Using force data to self-pace an instrumented treadmill and measure self-selected walking speed

Area of Science:

• Biomechanics
• Human locomotion
• Rehabilitation engineering

Background:

• Self-selected walking speed is a key functional measure.
• Treadmill laboratories require reliable methods to assess self-selected speed without extra hardware.

Purpose of the Study:

• To adapt and validate a self-pacing controller for force-instrumented treadmills to measure self-selected walking speeds.
• To compare the accuracy and efficiency of the self-pacing controller against traditional methods.

Main Methods:

• Adapted a self-pacing controller for force-instrumented treadmills.
• Validated speed and position estimation against motion capture data.
• Compared self-selected speeds from self-paced treadmill tests with standard walk tests in 10 healthy adults.

Main Results:

• The self-pacing algorithm accurately estimated subject speed and position (RMSD < 0.023 m/s for speed).
• Self-selected speeds from self-paced tests strongly correlated with the 10-meter walk test (R>0.93).
• Self-paced tests were faster and comparable in comfort to manual selection methods.

Conclusions:

• The self-paced force-instrumented treadmill reliably measures self-selected walking speeds.
• This system provides a viable alternative to manual treadmill speed selection for gait researchers and clinicians.
• The self-pacing software is provided to aid broader adoption.