Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA.
Department of Biomechanics, University of Nebraska at Omaha, 6001 Dodge St., Omaha, NE 68182, USA.
Sensors (Basel). 2022 Jan 29;22(3):1077. doi: 10.3390/s22031077.
The "total distance walked" obtained during a standardized walking test is an integral component of physical fitness and health status tracking in a range of consumer and clinical applications. Wearable inertial sensors offer the advantages of providing accurate, objective, and reliable measures of gait while streamlining walk test administration. The aim of this study was to develop an inertial sensor-based algorithm to estimate the total distance walked using older subjects with impaired fasting glucose (Study I), and to test the generalizability of the proposed algorithm in patients with Multiple Sclerosis (Study II). All subjects wore two inertial sensors (Opals by Clario-APDM Wearable Technologies) on their feet. The walking distance algorithm was developed based on 108 older adults in Study I performing a 400 m walk test along a 20 m straight walkway. The validity of the algorithm was tested using a 6-minute walk test (6MWT) in two sub-studies of Study II with different lengths of a walkway, 15 m (Study II-A, = 24) and 20 m (Study II-B, = 22), respectively. The start and turn around points were marked with lines on the floor while smaller horizontal lines placed every 1 m served to calculate the manual distance walked (ground truth). The proposed algorithm calculates the forward distance traveled during each step as the change in the horizontal position from each foot-flat period to the subsequent foot-flat period. The total distance walked is then computed as the sum of walk distances for each stride, including turns. The proposed algorithm achieved an average absolute error rate of 1.92% with respect to a fixed 400 m distance for Study I. The same algorithm achieved an absolute error rate of 4.17% and 3.21% with respect to an averaged manual distance for 6MWT in Study II-A and Study II-B, respectively. These results demonstrate the potential of an inertial sensor-based algorithm to estimate a total distance walked with good accuracy with respect to the manual, clinical standard. Further work is needed to test the generalizability of the proposed algorithm with different administrators and populations, as well as larger diverse cohorts.
在一系列消费者和临床应用中，标准化步行测试中获得的“总步行距离”是身体活动能力和健康状况跟踪的一个组成部分。可穿戴惯性传感器在简化步行测试管理的同时，提供了准确、客观和可靠的步态测量优势。本研究的目的是开发一种基于惯性传感器的算法，以估计总步行距离，研究对象为空腹血糖受损的老年人（研究 I），并在多发性硬化症患者中测试该算法的通用性（研究 II）。所有受试者的脚上均佩戴两个惯性传感器（Clario-APDM 可穿戴技术的 Opals）。研究 I 中，108 名老年人在 20 米直道上进行 400 米步行测试，基于此建立了步行距离算法。在研究 II 的两个子研究中，使用不同长度的步行道（15 米，Study II-A，n=24）和 20 米（Study II-B，n=22）进行 6 分钟步行测试（6MWT）来测试算法的有效性。在地面上标记起始点和转弯点的线，而每隔 1 米放置的较小水平线则用于计算手动行走距离（实际距离）。所提出的算法计算每个步幅中向前行进的距离，方法是从每个足放平期到后续足放平期的水平位置变化。然后，将各步的步行距离相加，计算总步行距离，包括转弯。对于研究 I，所提出的算法相对于固定的 400 米距离的平均绝对误差率为 1.92%。在研究 II-A 和研究 II-B 中，对于 6MWT 的平均手动距离，相同的算法分别实现了 4.17%和 3.21%的绝对误差率。这些结果表明，基于惯性传感器的算法具有良好的准确性，可以估计总步行距离，这是一种基于手动、临床标准的算法。需要进一步研究来测试该算法在不同管理者和人群中的通用性，以及更大的多样化队列。
