Using frequency analysis to improve the precision of human body posture algorithms based on Kalman filters

Area of Science:

• Biomedical Engineering
• Human Motion Analysis
• Sensor Technology

Background:

• Miniaturized inertial sensors are increasingly used for human motion and posture analysis in medicine.
• Kalman Filters are commonly employed to process sensor data for body part orientation estimation.
• Fixed parameters in traditional Kalman Filters can limit performance across different motion intensities.

Purpose of the Study:

• To improve the precision of human body orientation estimation algorithms.
• To provide physicians with more reliable and objective data for clinical practice.
• To adapt Kalman Filter parameters dynamically based on motion intensity.

Main Methods:

• Utilized frequency analysis to quantify motion intensity.
• Dynamically adjusted Kalman Filter parameters (process and observation noise variances).
• Evaluated the impact of parameter adaptation on orientation estimation accuracy.

Main Results:

• Demonstrated that optimal Kalman Filter parameters vary significantly with motion intensity.
• Showed that adapting parameters based on frequency analysis improves orientation estimation precision.
• Achieved more reliable objective data for medical applications.

Conclusions:

• Adaptive Kalman Filtering based on motion intensity enhances human motion analysis.
• Frequency analysis is a viable method for determining motion intensity for filter adaptation.
• Improved orientation estimation accuracy supports better clinical decision-making in medicine.