IEEE Trans Biomed Eng. 2011 Aug;58(8). doi: 10.1109/TBME.2011.2155063. Epub 2011 May 16.
Real-time pattern recognition control is frequently affected by misclassifications. This study investigated the use of a decision-based velocity ramp that attenuated movement speed after a change in classifier decision. The goal was to improve prosthesis positioning by minimizing the effect of unintended movements. Non-amputee and amputee subjects controlled a prosthesis in real-time using pattern recognition. While performing a target achievement test in a virtual environment, subjects had a significantly higher completion rate (p < 0.05) and a more direct path (p < 0.05) to the target with the velocity ramp than without it. Using a physical prosthesis, subjects stacked a greater average number of 1 cubes (p < 0.05) in three minutes with the velocity ramp than without it (76% more blocks for non-amputees; 89% more blocks for amputees). Real-time control using the velocity ramp also showed significant performance improvements above using majority vote. Eighty-three percent of subjects preferred to control the prosthesis using the velocity ramp. These results suggest that using a decision-based velocity ramp with pattern recognition may improve user performance. Since the velocity ramp is a post-processing step, it has the potential to be used with a variety of classifiers for many applications.
实时模式识别控制经常受到误分类的影响。本研究探讨了使用基于决策的速度斜坡的方法，即在分类器决策发生变化后减缓运动速度。目的是通过最小化意外运动的影响来改善假体定位。非截肢者和截肢者受试者使用模式识别实时控制假肢。在虚拟环境中进行目标达成测试时，与没有速度斜坡的情况相比，受试者的完成率（p < 0.05）更高，达到目标的路径更直接（p < 0.05）。使用物理假肢，与没有速度斜坡的情况相比，受试者在三分钟内堆叠的 1 号方块的平均数量更多（非截肢者多 76%；截肢者多 89%）。使用速度斜坡进行实时控制也显示出比使用多数表决有显著的性能提升。83%的受试者更喜欢使用速度斜坡来控制假肢。这些结果表明，使用基于决策的速度斜坡与模式识别相结合可能会提高用户的性能。由于速度斜坡是一个后处理步骤，因此它有可能用于许多应用的各种分类器。
