Department of Mechanical Engineering, Faculty of Engineering, University of Central Punjab, Lahore, Pakistan.
Human-centered robotics lab, National Center of Robotics and Automation (NCRA), Rawalpindi, Pakistan.
PLoS One. 2022 May 13;17(5):e0266726. doi: 10.1371/journal.pone.0266726. eCollection 2022.
Quantification of key gait parameters plays an important role in assessing gait deficits in clinical research. Gait parameter estimation using lower-limb kinematics (mainly leg velocity data) has shown promise but lacks validation for the amputee population. The aim of this study is to assess the accuracy of lower-leg angular velocity to predict key gait events (toe-off and heel strike) and associated temporal parameters for the amputee population. An open data set of reflexive markers during treadmill walking from 10 subjects with unilateral transfemoral amputation was used. A rule-based dual-minima algorithm was developed to detect the landmarks in the shank velocity signal indicating toe-off and heel strike events. Four temporal gait parameters were also estimated (step time, stride time, stance and swing duration). These predictions were compared against the force platform data for 3000 walking cycles from 239 walking trials. Considerable accuracy was achieved for the HS event as well as for step and stride timings, with mean errors ranging from 0 to -13ms. The TO prediction exhibited a larger error with its mean ranging from 35-81ms. The algorithm consistently predicted the TO earlier than the actual event, resulting in prediction errors in stance and swing timings. Significant differences were found between the prediction for sound and prosthetic legs, with better TO accuracy on the prosthetic side. The prediction accuracy also appeared to improve with the subjects' mobility level (K-level). In conclusion, the leg velocity profile, coupled with the dual-minima algorithm, can predict temporal parameters for the transfemoral amputee population with varying degrees of accuracy.
定量评估关键步态参数在临床研究中评估步态缺陷方面发挥着重要作用。使用下肢运动学（主要是腿部速度数据）估计步态参数已显示出前景，但缺乏对截肢人群的验证。本研究旨在评估小腿角速度预测关键步态事件（足离地和足跟触地）和相关时间参数的准确性，用于截肢人群。使用来自 10 名单侧股骨截肢者在跑步机上行走时的反射标记的开放数据集。开发了基于规则的双极小值算法来检测小腿速度信号中的地标，以指示足离地和足跟触地事件。还估计了四个时间步态参数（步时、步幅时间、站立和摆动持续时间）。将这些预测与来自 239 次行走试验的 3000 个行走周期的力台数据进行比较。HS 事件以及步时和步幅计时的准确性相当高，平均误差范围为 0 到-13ms。TO 预测的误差较大，平均值范围为 35-81ms。该算法始终预测到比实际事件更早的 TO，导致站立和摆动时间的预测误差。在健康腿和假肢腿的预测之间发现了显著差异，假肢侧的 TO 准确性更好。预测准确性似乎也随着受试者的移动水平（K 级）而提高。总之，腿部速度曲线，加上双极小值算法，可以以不同的精度预测股骨截肢人群的时间参数。
