Park Myung Woo, Chung Sun Gun, Beom Jaewon, Kim Kyung Su, Kim Joonghee, Park Chul-Hyun, Lee Jinkyu, Kim Keewon
Department of Rehabilitation Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea.
Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea.
BMC Geriatr. 2025 Jul 5;25(1):497. doi: 10.1186/s12877-025-06064-8.
Gait speed indicates morbidity and life expectancy in older adults, but the minimum walking distance for measurement remains unclear. This study aimed to determine the minimum distance required to measure clinically reliable gait speed using a smartphone camera and pose estimation, and whether the distance is influenced by subject characteristics or measurement methods.
Twenty-four healthy volunteers (≥ 65 years old) performed a video-recorded 10-m gait test, including acceleration and deceleration. Fourteen body points were derived using a pose-estimation algorithm. Speed was calculated based on the center of mass or a leading foot which simulates a condition with a walkway sensor and validated against manual measurements. Multiple videos of gait over varying distances were obtained by cropping video frames at 0.1-m intervals. Variance in gait speed over specific distances was calculated using ANOVA. "Minimum required distance" was defined as the shortest distance where the confidence interval of gait speed did not exceed the minimal clinically important difference (0.1 m/sec). We also investigated which clinical, anthropometric, or epidemiological variables might influence it by assessing their association with gait speed variance using mean squared error from linear regression.
Gait speed measured by pose estimation (1.55 ± 0.18 m/s) showed a high level of agreement with manual measurement (1.56 ± 0.14 m/s), with an intraclass correlation coefficient of 0.889 (95% CI: 0.822-0.931). "Minimum required distance" was 2.1 m when gait speed was calculated with the center of mass with 95% confidence interval while "minimun required distance" based on a leading foot was 4.7 m with 90% confidence interval. Gait speed itself and muscle strength were positively correlated with gait speed variance (r = 0.250, p = 0.036 for gait speed; r = 0.312, p = 0.008 for knee extension strength; r = 0.230, p = 0.053 for grip strength), whereas other epidemiologic or clinical parameters, and physical performance scales, were not.
Clinically reliable measurement of gait speed could be achieved over 2.1 m using a smartphone and pose estimation inducing the center of mass with 95% confidence interval. The weaker the muscle strength or the slower the gait speed, the shorter the distance might be required.
步速可反映老年人的发病率和预期寿命，但测量所需的最小步行距离仍不明确。本研究旨在确定使用智能手机摄像头和姿势估计来测量临床可靠步速所需的最小距离，以及该距离是否受受试者特征或测量方法的影响。
24名健康志愿者（≥65岁）进行了一段10米的步态测试视频录制，包括加速和减速过程。使用姿势估计算法得出14个身体点。根据质心或领先脚计算速度，模拟使用人行道传感器的情况，并与手动测量结果进行验证。通过以0.1米的间隔裁剪视频帧，获得了不同距离的多个步态视频。使用方差分析计算特定距离内步速的方差。“最小所需距离”定义为步速置信区间不超过最小临床重要差异（0.1米/秒）的最短距离。我们还通过使用线性回归的均方误差评估其与步速方差的关联，研究了哪些临床、人体测量或流行病学变量可能会影响它。
通过姿势估计测量的步速（1.55±0.18米/秒）与手动测量（1.56±0.14米/秒）高度一致，组内相关系数为0.889（95%CI：0.822 - 0.931）。当用质心计算步速时，“最小所需距离”为2.1米，置信区间为95%；而基于领先脚的“最小所需距离”为4.7米，置信区间为90%。步速本身和肌肉力量与步速方差呈正相关（步速：r = 0.250，p = 0.036；膝关节伸展力量：r = 0.312，p = 0.008；握力：r = 0.230，p = 0.053），而其他流行病学或临床参数以及身体表现量表则不然。
使用智能手机和姿势估计，通过质心计算，在2.1米以上的距离可以实现临床可靠的步速测量，置信区间为95%。肌肉力量越弱或步速越慢，所需的距离可能越短。
