Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach.

Area of Science:

• Biomechanics
• Rehabilitation Engineering
• Wearable Technology

Background:

• Human movement analysis is crucial in biomechanics and rehabilitation.
• Wearable devices offer versatile indoor and outdoor biomedical applications.
• Existing systems for trunk motion analysis have limitations.

Purpose of the Study:

• To design and develop a novel Trunk Motion System (TMS) using printed Body-Worn Sensors (BWS).
• To measure three-dimensional (3D) trunk motions accurately using a lightweight, portable, and non-invasive system.
• To validate the TMS system's performance across various planar and multiplanar movements.

Main Methods:

• Twelve BWSs were printed on stretchable clothing for 3D trunk movement measurement.
• A neural network data fusion algorithm integrated BWS data.
• The TMS was calibrated using data from the neural network and a Qualisys motion capture system.
• Three healthy participants performed seven different movement tasks.

Main Results:

• The TMS achieved high accuracy, with errors less than 1.0° for flexion/extension and lateral bending.
• Axial rotation and multiplanar motions showed accuracy below 1.3°.
• Overall accuracy for identified movements was less than 2.7°.

Conclusions:

• The developed TMS provides accurate and reliable measurement of 3D trunk orientations.
• The system's portability and non-invasive nature make it suitable for diverse clinical, biomechanical, and ergonomic studies.
• TMS can aid in preventing musculoskeletal injuries and evaluating intervention impacts.