Sentence examples for emg intensity from inspiring English sources

Eight studies compared mean differences in normalised EMG intensity (%MVC EMG) during active and functional tasks [ 6, 7, 12- 17], and three studies compared EMG onset timing [ 18- 20], two studies during active movement [ 19, 20] and one study during the reaction when the arm was unexpectedly released from a passive support [ 18].

Each muscle was assessed by EMG intensity (%MVC EMG)) and by the EMG timing if available.

Thus, although the differences between the pre- and post-training EMG intensity patterns were not always consistent, our findings did suggest that there is the potential for wavelet analyses and pattern classification techniques to be used to examine neuromuscular adaptations during resistance training.

There are major points to consider regarding the interpretation of EMG intensity.

Nine studies documented in eleven papers, eight comparing EMG intensity and three comparing EMG onset timing, representing 141 people with SIS and 138 controls were included.

The mean frequency of the preceding 20 s was subtracted from the distension value to give the EMG intensity as data points.

So, we aim to determine the best-fit parameters of these well-known and established DP models and introduce the model best describing the EMG intensity-response relationship of the epicondyle for tennis elbow endpoint.

Normalized EMG, pain intensity and lumbo-pelvic kinematics were compared across experimental conditions.

To quantify the relationship between tenderness of tennis elbow and EMG voltage intensity measured from the epicondyle, which identify a more specific EMG relationship [ 23– 26].

Following conventional methodology [15], the intensity of EMG modulations was quantified as rectified EMG.

To get deeper insights, linear and nonlinear random forests-based prediction models relating a single output variable, e.g. a thorax peak rotational speed, with a set of input variables, e.g. an average intensity of EMG signals were used.