Objective: Enable accurate estimation of vertical average loading rate (VALR) in runners with one or more wearable inertial measurement units (IMUs). Methods: A subject-independent convolutional neural network (CNN) model was developed to estimate VALR from wearable IMUs. Fifteen runners wore IMUs at the trunk, pelvis, thigh, shank, and foot and ran on an instrumented treadmill for combinations of the following conditions: foot-strike (forefoot, mid-foot, rear-foot), step rate (90% to 110% of baseline), running speed (2.4 m/s and 2.8 m/s) and footwear (standard and minimalist running shoes). Thirty-one IMU placement configurations with combinations of one to five IMUs were evaluated. VALR estimations from the wearable IMUs were compared with force-plate VALR measurements. Results: VALR estimations via the subject-independent CNN model with a single shank-worn IMU were highly correlated ( ρ = 0.94) with force-plate VALR measurements and were substantially higher than previously reported peak tibial acceleration correlations with force-plate VALR measurements from shank-worn accelerometers ( ρ = 0.44–0.66). Correlation results from the CNN model for a single IMU placed at the foot, pelvis, trunk, and thigh were ρ = 0.91, 0.76, 0.69, and 0.65, respectively. There was no improvement in accuracy from the shank-worn IMU when adding 1–4 additional IMUs from the trunk, pelvis, thigh, or foot. Conclusion: The proposed subject-independent CNN model with a single shank-worn IMU provides more accurate estimation of VALR than previous wearable sensing approaches. Significance: This could enable runners to more accurately assess impact loading rates and potentially provide insights into running-related injury risk and prevention.

中文翻译：

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通过与主题无关的卷积神经网络模型和最佳 IMU 放置在运行期间准确估计冲击载荷率

客观的： 使用一个或多个可穿戴惯性测量单元 (IMU) 实现对跑步者垂直平均负载率 (VALR) 的准确估计。 方法：开发了一种独立于主题的卷积神经网络 (CNN) 模型来估计可穿戴 IMU 的 VALR。15 名跑步者在躯干、骨盆、大腿、小腿和足部佩戴 IMU 并在配备仪表的跑步机上针对以下条件组合跑步：足部撞击（前足、中足、后足）、步率（90% 至基线的 110%）、跑步速度（2.4 m/s 和 2.8 m/s）和鞋类（标准和极简跑鞋）。评估了 1 到 5 个 IMU 组合的 31 个 IMU 放置配置。来自可穿戴 IMU 的 VALR 估计值与测力板 VALR 测量值进行了比较。结果： 通过独立于主体的 CNN 模型与单个小腿磨损 IMU 的 VALR 估计高度相关（ ρ = 0.94) 与测力板 VALR 测量值相比，显着高于先前报道的胫骨加速度峰值相关性与来自小腿加速度计的测力板 VALR 测量值的相关性（ ρ= 0.44–0.66）。CNN 模型对放置在足部、骨盆、躯干和大腿的单个 IMU 的相关结果为ρ= 0.91、0.76、0.69 和 0.65，分别。当从躯干、骨盆、大腿或足部增加 1-4 个额外的 IMU 时，小腿佩戴的 IMU 的准确性没有提高。结论： 与以前的可穿戴传感方法相比，所提出的具有单个小腿 IMU 的独立于主题的 CNN 模型提供了更准确的 VALR 估计。 意义： 这可以使跑步者更准确地评估冲击负荷率，并可能提供有关与跑步相关的伤害风险和预防的见解。