Neugebauer Jennifer M, Collins Kelsey H, Hawkins David A
Biomedical Engineering Graduate Group, University of California Davis, Davis, California, United States of America.
Department of Neurobiology, Physiology & Behavior, University of California Davis, Davis, California, United States of America.
PLoS One. 2014 Jun 10;9(6):e99023. doi: 10.1371/journal.pone.0099023. eCollection 2014.
Simple methods to quantify ground reaction forces (GRFs) outside a laboratory setting are needed to understand daily loading sustained by the body. Here, we present methods to estimate peak vertical GRF (pGRFvert) and peak braking GRF (pGRFbrake) in adults using raw hip activity monitor (AM) acceleration data. The purpose of this study was to develop a statistically based model to estimate pGRFvert and pGRFbrake during walking and running from ActiGraph GT3X+ AM acceleration data. 19 males and 20 females (age 21.2 ± 1.3 years, height 1.73 ± 0.12 m, mass 67.6 ± 11.5 kg) wore an ActiGraph GT3X+ AM over their right hip. Six walking and six running trials (0.95-2.19 and 2.20-4.10 m/s, respectively) were completed. Average of the peak vertical and anterior/posterior AM acceleration (ACCvert and ACCbrake, respectively) and pGRFvert and pGRFbrake during the stance phase of gait were determined. Thirty randomly selected subjects served as the training dataset to develop generalized equations to predict pGRFvert and pGRFbrake. Using a holdout approach, the remaining 9 subjects were used to test the accuracy of the models. Generalized equations to predict pGRFvert and pGRFbrake included ACCvert and ACCbrake, respectively, mass, type of locomotion (walk or run), and type of locomotion acceleration interaction. The average absolute percent differences between actual and predicted pGRFvert and pGRFbrake were 8.3% and 17.8%, respectively, when the models were applied to the test dataset. Repeated measures generalized regression equations were developed to predict pGRFvert and pGRFbrake from ActiGraph GT3X+ AM acceleration for young adults walking and running. These equations provide a means to estimate GRFs without a force plate.
需要简单的方法来量化实验室环境之外的地面反作用力（GRF），以了解身体承受的日常负荷。在此，我们提出了使用原始髋部活动监测器（AM）加速度数据来估计成年人垂直地面反作用力峰值（pGRFvert）和制动地面反作用力峰值（pGRFbrake）的方法。本研究的目的是开发一种基于统计的模型，以根据ActiGraph GT3X + AM加速度数据估计步行和跑步过程中的pGRFvert和pGRFbrake。19名男性和20名女性（年龄21.2±1.3岁，身高1.73±0.12米，体重67.6±11.5千克）在右髋部佩戴ActiGraph GT3X + AM。完成了六次步行和六次跑步试验（速度分别为0.95 - 2.19和2.20 - 4.10米/秒）。确定了步态站立期垂直和前后AM加速度峰值（分别为ACCvert和ACCbrake）以及pGRFvert和pGRFbrake的平均值。30名随机选择的受试者作为训练数据集，以开发预测pGRFvert和pGRFbrake的通用方程。采用留出法，其余9名受试者用于测试模型的准确性。预测pGRFvert和pGRFbrake的通用方程分别包括ACCvert和ACCbrake、体重、运动类型（步行或跑步）以及运动加速度相互作用类型。当将模型应用于测试数据集时，实际和预测的pGRFvert和pGRFbrake之间的平均绝对百分比差异分别为8.3%和17.8%。开发了重复测量广义回归方程，以根据ActiGraph GT3X + AM加速度预测年轻人步行和跑步时的pGRFvert和pGRFbrake。这些方程提供了一种无需测力台即可估计GRF的方法。
