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Annals of Geriatric Medicine and Research  
Cross-Comparisons of Gait Speeds by Automatic Sensors and a Stopwatch to Provide Converting Formula Between Measuring Modalities
Hee-Won Jung1, Hyun-Chul Roh2, Sun-wook Kim3, Sunyoung Kim4, Miji Kim5, Chang Won Won4
1Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
2Dyphi Research Institute, Dyphi Inc., Daejeon, Korea
3Internal Medicine, Ollin Clinic, Seongnam, Korea
4Department of Family Medicine, Kyung Hee University Medical Center, College of Medicine, Kyung Hee University, Seoul, Korea
5Department of Biomedical Science and Technology, College of Medicine, East-West Medical Research Institute, Kyoung Hee University, Seoul, Korea
Correspondence to: Corresponding Author:
Chang Won Won, MD, PhD
Department of Family Medicine, Kyung Hee University Medical Center, 23 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Korea
E-mail: chunwon62@naver.com
Received: April 3, 2019; Revised: April 29, 2019; Accepted: April 30, 2019; Published online: May 15, 2019.
© The Korean Geriatrics Society. All rights reserved.

This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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. Conclusions: 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: Walking speed, Screening, Diagnosis, Data analysis

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