Biomechanics, Physical Performance, and Exercise (BioPPEx) Research Group, Macquarie University, Sydney, Australia; Faculty of Medicine, Health, and Human Sciences, Macquarie University, Sydney, Australia.
Neuromuscular Research Lab/Warrior Performance Center, University of Pittsburgh, Pittsburgh, PA, USA; Department of Sports Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Gait Posture. 2024 Sep;113:519-527. doi: 10.1016/j.gaitpost.2024.08.003. Epub 2024 Aug 21.
Despite deleterious biomechanics associated with injury, particularly as it pertains to load carriage, there is limited research on the association between physical demands and variables captured with wearable sensors. While inertial measurement units (IMUs) can be used as surrogate measures of ground reaction force (GRF) variables, it is unclear if these data are sensitive to military-specific task demands.
Can wearable sensors characterise physical load and demands placed on individuals in different load, speed and grade conditions?
Data were collected on 20 individuals who were self-reportedly free from current injury, recreationally active, and capable of donning 23 kg in the form of a weighted vest. Each participant walked and ran on flat, uphill (+6 %) and downhill (-6 %) without and with load (23 kg). Data were collected synchronously from optical motion capture (OMC) and IMUs placed on the distal limb and the pelvis. Data from an 8-second window was used to generate a participant-based mean of OMC and IMU variables of interest. Repeated Measures ANOVA was used to measure main and interaction effects of load, speed, and grade. Simple linear regression was used to elucidate a relationship between OMC measures and estimated metabolic cost (EMC) to IMU measures.
Load reduces foot and pelvic accelerations (p<0.001) but elevate signal attenuation per step (p=0.044). Conversely, attenuation per kilometre is lowered with the addition of load (p=0.017). Uphill had the lowest attenuation per step (p=0.003) and kilometre (p≤0.033) in walking, while downhill had the greatest attenuation per step (p≤0.002) and per kilometre (p≤0.004). Attenuation measures are inconsistently moderately related to limb negative work (R≤0.57). EMC is moderately positively related to unloaded running (R≥0.39), and moderately negatively related to walking with and without load (R≤-0.52).
While load reduces peak accelerations at both the pelvis and foot. However, it may increase demand on the lower extremity to attenuate the signal between the two sensors with each step, while attenuation over time reduces with load.
尽管与损伤相关的生物力学(尤其是与负重有关的生物力学)具有危害性,但目前关于身体需求与可穿戴传感器捕捉到的变量之间关联的研究有限。尽管惯性测量单元(IMU)可用作地面反作用力(GRF)变量的替代测量值,但尚不清楚这些数据是否对特定于军事的任务需求敏感。
可穿戴传感器能否在不同的负重、速度和坡度条件下描述个人的身体负荷和需求?
本研究共纳入 20 名自报告无当前损伤、有规律运动且能够穿戴 23kg 重背心的个体。每位参与者在平地、上坡(+6%)和下坡(-6%)环境下进行无负重和负重(23kg)行走和跑步。通过光学运动捕捉(OMC)和放置在远端肢体和骨盆上的 IMU 同步收集数据。使用 8 秒的窗口数据生成参与者的 OMC 和 IMU 感兴趣变量的平均值。重复测量方差分析用于测量负荷、速度和坡度的主效应和交互效应。简单线性回归用于阐明 OMC 测量值与估计代谢成本(EMC)和 IMU 测量值之间的关系。
负荷降低了足部和骨盆的加速度(p<0.001),但增加了每步的信号衰减(p=0.044)。相反,随着负荷的增加,每公里的衰减降低(p=0.017)。在行走时,上坡的每步和每公里的衰减最低(p=0.003),下坡的每步和每公里的衰减最大(p≤0.002)。衰减测量值与肢体负功的关系不一致,中等相关(R≤0.57)。EMC 与无负荷跑步中度正相关(R≥0.39),与有负荷和无负荷行走中度负相关(R≤-0.52)。
虽然负荷降低了骨盆和足部的峰值加速度,但可能会增加下肢的需求,以在每一步中衰减两个传感器之间的信号,而随着时间的推移,衰减会随着负荷的增加而降低。