Behaviour of human motor units in different muscles during linearly varying contractions

Abstract

contraction (m.v.c.). Experiments were performed on four normal subjects and three groups of highly trained performers (long-distance swimmers, powerlifters and pianists).3. Results revealed a highly ordered recruitment and decruitment scheme, based on motoneurone excitability, in both muscles and in all subject groups.4. Differenceswereobservedbetweentheinitial (recruitment)andfinal (decruitment) firing rates in each muscle. These parameters were invariant with respect to the force rates studied, although some differences were observed among subject groups.5. In general, firing rates of f.

Age-related changes in motor unit firing pattern of vastus lateralis muscle during low-moderate contraction

“…Previous studies demonstrated that motor unit firing/recruitment properties are influenced by the contraction speed and/or contraction type, i.e., isometric, concentric, or eccentric (De Luca et al 1982;Moritani et al 1987). In order to apply our findings to actual human movements, the further studies are needed to clarify the motor unit firing properties in the elderly during motor tasks under various conditions.…”

“…Also, we investigated the relationship between the motor unit firing rate and maximal muscle strength. We hypothesized that in the elderly, the firing rate of individual motor units is lower than that in the young; similarly to other muscles (Erim et al 1999;Kamen et al 1995;Nelson et al 1984;Patten et al 2001;Soderberg et al 1991), firing rates of earlier-recruited motor units are higher than those of later-recruited motor units at any time and force (De Luca and Hostage 2010;De Luca et al 1982;Erim et al 1996), and motor unit firing behavior is associated with maximal muscle strength. Since it was reported that the change in the motor unit firing rate during resistance training was greater in the elderly than the young (Christie and Kamen 2010;Patten et al 2001), motor unit firing rate may be more adaptable and variable in the elderly.…”

Motor Unit Potential Train Validation and Its Application in EMG Signal Decomposition

“…Once the decomposition process is completed, the prototypical MUP shape (MUP template) and MU firing pattern statistics for each extracted MUPT are estimated for future analysis (especially for quantitative electromyography). This provides information, regarding the temporal behaviour and morphological layout of the MUs that significantly contributed to the detected EMG signal, which can assist with the diagnosis of various neuromuscular diseases and the study of MU control, and lead to a better understanding of healthy, pathological, ageing or fatiguing neuromuscular systems ( De Luca et al, 1982a, 1982bStalberg & Falck, 1997;Tröger & Dengler, 2000;Stashuk, 2001;Fuglsang-Frederiksen, 2006;Calder et al, 2008;Farkas et al, 2010). However, this is achieved only when this information is valid.…”

“…The purpose of EMG signal decomposition is to provide an estimate of the firing pattern and motor unit potential (MUP) template of each active motor unit (MU) that contributed significant MUPs to the EMG signal. The extracted MU firing patterns, MUP templates, and their estimated feature values can assist with the diagnosis of neuromuscular disorders (Stalberg & Falck, 1997;Tröger & Dengler, 2000;Fuglsang-Frederiksen, 2006;Pino et al, 2008;Farkas et al, 2010), the understanding of motor control ( De Luca et al 1982a, 1982bContessa et al,2009), and the characterization of MU architecture (Lateva & McGill, 2001), but only if they are valid trains. Depending on the complexity of the signal being decomposed, the variability of MUP shapes and MU firing patterns, and the criteria and parameters used by the decomposition algorithm to merge or split the obtained MUPTs, several invalid MUPTs may be created.…”

Statistical Parametric Mapping (SPM) for alpha-based statistical analyses of multi-muscle EMG time-series

“…The smoothness of coordinated motion results from the time-dependent activation of individual motor units. The combination of a sequential recruitment of muscle fibers [De Luca et al, 1982] and biological elasticity allows smooth muscle forces to be generated. Raw EMG signals themselves are not smooth, so well established signal processing techniques are used to reduce the noise associated with signal acquisition and to better represent underlying muscle forces, although the quality of the representation is influenced by many factors [Disselhorst-Klug et al, 2009].…”