Asgari Hossein, Heller Ben
Sport and Physical Activity Research Centre, Sheffield Hallam University, Olympic Legacy Park, 2 Old Hall Rd, Sheffield S9 3TY, UK.
Sensors (Basel). 2025 Jan 7;25(2):315. doi: 10.3390/s25020315.
Our aim was to validate a sacral-mounted inertial measurement unit (IMU) for reconstructing running kinematics and comparing movement patterns within and between runners. IMU data were processed using Kalman and complementary filters separately. RMSE and Bland-Altman analysis assessed the validity of each filtering method against a motion capture system. Running data from 24 recreational runners were analyzed using Fourier transform coefficients, PCA, and k-means clustering. High agreement was found for Kalman-filtered data in the frontal, sagittal, and transverse planes, with a Bland-Altman bias of ~2 mm on average, compared to a bias of ~10.5 mm for complementary-filtered data. Pelvic angles calculated from Kalman-filtered data had superior agreement, with systematic biases of ~0.3 versus 3.4 degrees for complementary-filtered data. Our findings suggest that inertial sensors are viable alternatives to motion capture for reconstructing pelvic running kinematics and movement patterns. In the second part of our study, negligible intra-individual differences were observed with changes in speed, while inter-individual differences were large. Two clusters of runners were identified, each showing distinct movement patterns and ranges of motion. These observations highlight the potential usefulness of inertial sensors for performance analysis and rehabilitation as they may permit the use of individual-specific and cluster-specific practice programs.
我们的目标是验证一种安装在骶骨上的惯性测量单元(IMU),用于重建跑步运动学并比较跑步者内部以及不同跑步者之间的运动模式。分别使用卡尔曼滤波器和互补滤波器处理IMU数据。均方根误差(RMSE)和布兰德-奥特曼分析评估了每种滤波方法相对于动作捕捉系统的有效性。使用傅里叶变换系数、主成分分析(PCA)和k均值聚类分析了24名休闲跑步者的跑步数据。与互补滤波数据平均约10.5毫米的偏差相比,卡尔曼滤波数据在额状面、矢状面和横断面的一致性较高,布兰德-奥特曼偏差平均约为2毫米。由卡尔曼滤波数据计算出的骨盆角度一致性更好,互补滤波数据的系统偏差为约3.4度,而卡尔曼滤波数据为约0.3度。我们的研究结果表明,在重建骨盆跑步运动学和运动模式方面,惯性传感器是动作捕捉的可行替代方案。在我们研究的第二部分,观察到随着速度变化个体内部差异可忽略不计,而个体间差异很大。识别出了两组跑步者,每组都呈现出独特的运动模式和运动范围。这些观察结果突出了惯性传感器在性能分析和康复方面的潜在用途,因为它们可能允许使用针对个体和群组的特定练习方案。