Sun Yiou, Song Zhenhua, Mo Lifen, Li Binbin, Liang Fengyan, Yin Ming, Wang Dong
Sanya Research Institute of Hainan University, School of Biomedical Engineering, Hainan University, Sanya, China.
Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Sanya, China.
Sci Rep. 2025 Mar 19;15(1):9541. doi: 10.1038/s41598-025-94167-y.
Gait impairment, which is commonly observed in stroke survivors, underscores the imperative of rehabilitating walking function. Wearable inertial measurement units (IMUs) can capture gait parameters in stroke patients, becoming a promising tool for objective and quantifiable gait assessment. Optimal sensor placement for stroke assessment that involves optimal combinations of features (kinematics) is required to improve stroke assessment accuracy while reducing the number of sensors to achieve a convenient IMU scheme for both clinical and home assessment; however, previous studies lack comprehensive discussions on the optimal sensor placement and features. To obtain an optimal sensor placement for stroke assessment, this study investigated the impact of IMU placement on stroke assessment based on gait data and clinical scores of 16 stroke patients. Stepwise regression was performed to select the kinematics most correlated with stroke assessment (lower limb part of Fugl-Meyer assessment). Sensors at different locations were combined into 28 sensor groups and their stroke assessment was compared. First, the reduced number of gait features does not significantly impact the stroke assessment. Second, the selected gait parameters by stepwise regression are found all from sensors at the hip and bilateral thighs. Last, a three-sensor scheme-sensors at the hip and bilateral thighs was suggested, which achieved a high accuracy with an adjusted R = 0.999, MAE = 0.07, and RMSE = 0.08. Further, the prediction error is zero if the predicted lower limb Fugl-Meyer scales are rounded to the nearest integer. These findings offer a convenient IMU solution for quantitatively assessing stroke patients. Therefore, the IMU-based stroke assessment provides a promising complementary tool for clinical assessment and home rehabilitation of stroke patients.
步态障碍在中风幸存者中很常见，这凸显了恢复步行功能的紧迫性。可穿戴惯性测量单元（IMU）可以捕捉中风患者的步态参数，成为客观且可量化步态评估的一个有前景的工具。为了提高中风评估的准确性，同时减少传感器数量以实现适用于临床和家庭评估的便捷IMU方案，需要针对中风评估进行最佳传感器放置，这涉及特征（运动学）的最佳组合；然而，以往的研究缺乏对最佳传感器放置和特征的全面讨论。为了获得用于中风评估的最佳传感器放置，本研究基于16名中风患者的步态数据和临床评分，研究了IMU放置对中风评估的影响。进行逐步回归以选择与中风评估（Fugl-Meyer评估的下肢部分）最相关的运动学指标。将不同位置的传感器组合成28个传感器组，并比较它们的中风评估结果。首先，减少的步态特征数量对中风评估没有显著影响。其次，通过逐步回归选择的步态参数均来自髋部和双侧大腿处的传感器。最后，提出了一种三传感器方案——髋部和双侧大腿处的传感器，其调整后的R = 0.999、平均绝对误差（MAE）= 0.07和均方根误差（RMSE）= 0.08，实现了高精度。此外，如果将预测的下肢Fugl-Meyer量表四舍五入到最接近的整数，则预测误差为零。这些发现为定量评估中风患者提供了一种便捷的IMU解决方案。因此，基于IMU的中风评估为中风患者的临床评估和家庭康复提供了一个有前景的补充工具。
