Cross-Comparisons of Gait Speeds by Automatic Sensors and a Stopwatch to Provide Converting Formula Between Measuring Modalities

Hee-Won Jung; Hyun-Chul Roh; Sun-Wook Kim; Sunyoung Kim; Miji Kim; Chang Won Won

doi:10.4235/agmr.19.0016

Cross-Comparisons of Gait Speeds by Automatic Sensors and a Stopwatch to Provide Converting Formula Between Measuring Modalities

Ann Geriatr Med Res. 2019 Jun;23(2):71-76. doi: 10.4235/agmr.19.0016. Epub 2019 Jun 30.

Authors

Hee-Won Jung¹, Hyun-Chul Roh², Sun-Wook Kim³, Sunyoung Kim⁴, Miji Kim⁵, Chang Won Won⁴

Affiliations

¹ Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
² Dyphi Research Institute, Dyphi Inc., Daejeon, Korea.
³ Internal Medicine, Ollin Clinic, Seongnam, Korea.
⁴ Department of Family Medicine, Kyung Hee University Medical Center, College of Medicine, Kyung Hee University, Seoul, Korea.
⁵ Department of Biomedical Science and Technology, College of Medicine, East-West Medical Research Institute, Kyoung Hee University, Seoul, Korea.

Abstract

Background: We aimed to compare 4 automatic devices with a conventional stopwatch for measuring gait speed.

Methods: We used 4 experimental devices to automatically measure gait speed: 1) Gaitspeedometer (GSM) 1, with laser sensors; 2) GSM2, with ultrasound sensors; 3) GSM3, with infrared sensors; and 4) GSM4, with a light detection and ranging sensor. To assess compatibility between different versions of GSMs, we collected 426 data points from 4 young engineers walking at random speeds and with varying postures. We used these data to convert gait speed measured by GSM1 and 2 for compatibility with GSM3 in the Korean Frailty and Aging Cohort Study (KFACS) dataset.

Results: Mean gait speeds measured with GSMs 1-4 were 1.7% slower (R²=0.997), 12.2% faster (R²=0.993), 1.3% slower (R²=0.999), and 4.3% slower (R²=0.996), respectively, than the gait speed measured with a stopwatch. The concordance correlation coefficient between each GSM and the stopwatch was higher than 0.9. Using linear regression analysis with no constant term, conversion formulas for GSMs were established for the KFACS dataset using GSM1 and GSM2.

Conclusion: The 4 methods of automatic gait speed measurement and the manually measured gait speed correlated well with each other, and we hope these new technologies reduce barriers to measuring older people's gait speed in busy clinical settings.

Keywords: Data analysis; Diagnosis; Screening; Walking speed.