Design and Evaluation of a Conductive-Knit Sensor System for Measuring Forearm Pronation and Supination

Area of Science:

• Biomedical Engineering
• Materials Science
• Textile Engineering

Background:

• Continuous monitoring of human motion in daily life is enabled by wearable sensors.
• Assessing forearm pronation and supination typically requires specialized laboratory equipment.
• Existing methods lack unobtrusiveness and comfort for long-term use.

Purpose of the Study:

• To design and evaluate a knitted textile sensor system for unobtrusive measurement of rotational forearm movements.
• To overcome hysteresis issues in knitted strain sensors for reliable rotational angle estimation.
• To provide a comfortable, user-friendly alternative to laboratory-based motion analysis.

Main Methods:

• Developed a knitted textile sensor system using carbon nanotube (CNT) conductive yarns.
• Implemented a dual-sensor configuration (positioned at 45°) to mitigate hysteresis effects.
• Utilized nonlinear (cubic) regression models for accurate rotational angle estimation.
• Validated the system against gyroscope measurements for offline and real-time analysis.

Main Results:

• The CNT-knitted sensor system demonstrated consistent strain resistance during elongation.
• Nonlinear models significantly improved accuracy compared to linear models.
• Achieved root-mean-square errors of ~2.5° (offline) and ~5° (real-time) compared to a gyroscope.
• The textile-based system offered breathability, flexibility, and comfort without skin attachment.

Conclusions:

• CNT-knitted sensors provide a feasible, lightweight, and cost-effective platform for measuring forearm rotation.
• The system enables unobtrusive monitoring of complex upper limb movements.
• Promising applications include rehabilitation monitoring, sports performance assessment, and healthcare technologies.