This Phase I SBIR describes the first step towards the commercial development of an innovative technology for recording and decomposing the surface electromyographic (sEMG) signal into its constituent motor unit action potentials (motoneuron firings). The proposed SBIR will provide a much needed mechanism to further develop, harden, and make more user-friendly technology developed in our laboratory under the support of a parent R01. The impact of this project will be to provide the research community-at-large a tool to perform motor control investigations based on motoneuron firing behavior not otherwise possible, and to explore the workings of the normal or dysfunctional neuromuscular system. There are no commercially available systems designed for this purpose. In response to insistent requests from colleagues we have provided our lab-based technology in its un refined form to a few of them. The objective of Phase I is to establish the merit/feasibility of the R&D effort for Phase II by developing and testing key elements of a marketable system. The approach will combine our proven product-development skills as the leading sEMG company in the world, with our laboratory-based R&D that has developed the state-of-the art MU decomposition technology. Phase I will begin: a) transferring the current disparate laboratory-based software components for recording sEMG signals in a manner that makes them conducive for decomposition by the algorithms in an organized and easy to use commercial platform (Aim 1);developing new post-processing analysis software in a user-friendly format that broadens the ability of researchers to analyze MU firings (Aim 2);and c) expanding the current technology to enable the analysis of MUs during single-cycle upper limb movements, and prepare for Phase II analysis of gait and other functional applications (Aim 3). Evaluation and feedback from prospective end users will guide the aims towards a marketable system. The proposed deliverable at the end of Phase II will consist of: i) a body-worn data-logger (to be developed in Phase II by adapting proven technology from our product line) that supports either stationary or ambulatory recording of sensor data, and ii) PC-based decomposition software (developed in Phase I and II) that enables the researcher to easily set up data collection experiments, monitor signal quality, manage data files, perform offline decomposition, and provide selectable MU data plots and advanced analyses.

Public Health Relevance

The introduction of a non-invasive tool to perform motor control investigations not otherwise possible will enable researchers to delineate the neural contributions to deficits or gains in muscle strength, dexterity, coordination, balance, and involuntary movements. This information will enable clinicians to design more directed care for reversing the effects of neurological damage or counteracting age-related deficiencies in muscle performance. Such evidence-based interventions would lead to more efficient allocation of health resources and improve quality of life.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Small Business Innovation Research Grants (SBIR) - Phase I (R43)
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Special Emphasis Panel (ZRG1-MOSS-F (15))
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Gnadt, James W
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Altec, Inc.
United States
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Contessa, Paola; Puleo, Alessio; De Luca, Carlo J (2016) Is the notion of central fatigue based on a solid foundation? J Neurophysiol 115:967-77
Kline, Joshua C; De Luca, Carlo J (2016) Synchronization of motor unit firings: an epiphenomenon of firing rate characteristics not common inputs. J Neurophysiol 115:178-92
Kline, Joshua C; De Luca, Carlo J (2014) Error reduction in EMG signal decomposition. J Neurophysiol 112:2718-28
De Luca, Carlo J; Kline, Joshua C (2014) Statistically rigorous calculations do not support common input and long-term synchronization of motor-unit firings. J Neurophysiol 112:2729-44