Gait Event Detection During Running Using a Bending Angle Sensor Attached to Loose-Fitting Clothing

Area of Science:

• Biomechanics
• Sports Science
• Wearable Technology

Background:

• Accurate measurement of running parameters like cycle time (t_g) and contact time (t_c) is crucial for performance analysis and injury prevention.
• Traditional methods such as 3D motion capture are accurate but expensive and cumbersome.
• Wearable sensors offer a potential solution for accessible and convenient gait analysis.

Purpose of the Study:

• To evaluate the accuracy of a bending angle sensor integrated into loose-fitting clothing for measuring running cycle time (t_g) and contact time (t_c).
• To compare the sensor's measurements against gold-standard 3D motion capture data.
• To determine the reliability of the sensor across different running speeds.

Main Methods:

• Twenty healthy adult males ran on a treadmill at 8, 10, and 12 km/h.
• A bending angle sensor was attached to the right knee of loose-fitting pants.
• Simultaneous measurements were taken using the sensor and a 3D motion capture system.
• Knee joint angular acceleration was used to detect foot-strike and toe-off events to calculate t_g and t_c.

Main Results:

• The bending angle sensor demonstrated high accuracy for detecting t_g, with all error metrics (MAE, MBE, RMSE) within 10 ms.
• For t_c, the root mean square error (RMSE) was 21.4 ms at 8 km/h and below 20 ms at 10 and 12 km/h.
• The sensor provided reliable measurements across the tested running speeds.

Conclusions:

• A bending angle sensor attached to loose-fitting clothing can accurately detect running cycle time (t_g) and contact time (t_c).
• This technology presents a practical and accessible method for gait analysis outside of laboratory settings.
• The findings support the use of wearable sensors for real-world running performance monitoring.