有巨大的机会来推进科学,临床护理,运动表现,和社会健康,如果我们能够开发监测肌肉骨骼负荷的工具(例如,骨骼或肌肉上的力)在实验室外。虽然可穿戴传感器能够在应用情况下实现对人体运动的非侵入性监测,目前的商用可穿戴设备无法估计体内结构上的组织水平负荷。在这里,我们探讨了在跑步过程中使用可穿戴传感器来估计胫骨力的可行性。首先,我们使用基于实验室的数据和肌肉骨骼模型来估计10名参与者在一系列速度和坡度下跑步的胫骨力.接下来,我们将基于实验室的数据转换为使用可穿戴设备(脚和小腿上的惯性测量单位,和压力感测鞋垫),并使用这些数据开发了两种多传感器算法来估计峰值胫骨力:一种基于物理的算法和一种机器学习的算法。此外,为了反映当前的跑步可穿戴设备,这些可穿戴设备利用跑步影响指标来推断肌肉骨骼负荷或受伤风险,我们使用通常测量的冲击度量来估计胫骨力,地面反作用力垂直平均加载率(VALR)。使用VALR估算胫骨力峰值,平均绝对误差为9.9%,这并不比理论计步器更准确,该计步器假设每个步幅都具有相同的峰值力。我们基于物理的算法将误差降低到5.2%,我们的机器学习算法将误差降低到2.6%。Further,为了深入了解力估计准确性与过度使用伤害风险的关系,我们计算了由于给定的加载周期而预期的骨损伤。我们发现,胫骨力的适度误差会转化为骨骼损伤估计值的较大误差。例如,使用VALR的胫骨力的9.9%误差转化为估计的骨损伤的104%误差。令人鼓舞的是,基于物理和机器学习的算法将损伤误差降低到41%和18%,分别。这项研究强调了结合可穿戴设备的令人兴奋的潜力,肌肉骨骼生物力学和机器学习,以开发更准确的工具来监测应用情况下的肌肉骨骼负荷。
There are tremendous opportunities to advance science, clinical care, sports performance, and societal health if we are able to develop tools for monitoring musculoskeletal loading (e.g., forces on bones or muscles) outside the lab. While wearable sensors enable non-invasive monitoring of human movement in applied situations, current commercial wearables do not estimate tissue-level loading on structures inside the body. Here we explore the feasibility of using wearable sensors to estimate tibial bone force during running. First, we used lab-based data and musculoskeletal modeling to estimate tibial force for ten participants running across a range of speeds and slopes. Next, we converted lab-based data to signals feasibly measured with wearables (inertial measurement units on the foot and shank, and pressure-sensing insoles) and used these data to develop two multi-sensor algorithms for estimating peak tibial force: one physics-based and one machine learning. Additionally, to reflect current running wearables that utilize running impact metrics to infer musculoskeletal loading or injury risk, we estimated tibial force using a commonly measured impact metric, the ground reaction force vertical average loading rate (VALR). Using VALR to estimate peak tibial force resulted in a mean absolute percent error of 9.9%, which was no more accurate than a theoretical step counter that assumed the same peak force for every running stride. Our physics-based algorithm reduced error to 5.2%, and our machine learning algorithm reduced error to 2.6%. Further, to gain insights into how force estimation accuracy relates to overuse injury risk, we computed bone damage expected due to a given loading cycle. We found that modest errors in tibial force translated into large errors in bone damage estimates. For example, a 9.9% error in tibial force using VALR translated into 104% error in estimated bone damage. Encouragingly, the physics-based and machine learning algorithms reduced damage errors to 41% and 18%, respectively. This study highlights the exciting potential to combine wearables, musculoskeletal biomechanics and machine learning to develop more accurate tools for monitoring musculoskeletal loading in applied situations.