Harnessing Motoneuron Activity: From Lab to Clinic

利用运动神经元活动：从实验室到诊所

• 批准号: 7860691
• 负责人: Carlo J De Luca
• 金额: $ 56.38万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-01 至 2012-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7860691
• 关键词: Action Potentials; Address; Age; Aging; Algorithms; Artificial Intelligence; Biomedical Engineering; Boston; Characteristics; Clinic; Clinical; Clinical Markers; Computer Systems Development; Computer software; Computers; Development; Elderly; Electrodes; Engineering; Exercise; Human Resources; Individual; Laboratories; Lead; Leadership; Manufacturer Name; Medicine; Modeling; Modification; Motor; Motor Neurons; Neurology; Performance; Physical activity; Process; Rehabilitation therapy; Shapes; Signal Transduction; Source; Staging; Surface; System; Technology; Testing; Time; Universities; Wolves; Work; advanced system; age related; commercialization; computerized data processing; design; improved; markov model; mathematical model; medical schools; meetings; motor control; neuroadaptation; neuromuscular system; new technology; programs; relating to nervous system; software development

DESCRIPTION (provided by applicant): We propose to continue the development of an automatic system that will accurately and quickly decompose electromyographic (EMG) signals into their constituent action potentials and provide the timing of every firing of a set of concurrently active motor units. This information will enable a wide range of studies to investigate the workings of the healthy and diseased neuromuscular system. We will improve the performance of the decomposition algorithms by incorporating new Artificial Intelligence concepts, and a new multi-strategy Hidden Markov Model (HMM) processing stage, to address signal decomposition challenges that cannot be met by the present technology. We will improve the present accuracy from typically 85% for 8 concurrently active motor units to greater than 96% for up to 15 concurrently active motor units. We will also design and build the hardware and software for a stand-alone portable system that may be used in the laboratory or clinic. Then we will transfer the system to a manufacturer for commercialization. In so doing we will produce, for the first time, an advanced system for conveniently and accurately obtaining the firings of a large group of concurrently active motor units from an EMG signal. The new technology will be tested in two applied studies that will be carried out concurrently with the technical developments. One will investigate neural modifications in the firing characteristics of motor units as a function of aging and physical activity. The other will investigate the mitigating effects of resistive exercise on age-related neural adaptations, culminating in the development of a clinical marker to estimate the likelihood that an elderly individual will benefit from an exercise program. The proposed BRP will be lead by Drs. De Luca, Roy, and Adam, key personnel from Boston University (BU) with expertise in biomedical engineering and EMG system development. Signal processing/software development will be provided by the leadership from Dr. Nawab through BU's Department of Electrical and Computer Engineering. Clinical expertise on aging/motor control will be provided by Dr. Novak, from The Department of Neurology at BU School of Medicine, and through Dr. Wolf, from the Department of Rehabilitation Medicine at the Emory University School of Medicine in Atlanta

描述（由申请人提供）：我们建议继续开发一种自动系统，该系统将准确、快速地将肌电图（EMG）信号分解为其组成的动作电位，并提供一组同时活动的运动单元的每次发射的时间。这些信息将使广泛的研究能够调查健康和患病神经肌肉系统的运作情况。我们将通过结合新的人工智能概念和新的多策略隐马尔可夫模型（HMM）处理阶段来提高分解算法的性能，以解决现有技术无法满足的信号分解挑战。我们将把目前的准确度从 8 个同时活动的运动单元的通常 85% 提高到最多 15 个同时活动的运动单元的 96% 以上。我们还将为可在实验室或诊所使用的独立便携式系统设计和构建硬件和软件。然后我们会将系统转移给制造商进行商业化。在此过程中，我们将首次生产出一种先进的系统，可以方便、准确地从肌电图信号中获取一大群同时活动的运动单元的放电情况。新技术将在两项应用研究中进行测试，这两项研究将与技术开发同时进行。人们将研究运动单位发射特性的神经变化作为衰老和身体活动的函数。另一个将研究阻力运动对与年龄相关的神经适应的缓解作用，最终开发出一种临床标记来估计老年人从运动计划中受益的可能性。拟议的 BRP 将由博士领导。 De Luca、Roy 和 Adam 是来自波士顿大学 (BU) 的关键人员，拥有生物医学工程和肌电图系统开发方面的专业知识。信号处理/软件开发将由 Nawab 博士通过 BU 电气与计算机工程系领导提供。波士顿大学医学院神经病学系的 Novak 博士和亚特兰大埃默里大学医学院康复医学系的 Wolf 博士将提供有关衰老/运动控制的临床专业知识