A Practical Sensor-to-Segment Calibration Method for Upper Limb Inertial Motion Capture in a Clinical Setting.

Area of Science:

• Biomechanics and Movement Science
• Biomedical Engineering
• Clinical Motion Analysis

Background:

• Inertial sensors offer potential for clinical human motion capture outside research labs.
• A key limitation is the lack of standardized calibration methods, especially for sensor-to-segment alignment.
• Accurate rotational offset calculation is crucial for reliable inertial motion capture.

Purpose of the Study:

• To develop and validate a practical sensor-to-segment calibration method for upper limb motion capture.
• The method aims to be suitable for clinical applications, overcoming current barriers.
• Focus on a calibration technique that is easy to implement in clinical environments.

Main Methods:

• Developed a calibration method utilizing joint axis estimation from arbitrary elbow motion.
• Incorporated custom attachment mounts for improved physical alignment of inertial sensors.
• Validated the method with twenty healthy participants using OpenSim's inertial sensor workflow, comparing results against optical motion capture.

Main Results:

• The novel calibration method achieved median Root Mean Square (RMS) errors of 5-8° for upper limb kinematics.
• Median correlation coefficients between 0.977-0.987 demonstrated high agreement with optical motion capture.
• Significantly higher accuracy was observed compared to static pose calibration (p < 0.001).

Conclusions:

• The developed calibration method is practical for clinical settings due to its speed and ease of use.
• It does not rely on specific subject movements or precise operator sensor placement.
• This method represents a realistic advancement for integrating inertial sensor technology into routine clinical practice.