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Abstract：
After an ACL injury, rehabilitation consists of multiple phases, and progress between these phases is guided by subjective visual assessments of activities such as running, hopping, jump landing, etc. Estimation of objective kinetic measures like knee joint moments and GRF during assessment can help physiotherapists gain insights on knee loading and tailor rehabilitation protocols. Conventional methods deployed to estimate kinetics require complex, expensive systems and are limited to laboratory settings. Alternatively, multiple algorithms have been proposed in the literature to estimate kinetics from kinematics measured using only IMUs. However, the knowledge about their accuracy and generalizability for patient populations is still limited. Therefore, this article aims to identify the available algorithms for the estimation of kinetic parameters using kinematics measured only from IMUs and to evaluate their applicability in ACL rehabilitation through a comprehensive systematic review. The papers identified through the search were categorized based on the modelling techniques and kinetic parameters of interest, and subsequently compared based on the accuracies achieved and applicability for ACL patients during rehabilitation. IMUs have exhibited potential in estimating kinetic parameters with good accuracy, particularly for sagittal movements in healthy cohorts. However, several shortcomings were identified and future directions for improvement have been proposed, including extension of proposed algorithms to accommodate multiplanar movements and validation of the proposed techniques in diverse patient populations and in particular the ACL population.
摘要：
ACL受伤后,康复包括多个阶段，这些阶段之间的进展是通过对活动的主观视觉评估来指导的，如跑步，跳跃,跳跃着陆,等。评估过程中对膝关节力矩和GRF等客观动力学措施的估计可以帮助物理治疗师了解膝关节负荷并制定康复方案。用于估计动力学的常规方法需要复杂的，昂贵的系统，并且仅限于实验室设置。或者，在文献中已经提出了多种算法来从仅使用IMU测量的运动学估计动力学。然而,对患者人群的准确性和普适性的认识仍然有限.因此,本文旨在确定使用仅从IMU测量的运动学估计动力学参数的可用算法，并通过全面的系统评价评估其在ACL康复中的适用性。通过搜索确定的论文是根据感兴趣的建模技术和动力学参数进行分类的，随后根据康复期间ACL患者的准确性和适用性进行比较。IMU在以良好的精度估计动力学参数方面表现出潜力，特别是对于健康队列中的矢状运动。然而,发现了几个缺点，并提出了未来的改进方向，包括扩展所提出的算法以适应多平面运动，并在不同患者群体，特别是ACL群体中验证所提出的技术。
