Energetic cost of running is indipendent from running velocity, but there is significant inter-runner variance. That means, energetic cost of running depends on runner experience/tehnique, body characteristics and skeletal muscle composition. In this complex project we provided data from our sensor for muscle compisition and muscle tone, to explain that both are predictors of running energetic cost.
It was a first presentation of a newly developed marker for muscle atrophy. We presented resutls of validation study and explain mechanisms that affects its value. Furthermore, we have demonstrated several fields of use with preliminary results of our applied studies
A signal-based Pulse-to-Noise (PNR) metrics for assessment of accuracy of motor unit identification from high-density surface electromyograms (EMG) is introduced. This metric is computationally efficient, does not require any additional experimental costs and can be applied to every motor unit that is identified by the previously developed Convolution Kernel Compensation technique. The analytical derivation of the newly introduced metrics is provided, along with its extensive experimental validation on both synthetic and experimental surface EMG signals. This manuscript has been selected by Journal of Neural Engineering as one of the sixteen highlights of 2014.