Powell Michael A, Thakor Nitish V
Johns Hopkins University, Department of Biomedical Engineering.
J Prosthet Orthot. 2013 Jan 1;25(1):30-41. doi: 10.1097/JPO.0b013e31827af7c1.
Pattern recognition-based control of myoelectric prostheses offers amputees a natural, intuitive way of controlling the increasing functionality of modern myoelectric prostheses. While this approach to prosthesis control is certainly attractive, it is a significant departure from existing control methods. The transition from the more traditional methods of direct or proportional control to pattern recognition-based control presents a training challenge that will be unique to each amputee. In this paper we describe specific ways that a transradial amputee, prosthetist, and occupational therapist team can overcome these challenges by developing consistent and distinguishable muscle patterns. A central part of this process is the employment of a computer-based pattern recognition training system with which an amputee can learn and improve pattern recognition skills throughout the process of prosthesis fitting and testing. We describe in detail the manner in which four transradial amputees trained to improve their pattern recognition-based control of a virtual prosthesis by focusing on building consistent, distinguishable muscle patterns. We also describe a three-phase framework for instruction and training: 1) initial demonstration and conceptual instruction, 2) in-clinic testing and initial training, and 3) at-home training.
基于模式识别的肌电假肢控制为截肢者提供了一种自然、直观的方式来控制现代肌电假肢日益增强的功能。虽然这种假肢控制方法确实很有吸引力，但它与现有的控制方法有很大不同。从更传统的直接或比例控制方法向基于模式识别的控制转变带来了一个训练挑战，每个截肢者面临的挑战都将是独特的。在本文中，我们描述了经桡骨截肢者、假肢技师和职业治疗师团队通过开发一致且可区分的肌肉模式来克服这些挑战的具体方法。这一过程的核心部分是使用基于计算机的模式识别训练系统，截肢者可以在假肢适配和测试的整个过程中学习并提高模式识别技能。我们详细描述了四名经桡骨截肢者通过专注于构建一致、可区分的肌肉模式来训练以改善其对虚拟假肢基于模式识别的控制的方式。我们还描述了一个指导和训练的三阶段框架：1）初始演示和概念指导，2）临床测试和初始训练，3）在家训练。
