Quantification of forearm muscle activation by ultrasound imaging and high-density electromyography

Abstract Although mechanical ventilation is a lifesaving treatment, abundant evidence indicates that its prolonged use (1 week or more) promotes respiratory muscle weakness due to both contractile dysfunction and atrophy. Along with the diaphragm, the intercostal muscles are one of the most important groups of respiratory muscles. In recent years, muscular ultrasound has become a useful bedside tool for the clinician to identify patients with respiratory muscle dysfunction related to critical illness and/or invasive mechanical ventilation. Images obtained over the course of illness can document changes in muscle dimension and can be used to estimate changes in function. Recent evidence suggests the clinical usefulness of ultrasound imaging in the assessment of intercostal muscle function. In this narrative review, we summarize the current literature on ultrasound imaging of the parasternal intercostal muscles as used to assess the extent of muscle activation and muscle weakness and its potential impact during discontinuation of mechanical ventilation. In addition, we proposed a practical flowchart based on recent evidence and experience of our group that can be applied during the weaning phase. This approach integrates multiple predictive parameters of weaning success with respiratory muscle ultrasound.

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Influence of Ball Impact Location on Racquet Kinematics, Forearm Muscle Activation and Shot Accuracy during the Forehand Groundstrokes in Tennis

Proceedings ◽

10.3390/proceedings2020049089 ◽

2020 ◽

Vol 49 (1) ◽

pp. 89

Author(s):

Masahiro Ikenaga ◽

Nobue Okuma ◽

Hiroki Nishiyama ◽

Shinichiro Chiba ◽

Katsutoshi Nishino ◽

...

Keyword(s):

Muscle Activation ◽

Muscular Activity ◽

Side Impact ◽

Ball Impact ◽

Tennis Players ◽

Impact Location ◽

Flexor Muscles ◽

Flight Parameters ◽

Extensor Carpi Radialis ◽

Forearm Muscle

We aimed to clarify the effect of ball–racquet impact locations on the dynamic behavior of tennis racquet, the accuracy of shots and muscle activation of the forearm. Eight male intermediate tennis players performed ten forehand groundstrokes. A motion capture system was used to measure the motions of racquet, ball and human body at 2000 Hz, and electromyography (EMG) activities of wrist extensor and flexor muscles were measured simultaneously. The flight parameters of the ball were measured by ballistic measurement equipment. All shots were divided into tertiles based on ball impact location along the lateral axis of tennis racquet. We found that the off-center, upper-side impact induces a larger muscular activity in extensor carpi radialis. Passive radial deviation of the wrist occurring immediately after ball impact may account for this. Furthermore, the off-center, upper-side impact could be associated with a missed shot having a lower, outward ball launch angle.

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Extracting and Classifying Spatial Muscle Activation Patterns in Forearm Flexor Muscles Using High-Density Electromyogram Recordings

International Journal of Neural Systems ◽

10.1142/s0129065718500259 ◽

2019 ◽

Vol 29 (01) ◽

pp. 1850025 ◽

Cited By ~ 10

Author(s):

Chenyun Dai ◽

Xiaogang Hu

Keyword(s):

Pattern Recognition ◽

Spatial Patterns ◽

Muscle Activation ◽

High Density ◽

Emg Activity ◽

Finger Movement ◽

Activation Patterns ◽

Flexor Muscles ◽

Forearm Flexor Muscles ◽

Forearm Flexor

The human hand is capable of producing versatile yet precise movements largely owing to the complex neuromuscular systems that control our finger movement. This study seeks to quantify the spatial activation patterns of the forearm flexor muscles during individualized finger flexions. High-density (HD) surface electromyogram (sEMG) signals of forearm flexor muscles were obtained, and individual motor units were decomposed from the sEMG. Both macro-level spatial patterns of EMG activity and micro-level motor unit distributions were used to systematically characterize the forearm flexor activation patterns. Different features capturing the spatial patterns were extracted, and the unique patterns of forearm flexor activation were then quantified using pattern recognition approaches. We found that the forearm flexor spatial activation during the ring finger flexion was mostly distinct from other fingers, whereas the activation patterns of the middle finger were least distinguishable. However, all the different activation patterns can still be classified in high accuracy (94–100%) using pattern recognition. Our findings indicate that the partial overlapping of neural activation can limit accurate identification of specific finger movement based on limited recordings and sEMG features, and that HD sEMG recordings capturing detailed spatial activation patterns at both macro- and micro-levels are needed.

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