用十字韧带拉伸检测步态事件，提升假肢控制精度。
Event Detection for Active Lower Limb Prosthesis
- 通过双髁膝结构测量韧带拉伸，捕捉步态关键点。
- 发现后十字韧带在步态80%、前十字在5%处拉伸显著变化。
- 可预测足部触地与足放平，适合智能假肢研发者参考。
准确的事件检测对半主动和动力假肢设计至关重要。自然膝关节具有复杂的平移与旋转运动，影响步态特征；简化为铰链关节会丢失部分行为。本研究探究十字韧带拉伸在事件检测中的作用。采用双髁膝结构，由前后十字韧带模拟物约束，通过平行于韧带的线性位移传感器（LVDT）记录屈膝拐杖上罗素膝的韧带拉伸数据，采集了三种速度下跑步机上的步态数据。研究发现，十字韧带拉伸存在速度依赖性：后十字韧带在步态周期约80%处、前十字韧带在约5%处出现显著变化。尽管整体周期轮廓随速度保持一致，但后十字与前十字韧带分别在90%和95%处出现转折点，可作为初始触地的预测前兆；另一对转折点也出现在90%和95%处，可用于预测足放平。结果表明，采用双髁膝设计可提升步态事件检测精度，从而增强后续动力假肢控制器的性能。
原文摘要 · Abstract (English)
Accurate event detection is key to the successful design of semi-passive and powered prosthetics. Kinematically, the natural knee is complex, with translation and rotation components that have a substantial impact on gait characteristics. When simplified to a pin joint, some of this behaviour is lost. This study investigates the role of cruciate ligament stretch in event detection. A bicondylar knee design was used, constrained by analogues of the anterior and posterior cruciate ligaments. This offers the ability to characterize knee kinematics by the stretch of the ligaments. The ligament stretch was recorded using LVDTs parallel to the ligaments of the Russell knee on a bent knee crutch. Which was used to capture data on a treadmill at 3 speeds. This study finds speed dependence within the stretch of the cruciate ligaments, prominently around 5\% and 80\% of the gait cycle for the posterior and anterior. The cycle profile remains consistent with speed; therefore, other static events such as the turning point feature at around 90\% and 95\% of the cycle, for the posterior and anterior, respectively, could be used as a predictive precursor for initial contact. Likewise at 90\% and 95\%, another pair of turning points that in this case could be used to predict foot flat. This concludes that the use of a bicondylar knee design could improve the detection of events during the gait cycle, and therefore could increase the accuracy of subsequent controllers for powered prosthetics.
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