Shoulder and elbow joint angle tracking with inertial sensors

Area of Science:

• Biomechanics
• Wearable technology
• Robotics

Background:

• Wearable inertial systems enable continuous human movement tracking for assessing physical activity and function.
• Traditional orientation calculation via gyroscope integration suffers from drift error.
• Kalman filters are commonly used to fuse accelerometer and gyroscope data for drift compensation.

Purpose of the Study:

• To develop and validate a novel inertial tracking system for continuous estimation of human shoulder and elbow joint angles.
• To integrate robotic kinematic models with state-space methods for enhanced movement analysis.
• To assess the accuracy and reliability of the proposed system against an optical-tracking reference.

Main Methods:

• Utilized two wearable inertial measurement units to capture kinematic data.
• Combined robotic arm kinematic models with state-space methods for joint angle estimation.
• Implemented a nonlinear state-space inertial tracker using the unscented Kalman filter.

Main Results:

• Achieved root-mean-square angle errors below 8° for shoulder and elbow joints across 8 subjects.
• Demonstrated high agreement with an optical-tracking reference system, with average correlation coefficients (r) ≥ 0.95.
• Validated performance for both regular and fast-speed arm movements.

Conclusions:

• The developed unscented Kalman filter-based inertial tracker provides accurate and reliable estimation of human shoulder and elbow joint angles.
• The system shows potential for continuous monitoring of physical activity and functional ability.
• The methodology is adaptable for tracking other human joint movements.