildly. Filtering ensures the velocity profile reflects the subject's actual speed, not system error.

Stabilizing Acceleration Data

Acceleration is the second derivative of position, meaning it is highly sensitive to noise. Even microscopic errors in position data can result in massive, unrealistic spikes in acceleration. Filtering is absolutely essential to prevent these calculation errors.

Understanding the Trade-offs

The Risk of Over-Filtering

If the cutoff frequency is set too low (e.g., significantly below 7 Hz), you risk oversmoothing the data. This removes not just the noise, but also legitimate rapid movements, such as the initial impact of a heel strike.

The Risk of Under-Filtering

If the cutoff frequency is set too high, significant noise retention occurs. While the position data may look acceptable, the derived acceleration data will remain too noisy to be useful for dynamic analysis.

Ensuring Precision in Your Analysis

To ensure your gait analysis provides actionable insights, you must balance signal retention with noise reduction.

  • If your primary focus is Visual Trajectory: A standard low-pass filter ensures smooth animations and clean graphs for presentation.
  • If your primary focus is Joint Forces and Acceleration: Strict filtering is non-negotiable, as noise amplification in these derivatives will invalidate your results.

Effective filtering transforms raw, noisy coordinates into a reliable digital twin of human motion.

Summary Table:

Feature Signal (Human Movement) Noise (System Artifacts)
Frequency Range Low Frequency (typically < 7 Hz) High Frequency
Physical Source Actual limb and body motion Electronic jitter, marker vibration
Data Impact Represents true kinematic paths Distorts velocity and acceleration
Filter Action Passed through the threshold Blocked/Attenuated

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References

  1. Takuo Negishi, Naomichi Ogihara. Functional significance of vertical free moment for generation of human bipedal walking. DOI: 10.1038/s41598-023-34153-4

This article is also based on technical information from 3515 Knowledge Base .

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