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.

Public Health Relevance

The introduction of a non-invasive tool to perform motor control investigations during gait and other dynamic activities, not otherwise possible, will enable researchers and clinicians to delineate the neural contributions to deficits or gains in muscle strength, dexterity, coordination, balance, and involuntary movements. This information can be used 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 II (R44)
Project #
Application #
Study Section
Musculoskeletal Rehabilitation Sciences Study Section (MRS)
Program Officer
Gnadt, James W
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Altec, Inc.
United States
Zip Code
Contessa, Paola; Letizi, John; De Luca, Gianluca et al. (2018) Contribution from motor unit firing adaptations and muscle coactivation during fatigue. J Neurophysiol 119:2186-2193
De Luca, Carlo J; Kline, Joshua C (2016) The common input notion, conceived and sustained by conjecture. J Neurophysiol 115:1079-80
Contessa, Paola; De Luca, Carlo J; Kline, Joshua C (2016) The compensatory interaction between motor unit firing behavior and muscle force during fatigue. J Neurophysiol 116:1579-1585
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
De Luca, Carlo J; Nawab, S Hamid; Kline, Joshua C (2015) Clarification of methods used to validate surface EMG decomposition algorithms as described by Farina et al. (2014). J Appl Physiol (1985) 118:1084
De Luca, Carlo J; Chang, Shey-Sheen; Roy, Serge H et al. (2015) Decomposition of surface EMG signals from cyclic dynamic contractions. J Neurophysiol 113:1941-51
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