Division of Navigation & Information Systems, Mokpo National Maritime University, Mokpo 58628, Korea.
Department of Computer Science, University of Nebraska Omaha, Omaha, NE 68182, USA.
Sensors (Basel). 2018 May 15;18(5):1577. doi: 10.3390/s18051577.
Total knee arthroplasty is a common surgical treatment for end-stage osteoarthritis of the knee. The majority of existing studies that have explored the relationship between recovery and gait biomechanics have been conducted in laboratory settings. However, seamless gait parameter monitoring in real-world conditions may provide a better understanding of recovery post-surgery. The purpose of this study was to estimate kinematic and kinetic gait variables using two ankle-worn wearable sensors in individuals after unilateral total knee arthroplasty. Eighteen subjects at least six months post-unilateral total knee arthroplasty participated in this study. Four biomechanical gait variables were measured using an instrumented split-belt treadmill and motion capture systems. Concurrently, eleven inertial gait variables were extracted from two ankle-worn accelerometers. Subsets of the inertial gait variables for each biomechanical gait variable estimation were statistically selected. Then, hierarchical regressions were created to determine the directional contributions of the inertial gait variables for biomechanical gait variable estimations. Selected inertial gait variables significantly predicted trial-averaged biomechanical gait variables. Moreover, strong directionally-aligned relationships were observed. Wearable-based gait monitoring of multiple and sequential kinetic gait variables in daily life could provide a more accurate understanding of the relationships between movement patterns and recovery from total knee arthroplasty.
全膝关节置换术是治疗膝关节晚期骨关节炎的一种常见手术方法。大多数现有的研究都在实验室环境中探讨了恢复和步态生物力学之间的关系。然而，在真实环境中进行无缝的步态参数监测可能可以更好地了解手术后的恢复情况。本研究旨在使用两个踝部佩戴的可穿戴传感器来估计单侧全膝关节置换术后个体的运动学和动力学步态变量。本研究纳入了 18 名至少在单侧全膝关节置换术后 6 个月的受试者。使用仪器化分带跑步机和运动捕捉系统测量了四个生物力学步态变量。同时，从两个踝部佩戴的加速度计中提取了 11 个惯性步态变量。对每个生物力学步态变量估计的惯性步态变量子集进行了统计选择。然后，创建了层次回归来确定惯性步态变量对生物力学步态变量估计的定向贡献。选定的惯性步态变量显著预测了试验平均的生物力学步态变量。此外，观察到了强定向一致的关系。在日常生活中对多个连续的动力学步态变量进行基于可穿戴设备的步态监测，可以更准确地了解运动模式与全膝关节置换术后恢复之间的关系。
