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Estimating Walking Speed in the Wild.

Loubna Baroudi¹, Mark W Newman², Elizabeth A Jackson³

¹Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, United States.

Frontiers in Sports and Active Living

|December 21, 2020

Summary

This summary is machine-generated.

This study introduces a new method using a thigh-worn sensor to accurately estimate walking speed in daily life. This technology helps clinicians monitor patient physical activity and health performance outside the clinic.

Keywords:

free-living gait analysis gait speed physical activity wearable sensors

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Area of Science:

Biomedical Engineering
Kinesiology
Wearable Technology

Background:

Physical activity is vital for preventing and recovering from diseases, especially cardiovascular conditions.
Accurate measurement of daily physical activity, including walking speed, is crucial for clinical assessment.
Current clinical walking tests are limited by their short duration and artificial environment.

Purpose of the Study:

To develop a method for accurately estimating free-living gait speed using low-power wearable sensors.
To bridge the gap between clinical assessments and real-world patient movement data.
To enable continuous, long-term monitoring of walking intensity and duration.

Main Methods:

Utilized a low-power, accelerometer-based bio-logging tag worn on the thigh.
Developed subject-specific, data-driven models to map stride frequency to stride speed using high-resolution gait kinematics.
Validated the method against a foot-worn inertial measurement unit during a long outdoor walk.

Main Results:

Achieved an average concordance correlation coefficient of 0.80 for gait speed estimation across subjects.
97% of the estimated stride speed errors were within ±0.2 m/s.
Demonstrated accurate gait speed estimation in a free-living environment.

Conclusions:

The proposed method enables accurate, week-timescale estimation of free-living walking speed.
This technology can provide clinicians with valuable data on patient activity levels and fitness.
Offers a promising approach to complement traditional clinical assessments with real-world movement insights.