Non-invasive System for Identifying Motoneuron Behavior

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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.