Chicoine Caitlin L, Simon Ann M, Hargrove Levi J
Center for Bionic Medicine, Rehabilitation Institute of Chicago, Chicago, IL 60611, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1876-9. doi: 10.1109/EMBC.2012.6346318.
Pattern recognition can provide intuitive control of myoelectric prostheses. Currently, screen-guided training (SGT), in which individuals perform specific muscle contractions in sync with prompts displayed on a screen, is the common method of collecting the electromyography (EMG) data necessary to train a pattern recognition classifier. Prosthesis-guided training (PGT) is a new data collection method that requires no additional hardware and allows the individuals to keep their focus on the prosthesis itself. The movement of the prosthesis provides the cues of when to perform the muscle contractions. This study compared the training data obtained from SGT and PGT and evaluated user performance after training pattern recognition classifiers with each method. Although the inclusion of transient EMG signal in PGT data led to decreased accuracy of the classifier, subjects completed a performance task faster than when compared to using a classifier built from SGT data. This may indicate that training data collected using PGT that includes both steady state and transient EMG signals generates a classifier that more accurately reflects muscle activity during real-time use of a pattern recognition-controlled myoelectric prosthesis.
模式识别能够为肌电假肢提供直观控制。目前，屏幕引导训练（SGT）是收集训练模式识别分类器所需肌电图（EMG）数据的常用方法，在该训练中，个体根据屏幕上显示的提示同步进行特定的肌肉收缩。假肢引导训练（PGT）是一种新的数据收集方法，无需额外硬件，且能让个体将注意力集中在假肢本身。假肢的运动提供了何时进行肌肉收缩的提示。本研究比较了从SGT和PGT获得的训练数据，并在使用每种方法训练模式识别分类器后评估了用户表现。尽管PGT数据中包含瞬态EMG信号会导致分类器准确性降低，但与使用由SGT数据构建的分类器相比，受试者完成性能任务的速度更快。这可能表明，使用包含稳态和瞬态EMG信号的PGT收集的训练数据生成的分类器，能更准确地反映模式识别控制的肌电假肢实时使用过程中的肌肉活动。
