Non-invasive System for Identifying Motoneuron Behavior

这一第二阶段SBIR[PA-11-134]将完成一种商用前、非侵入性的成功开发 一种测量技术，可以从表面检测到的发动机单元(MU)的点火情况进行识别 由导致肢体运动(不等长收缩)的收缩产生的肌电信号，如步态 和锻炼。MU提供了神经肌肉系统中产生力量的基本单位，而 能够测量它们的控制特性是理解人类运动的关键因素。进阶 需要用于MU检测的工具来了解、评估和改进 健康和受损人群。目前的MU检测技术是有创的(将针头传感器插入到 肌肉)，高度受限(不会导致肢体运动的等长收缩)，以及有限 输出(通常为3-6微秒)。拟议的第二阶段SBIR将提供一个可以分解的非侵入性系统 各向异性收缩的表面肌电(SEMG)信号，以识别心脏的放电情况 多达25个同时活动的MU，准确率为95%。该项目建立在我们开发的 在等长收缩条件下从非侵入性传感器中识别MU的技术。它遵循的是 第一阶段的演示表明，我们的母技术可以扩展到识别 在有限的一组各向异性收缩期间的sEMG信号的MUS(1R43NS077526-0)。这项研究 第二阶段的战略建立在第一阶段验证的信号处理方法的基础上，以产生增强的 软件算法，从更广泛的范围内产生更多的准确MU发射次数 不等长收缩条件和肌肉群。该项目包括修改目前的一项 可用的可穿戴数据记录器和传感器，将与软件算法集成，以提供 强化的商用前系统，支持协议开发、动态记录、表面肌电信号 分解和高级后处理分析。这项工作的影响将是提供大脑和 行为研究人员和临床医生拥有执行运动控制调查的工具，否则是不可能的。 这将允许更多的最终用户更有效地探索正常或 神经肌肉系统功能障碍，导致改进的干预和管理。

This Phase II SBIR [PA-11-134] will complete the successful development of a pre-commercial, non-invasive measurement technology that can identify the firing instances of motor units (MUs) from surface-detected EMG signals produced by contractions that result in limb movements (anisometric contractions) such as gait and exercise. MUs provide the fundamental unit of force generation in the neuromuscular system, and the ability to measure their control properties is a key element to understanding human movement. Advanced tools for MU detection are needed for understanding, evaluating, and improving physical performance in healthy and impaired populations. Current MU detection technology is invasive (inserting a needle sensor into a muscle), highly constrained (for isometric contractions that do not result in limb movement), and of limited output (typically 3-6 MUs). The proposed Phase II SBIR will deliver a non-invasive system that can decompose the surface electromyographic (sEMG) signal from anisometric contractions, to identify the firing instances of as many as 25 concurrently active MUs with an accuracy >95%. The project builds upon our development of technology for identifying MUs from non-invasive sensors during isometric contraction conditions. It follows the demonstration in Phase I that our parent technology can be expanded to identify the firing instances of MUs from sEMG signals during a limited set of anisometric contractions (1R43NS077526-0). The research strategy in Phase II builds upon the signal processing approach validated in Phase I to produce enhanced software algorithms that yield significantly higher numbers of accurate MU firings from a broader range of anisometric contraction conditions and muscle groups. The project includes the modification of a currently available body-worn datalogger and sensor that will be integrated with the software algorithms to deliver a hardened pre-commercial system that supports protocol development, ambulatory recording, sEMG decomposition, and advanced post-processing analyses. The impact of this work will be to provide brain and behavior researchers and clinicians with a tool to perform motor control investigations not otherwise possible. This will allow a greater number of end users to more effectively explore the workings of the normal or dysfunctional neuromuscular system, leading to improved interventions and management.