Smith Ryan J, Huberdeau David, Tenore Francesco, Thakor Nitish V
Biomedical Engineering department at The Johns Hopkins University, Baltimore, MD, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2376-9. doi: 10.1109/IEMBS.2009.5334981.
This study presents the development of a myoelectric decoding algorithm capable of continuous online decoding of finger movements with the intended eventual application for use in prostheses for transradial amputees. The effectiveness of the algorithm was evaluated through controlling a multi-fingered hand in a virtual environment. Two intact limbed adult subjects were able to use myoelectric signals collected from 8 bipolar electrodes to control four fingers in real-time to touch and maintain contact with targets appearing at various points in the flexion space of the hand. In these tasks, subjects achieved accuracies of 94% when target regions extended +/- 11.5 degrees about a target angle and 81% when the target region extended only +/- 5.75 degrees about the target angle. The real-time virtual system provides a practical and economic way to develop and train algorithms and amputee subjects using dexterous prostheses.
本研究展示了一种肌电解码算法的开发，该算法能够对手指运动进行连续在线解码，最终旨在应用于经桡骨截肢者的假肢。通过在虚拟环境中控制多指手来评估该算法的有效性。两名肢体健全的成年受试者能够使用从8个双极电极收集的肌电信号实时控制4根手指，以触摸并保持与出现在手部屈曲空间不同位置的目标接触。在这些任务中，当目标区域围绕目标角度扩展±11.5度时，受试者的准确率达到94%；当目标区域仅围绕目标角度扩展±5.75度时，准确率为81%。实时虚拟系统为开发和训练算法以及使用灵巧假肢的截肢者受试者提供了一种实用且经济的方法。
