Department of Electrical and Electronics Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore 575025, India.
J Electromyogr Kinesiol. 2019 Oct;48:152-160. doi: 10.1016/j.jelekin.2019.07.006. Epub 2019 Jul 21.
Research in pattern recognition (PR) for myoelectric control of the upper limb prostheses has been extensive. However, there has been limited attention to the factors that influence the clinical translation of this technology. A relevant factor of influence in clinical performance of EMG PR-based control of prostheses is the variation in muscle activation level, which modifies the EMG patterns even when the amputee attempts the same movement. To decrease the effect of muscle activation level variations on EMG PR, this work proposes to use dynamic time warping (DTW) and is validated on two databases. The first database, which has data from ten intact-limbed subjects, was used to test the baseline performance of DTW, resulting in an average classification accuracy of more than 90%. The second database comprised data from nine upper limb amputees recorded at three levels of force for six hand grips. The results showed that DTW trained at a single force level achieved an average classification accuracy of 60 ± 9%, 70 ± 8%, and 60 ± 7% at the low, medium and high force levels respectively across all amputee subjects. The proposed scheme with DTW achieved a significant 10% improvement in classification accuracy when trained at a low force level when compared to the traditional time-dependent power spectrum descriptors (TD-PSD) method.
针对上肢假肢肌电控制的模式识别 (PR) 研究已经很广泛。然而，对于影响这项技术临床转化的因素关注有限。影响假肢肌电 PR 控制临床性能的一个相关因素是肌肉激活水平的变化，即使截肢者尝试相同的运动，也会改变肌电模式。为了减少肌肉激活水平变化对肌电 PR 的影响，这项工作提出使用动态时间规整 (DTW) ，并在两个数据库上进行了验证。第一个数据库包含来自十个完整肢体受试者的数据，用于测试 DTW 的基线性能，结果平均分类准确率超过 90%。第二个数据库包含来自九个上肢截肢者的数据，记录了在三个力量水平下进行的六种手抓握动作。结果表明，在所有截肢者中，DTW 在单一力量水平下的平均分类准确率分别为 60±9%、70±8%和 60±7%，在低、中、高力量水平下。与传统的时变功率谱描述符 (TD-PSD) 方法相比，当在低力量水平下训练时，基于 DTW 的建议方案在分类准确性方面显著提高了 10%。
