The primary purpose of applying a fourth-order low-pass Butterworth filter is to rigorously clean raw sensor data by stripping away high-frequency interference while protecting the genuine signals of human motion. This processing step is essential for converting noisy, raw measurements into precise biomechanical datasets used for analysis.
Raw data from motion and force sensors is rarely usable in its initial state due to environmental and mechanical interference. This filter bridges the gap between raw input and actionable insight, ensuring that critical metrics like range of motion and angular velocity are based on biological reality rather than signal artifacts.
The Challenge of Raw Data
Sources of Signal Interference
Raw data collected from force and motion sensors is inherently "dirty." It often contains high-frequency noise that distorts the true signal.
Identifying the Culprits
This noise is typically generated by non-biological sources. Common culprits include electrical signals, mechanical vibrations inherent to the testing equipment, and irrelevant micro-movements.
Why the Fourth-Order Low-Pass Butterworth Filter is Used
Eliminating the Noise
The "low-pass" nature of this filter allows it to specifically target and effectively eliminate the high-frequency noise identified above. By cutting off frequencies above a certain threshold, it removes the erratic data points caused by vibrations and electrical interference.
Preserving Signal Integrity
While removing noise is critical, it must be done without degrading the data that matters. The filter is designed to preserve the integrity of mid-to-low frequency signals. These frequencies are where meaningful human movement data resides.
Impact on Biomechanical Analysis
Ensuring Calculation Accuracy
Biomechanical testing relies on derived calculations, such as kinetic indicators. If the underlying data is noisy, calculations for metrics like range of motion (ROM) and angular velocity will be incorrect.
Evaluating Product Performance
The ultimate goal of this data processing is to support high-stakes evaluations. Accurate data is non-negotiable when assessing the performance and safety of footwear products.
Understanding the Trade-offs
Balancing Purity and Reality
The application of this filter represents a necessary balance in data processing. The objective is to remove enough noise to make the data usable, but not so much that you alter the characteristics of the human movement.
The Risk of Raw Data
Using data without this specific filtering process introduces a high risk of error. You would be analyzing artifacts of the machine rather than the mechanics of the body, rendering safety and performance conclusions invalid.
Making the Right Choice for Your Goal
To ensure your biomechanical testing yields valid results, consider the following:
- If your primary focus is Data Accuracy: Apply this filter to remove electrical and mechanical artifacts, ensuring your kinetic indicators reflect true human movement.
- If your primary focus is Product Evaluation: meaningful assessments of footwear safety and performance rely entirely on the integrity of the mid-to-low frequency signals preserved by this process.
Clean data is the only foundation upon which reliable biomechanical conclusions can be built.
Summary Table:
| Feature | Purpose in Biomechanical Testing |
|---|---|
| Filter Type | Fourth-Order Low-Pass Butterworth |
| Primary Goal | Remove high-frequency noise while preserving biological signals |
| Noise Sources | Electrical interference, mechanical vibrations, micro-movements |
| Key Metrics Protected | Range of Motion (ROM), Angular Velocity, Kinetic Indicators |
| Output Benefit | Clean data for reliable footwear safety and performance evaluation |
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References
- Xiaohan Xu, Genevieve Williams. Kinematics of balance controls in people with chronic ankle instability during unilateral stance on a moving platform. DOI: 10.1038/s41598-025-85220-x
This article is also based on technical information from 3515 Knowledge Base .
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