Myoelectric Control of Powered Upper Limb Prostheses

动力上肢假肢的肌电控制

• 批准号: RGPIN-2015-05539
• 负责人: Englehart, Kevin
• 金额: $ 2.19万
• 依托单位: University of New Brunswick
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=613610
• 关键词: Myoelectric; Control; Powered; Upper; Limb

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.

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。