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

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Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
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Special Emphasis Panel (ZRG1-MOSS-G (53))
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Shinowara, Nancy
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Boston University
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De Luca, Carlo J; Contessa, Paola (2015) Biomechanical benefits of the Onion-Skin motor unit control scheme. J Biomech 48:195-203
De Luca, Carlo J; Kline, Joshua C; Contessa, Paola (2014) Transposed firing activation of motor units. J Neurophysiol 112:962-70
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
Contessa, Paola; De Luca, Carlo J (2013) Neural control of muscle force: indications from a simulation model. J Neurophysiol 109:1548-70
De Luca, C J; Kline, J C (2012) Influence of proprioceptive feedback on the firing rate and recruitment of motoneurons. J Neural Eng 9:016007
Zaheer, Farah; Roy, Serge H; De Luca, Carlo J (2012) Preferred sensor sites for surface EMG signal decomposition. Physiol Meas 33:195-206
De Luca, Carlo J; Contessa, Paola (2012) Hierarchical control of motor units in voluntary contractions. J Neurophysiol 107:178-95
Nawab, S Hamid; Cole, Bryan T (2011) What is IPUS and how does it help resolve biosignal complexity? Conf Proc IEEE Eng Med Biol Soc 2011:4840-3
De Luca, Carlo J; Hostage, Emily C (2010) Relationship between firing rate and recruitment threshold of motoneurons in voluntary isometric contractions. J Neurophysiol 104:1034-46

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