Human Motion Recognition of Knitted Flexible Sensor in Walking Cycle

Area of Science:

• Textile Engineering
• Biomedical Engineering
• Wearable Technology

Background:

• Knitted fabric sensors offer large strain performance suitable for monitoring human body movements in sports health.
• Fabric deformation during movement alters electrical signals, enabling motion tracking.
• Highly elastic knitted materials present an opportunity for advanced wearable motion sensors.

Purpose of the Study:

• To evaluate the mechanical and electrical properties of highly elastic knitted sweatpants as strain sensors under large strain conditions.
• To assess the sensor's sensitivity, stability, and accuracy in distinguishing gait cycles and predicting motion postures.
• To demonstrate the feasibility of using knitted fabric sensors for real-time motion monitoring.

Main Methods:

• Mechanical and electrical properties of knitted sweatpants were tested under large strain.
• Gait cycles were analyzed by dividing walking into stance and swing phases with six subdivisions.
• Sensor outputs were transmitted via Bluetooth 4.0 to a computer for analysis using a rule-based algorithm in Matlab.

Main Results:

• The knitted fabric sensor demonstrated good sensitivity and stability during various movements.
• The sensor accurately distinguished gait cycles based on resistance characteristic values.
• Hysteresis and repeatability analyses confirmed constant electrical performance.
• Four motion postures were successfully predicted and identified using resistance values, peak calculations, and time-axis analysis.

Conclusions:

• Highly elastic knitted sweatpants can serve as effective strain sensors for sports health applications.
• The developed sensor system accurately monitors human motion and gait cycles with reliable electrical performance.
• This technology offers a promising alternative to traditional motion monitoring systems for wearable applications.