论文标题
双侧反映运动提高了训练数据的准确性和精度，以监督神经或肌电假体控制
Bilaterally Mirrored Movements Improve the Accuracy and Precision of Training Data for Supervised Learning of Neural or Myoelectric Prosthetic Control
论文作者
论文摘要
对假体的直观控制依赖于训练算法将生物记录与运动意图相关联。训练数据集的质量对于运行时间的性能至关重要，但是在截肢后，很难准确地标记手动运动学。我们用两种不同的方法量化了标记手动运动学的准确性和精度：1）假设参与者完全模仿假体的预定动作（模仿训练），而2）假设参与者在相同的双边运动（镜像训练）（镜像训练）期间可以完全反映其对侧递减（镜像训练）。我们比较了在非开拓者个体中使用红外摄像机实时跟踪八个不同关节角度的方法。骨料数据显示，模仿训练并不能说明手工姿势的生物力学耦合或时间变化。镜像训练在标记手动运动学时明显更准确和精确。但是，当训练改良的卡尔曼过滤器估计电动机意图时，模仿和镜像的训练方法并没有显着差异。结果表明，镜像培训方法创造了更忠实但更复杂的数据集。高级算法可以学习复杂的镜像训练数据集，可能会产生更好的运行时间假肢控制。
Intuitive control of prostheses relies on training algorithms to correlate biological recordings to motor intent. The quality of the training dataset is critical to run-time performance, but it is difficult to label hand kinematics accurately after the hand has been amputated. We quantified the accuracy and precision of labeling hand kinematics for two different approaches: 1) assuming a participant is perfectly mimicking predetermined motions of a prosthesis (mimicked training), and 2) assuming a participant is perfectly mirroring their contralateral hand during identical bilateral movements (mirrored training). We compared these approaches in non-amputee individuals, using an infrared camera to track eight different joint angles of the hands in real-time. Aggregate data revealed that mimicked training does not account for biomechanical coupling or temporal changes in hand posture. Mirrored training was significantly more accurate and precise at labeling hand kinematics. However, when training a modified Kalman filter to estimate motor intent, the mimicked and mirrored training approaches were not significantly different. The results suggest that the mirrored training approach creates a more faithful but more complex dataset. Advanced algorithms, more capable of learning the complex mirrored training dataset, may yield better run-time prosthetic control.
