Modern lower limb prostheses neither measure nor incorporate healthy residual leg information for intent recognition or device control. In order to increase robustness and reduce misclassification of devices like these, we propose a vision-based solution for real-time 3D human contralateral limb tracking (CoLiTrack). An inertial measurement unit and a depth camera are placed on the side of the prosthesis. The system is capable of estimating the shank axis of the healthy leg. Initially, the 3D input is transformed into a stabilized coordinate system. By splitting the subsequent shank estimation problem into two less computationally intensive steps, the computation time is significantly reduced: First, an iterative closest point algorithm is applied to fit circular models against 2D projections. Second, the random sample consensus method is used to determine the final shank axis. In our study, three experiments were conducted to validate the static, the dynamic and the real-world performance of our CoLiTrack approach. The shank angle can be tracked at 20 Hz for one sixth of the entire human gait cycle with an angle estimation error below \(2.8\pm 2.1^{\circ }\). Our promising results demonstrate the robustness of the novel CoLiTrack approach to make “next-generation prostheses” more user-friendly, functional and safe.

现代的下肢假体既不能测量也不能合并健康的残肢信息以进行意图识别或设备控制。为了提高鲁棒性并减少此类设备的误分类，我们提出了一种基于视觉的实时3D人类对侧肢体跟踪（CoLiTrack）解决方案。惯性测量单元和深度相机放置在假体的侧面。该系统能够估计健康腿的小腿轴线。最初，将3D输入转换为稳定的坐标系。通过将随后的小腿估计问题分成两个计算量较小的步骤，可显着减少计算时间：首先，采用迭代最近点算法对2D投影拟合圆形模型。第二，随机样本一致性方法用于确定最终的柄轴。在我们的研究中，进行了三个实验来验证CoLiTrack方法的静态，动态和真实性能。整个人体步态周期的六分之一可以以20 Hz的频率跟踪小腿角度，角度估计误差低于\（2.8 \ pm 2.1 ^ {\ circ} \）。我们令人鼓舞的结果证明了新颖的CoLiTrack方法的健壮性，可以使“下一代假体”更加人性化，功能化和安全化。