Myoelectric Control of Powered Upper Limb Prostheses

肢体的丧失或先天性缺失是一种重大残疾，会对截肢者的生活产生相当大的生理和心理影响。据估计，全球有300多万上肢截肢者，发病率稳步增长。更好的机器人假肢可以极大地改善上肢截肢者的生活质量，许多上肢截肢者拒绝使用现有的设备，因为他们无法像控制完好的手臂那样，以直觉和潜意识的方式控制它们。***肌电假肢利用残肢肌肉的肌电图（EMG）信号（肌肉收缩时产生的电信号）来控制机械手臂关节。肌电图的使用提供了一种非侵入性的方法来建立神经肌肉系统的自然界面，以控制肢体失去的功能。******虽然在过去的20年里，在制造更轻、更强、更多功能的假肢方面取得了重大进展，但在这些假肢的可行控制方面却几乎没有进展。使用过肌电假肢的人清楚地表明，控制的可靠性和灵活性是接受这些装置的最重要因素。******这项研究的目的是为肌电义肢提供健壮、灵巧的控制。尽管申请人和其他人已经在受控实验室环境中使用模式识别和基于回归的方法在肌电控制方面取得了巨大成功，但要使这些方法在用户家庭和工作环境中取得成功，需要在稳健性和灵活性方面取得进步，这将转化为用户体验的有意义的改进。这将通过引入创新的建模和信号处理范例来实现，包括模式识别、自适应学习和非线性回归方面的新方法。**

The loss or congenital absence of a limb is a major disability that can have considerable physical and psychological impact on the life of an amputee. It is estimated that there are more than 3 million upper limb amputees globally, with the rate of incidence growing steadily. Better robotic prostheses can dramatically improve the quality of life for persons with an upper limb amputation, many of whom reject existing devices because they have trouble controlling them in the same intuitive, subconscious way that they controlled their intact arms.***Myoelectric prostheses use the electromyogram (EMG) signals (the electrical signals generated during muscle contraction) from residual limb muscles to control motorized arm joints. The use of EMG offers a non-invasive means of establishing a natural interface to the neuromuscular system to control the lost functions of the limb.******Although significant advances have been made in building lighter, stronger and more versatile prostheses over the last 20 years, little progress has been made in viable control of these prostheses. Individuals who have used myoelectric prostheses clearly indicate that it is the reliability and dexterity of control that is the most significant factor in acceptance of these devices.******The objective of this research is to deliver robust, dexterous control to myoelectric prostheses. Although the applicant, and others, have demonstrated great success in myoelectric control using pattern recognition and regression based methods in controlled laboratory settings, the ability to have these methods succeed in user's homes and work environments requires advances in robustness and dexterity that will translate into a meaningful improvement in user experience. This will be accomplished by introducing innovative modeling and signal processing paradigms, including novel methods in pattern recognition, adaptive learning, and nonlinear regression. **