Song B, Paolieri M, Stewart H E, Golubchik L, McNitt-Gray J L, Misra V, Shah D
IEEE Trans Biomed Eng. 2025 Feb;72(2):595-608. doi: 10.1109/TBME.2024.3465373. Epub 2025 Jan 21.
Our aim is to determine if data collected with inertial measurement units (IMUs) during steady-state running could be used to estimate ground reaction forces (GRFs) and to derive biomechanical variables (e.g., contact time, impulse, change in velocity) using lightweight machine-learning approaches. In contrast, state-of-the-art estimation using LSTMs suffers from prohibitive inference times on edge devices, requires expensive training and hyperparameter optimization, and results in black box models.
We proposed a novel lightweight solution, SVD Embedding Regression (SER), using linear regression between SVD embeddings of IMU data and GRF data. We also compared lightweight solutions including SER and k-Nearest-Neighbors (KNN) regression with state-of-the-art LSTMs.
We performed extensive experiments to evaluate these techniques under multiple scenarios and combinations of IMU signals and quantified estimation errors for predicting GRFs and biomechanical variables. We did this using training data from different athletes, from the same athlete, or both, and we explored the use of acceleration and angular velocity data from sensors at different locations (sacrum and shanks).
Our results illustrated that lightweight solutions such as SER and KNN can be similarly accurate or more accurate than LSTMs. The use of personal data reduced estimation errors of all methods, particularly for most biomechanical variables (as compared to GRFs); moreover, this gain was more pronounced in the lightweight methods.
The study of GRFs is used to characterize the mechanical loading experienced by individuals in movements such as running, which is clinically applicable to identify athletes at risk for stress-related injuries.
我们的目标是确定在稳态跑步过程中使用惯性测量单元（IMU）收集的数据是否可用于估计地面反作用力（GRF），并使用轻量级机器学习方法推导生物力学变量（例如接触时间、冲量、速度变化）。相比之下，使用长短期记忆网络（LSTM）的现有估计方法在边缘设备上推理时间过长，需要昂贵的训练和超参数优化，并且会产生黑箱模型。
我们提出了一种新颖的轻量级解决方案，即奇异值分解嵌入回归（SER），利用IMU数据和GRF数据的奇异值分解嵌入之间的线性回归。我们还将包括SER和k近邻（KNN）回归在内的轻量级解决方案与现有技术的LSTM进行了比较。
我们进行了广泛的实验，以评估这些技术在多种场景以及IMU信号组合下的性能，并对预测GRF和生物力学变量的估计误差进行了量化。我们使用来自不同运动员、同一运动员或两者的训练数据进行了此项研究，并探索了来自不同位置（骶骨和小腿）传感器的加速度和角速度数据的使用情况。
我们的结果表明，SER和KNN等轻量级解决方案的准确性可以与LSTM相当或更高。使用个人数据可降低所有方法的估计误差，尤其是对于大多数生物力学变量（与GRF相比）；此外，这种改进在轻量级方法中更为明显。
对GRF的研究用于表征个体在跑步等运动中所经历的机械负荷，这在临床上可用于识别有应力相关损伤风险的运动员。
