Dorschky Eva, Nitschke Marlies, Mayer Matthias, Weygers Ive, Gassner Heiko, Seel Thomas, Eskofier Bjoern M, Koelewijn Anne D
Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Institute of Mechatronic Systems, Leibniz Universität Hannover, Hannover, Germany.
Front Bioeng Biotechnol. 2025 Feb 19;13:1507162. doi: 10.3389/fbioe.2025.1507162. eCollection 2025.
Estimating spatiotemporal, kinematic, and kinetic movement variables with little obtrusion to the user is critical for clinical and sports applications. One possible approach is using a sparse inertial sensor setup, where sensors are not placed on all relevant body segments. Here, we investigated if movement variables can be estimated similarly accurate from sparse sensor setups as from a full lower-body sensor setup. We estimated the variables by solving optimal control problems with sagittal plane lower-body musculoskeletal models, in which we minimized an objective that combined tracking of accelerometer and gyroscope data with minimizing muscular effort. We created simulations for 10 participants at three walking and three running speeds, using seven sensor setups with between two and seven sensors located at the feet, shank, thighs, and/or pelvis. We found that differences between variables estimated from inertial sensors and those from optical motion capture were small for all sensor setups. Including all sensors did not necessarily lead to the smallest root mean square deviations (RMSDs) and highest coefficients of determination ( ). Setups without a pelvis sensor led to too much forward trunk lean and inaccurate spatiotemporal variables. Mean RMSDs were highest for the setup with two foot-worn inertial sensors (largest error in knee angle during running: 18 deg vs. 11 deg for the full lower-body setup), and ranged between 4.8-18 deg for the joint angles, between 1.0-5.4 BW BH% for the joint moments, and between 0.03 BW-0.49 BW for the ground reaction forces. We found strong or moderate relationships ( ) on average for all kinematic and kinetic variables, except for the hip and knee moment for five out of the seven setups. The large range of the coefficient of determination for most kinetic variables indicated individual differences in simulation quality. Therefore, we conclude that we can perform a comprehensive sagittal-plane motion analysis with sparse sensor setups as accurately as with a full sensor setup with sensors on the feet and on either the pelvis or the thighs. Such a sparse sensor setup enables comprehensive movement analysis outside the laboratory, by increasing usability of inertial sensors.
在对用户干扰极小的情况下估计时空、运动学和动力学运动变量对于临床和体育应用至关重要。一种可能的方法是使用稀疏惯性传感器设置，即传感器并非放置在所有相关身体部位上。在此，我们研究了从稀疏传感器设置中估计的运动变量是否能与完整的下半身传感器设置一样准确。我们通过使用矢状面下半身肌肉骨骼模型解决最优控制问题来估计这些变量，在该模型中，我们将一个结合了加速度计和陀螺仪数据跟踪以及最小化肌肉力量的目标函数最小化。我们针对10名参与者在三种步行速度和三种跑步速度下进行了模拟，使用了七种传感器设置，传感器分布在脚、小腿、大腿和/或骨盆处，数量在两个到七个之间。我们发现，对于所有传感器设置，从惯性传感器估计的变量与光学动作捕捉估计的变量之间的差异都很小。包含所有传感器并不一定能得到最小的均方根偏差（RMSD）和最高的决定系数（ ）。没有骨盆传感器的设置会导致躯干过度前倾以及时空变量不准确。对于仅在双脚佩戴惯性传感器的设置，均方根偏差最高（跑步时膝关节角度的最大误差：与完整下半身设置的11度相比，该设置为18度），关节角度的均方根偏差在4.8 - 18度之间，关节力矩在1.0 - 5.4体重·身高百分比之间，地面反作用力在0.03体重 - 0.49体重之间。我们发现，除了七种设置中的五种设置下的髋关节和膝关节力矩外，所有运动学和动力学变量平均具有强或中等的相关性（ ）。大多数动力学变量的决定系数范围较大，表明模拟质量存在个体差异。因此，我们得出结论，我们可以使用稀疏传感器设置像使用在脚以及骨盆或大腿上都有传感器的完整传感器设置一样准确地进行全面的矢状面运动分析。这样的稀疏传感器设置通过提高惯性传感器的可用性，能够在实验室外进行全面的运动分析。
