Human walking in the real world: Interactions between terrain type, gait parameters, and energy expenditure.
Human walking in the real world: Interactions between terrain type, gait parameters, and energy expenditure.
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DOI：
10.1371/journal.pone.0228682
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Kuo AD
中科院分区：
文献类型：
--
作者：
Kowalsky DB;Rebula JR;Ojeda LV;Adamczyk PG;Kuo AD
人类经常穿越具有各种表面不规则性和不一致性的真实世界环境，这可能会扰乱稳定的步态，并需要额外的努力。然而，这种影响几乎没有得到定量的证明，因为很少有实验室的生物力学措施适用于户外。然而，步行可以用其他方法来量化。特别是，脚在太空中的轨迹可以从脚上安装的惯性测量单元(IMU)重建，以产生步幅和相关变量的测量。但目前尚不清楚这些措施是否与新陈代谢能量消耗有关。因此，我们量化了五种不同的户外地形对健康成年人(N=10，以1.25m/S的速度行走)的步行运动(来自IMUS)和净代谢率(来自耗氧量)的影响。能源消耗显著增加(P<；0.05)的顺序是人行道、泥土、砾石、草和木屑，其中木屑比人行道贵约27%。地形类型也影响测量，特别是步幅可变性和虚拟足部净空(摆动脚高于连续脚步的最低高度)。综合起来，这些方法还可以粗略地预测代谢成本(调整后的R2=0.52，偏最小二乘回归)，甚至可以区分地形类型(重新分类误差10%)。人体佩戴的传感器可以表征不平坦的地形如何影响真实世界中的步态、步态可变性和新陈代谢成本。
Humans often traverse real-world environments with a variety of surface irregularities and inconsistencies, which can disrupt steady gait and require additional effort. Such effects have, however, scarcely been demonstrated quantitatively, because few laboratory biomechanical measures apply outdoors. Walking can nevertheless be quantified by other means. In particular, the foot’s trajectory in space can be reconstructed from foot-mounted inertial measurement units (IMUs), to yield measures of stride and associated variabilities. But it remains unknown whether such measures are related to metabolic energy expenditure. We therefore quantified the effect of five different outdoor terrains on foot motion (from IMUs) and net metabolic rate (from oxygen consumption) in healthy adults (N = 10; walking at 1.25 m/s). Energy expenditure increased significantly (P < 0.05) in the order Sidewalk, Dirt, Gravel, Grass, and Woodchips, with Woodchips about 27% costlier than Sidewalk. Terrain type also affected measures, particularly stride variability and virtual foot clearance (swing foot’s lowest height above consecutive footfalls). In combination, such measures can also roughly predict metabolic cost (adjusted R2 = 0.52, partial least squares regression), and even discriminate between terrain types (10% reclassification error). Body-worn sensors can characterize how uneven terrain affects gait, gait variability, and metabolic cost in the real world.
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