Surface electromyographic analysis of the biceps brachii muscle of cricket bowlers during bowling.

Cricket bowling generates forces with torques on the upper limb muscles and makes the biceps brachii (BB) muscle vulnerable to overuse injury. The aim of this study was to investigate whether there are differences in the amplitude of the EMG signal of the BB muscle during fast and spin delivery, during the seven phases of both types of bowling and the kinesiological interpretation of the bowling arm for muscle contraction mechanisms during bowling. A group of 16 male amateur bowlers participated in this study, among them 8 fast bowlers (FB) and 8 spin bowlers (SB). The root mean square (EMGRMS), the average sEMG (EMGAVG), the maximum peak amplitude (EMGpeak), and the variability of the signal were calculated using the coefficient of variance (EMGCV) from the BB muscle of each bowler (FB and SB) during each bowling phase. The results demonstrate that, (i) the BB muscle is more active during FB than during SB, (ii) the point of ball release and follow-through generated higher signals than the other five movements during both bowling categories, (iii) the BB muscle variability is higher during SB compared with FB, (iv) four statistically significant differences (p<0.05) found between the bowling phases in fast bowling and three in spin bowling, and (v) several arm mechanics occurred for muscle contraction. There are possible clinical significances from the outcomes; like, recurring dynamic contractions on BB muscle can facilitate to clarify the maximum occurrence of shoulder pain as well as biceps tendonitis those are medically observed in professional cricket bowlers, and treatment methods with specific injury prevention programmes should focus on the different bowling phases with the maximum muscle effect. Finally, these considerations will be of particular importance in assessing different physical therapy on bowler's muscle which can improve the ball delivery performance and stability of cricket bowlers.

Subtractive fuzzy classifier based driver distraction levels classification using EEG.

[Purpose] In earlier studies of driver distraction, researchers classified distraction into two levels (not distracted, and distracted). This study classified four levels of distraction (neutral, low, medium, high). [Subjects and Methods] Fifty Asian subjects (n=50, 43 males, 7 females), age range 20-35 years, who were free from any disease, participated in this study. Wireless EEG signals were recorded by 14 electrodes during four types of distraction stimuli (Global Position Systems (GPS), music player, short message service (SMS), and mental tasks). We derived the amplitude spectrum of three different frequency bands, theta, alpha, and beta of EEG. Then, based on fusion of discrete wavelet packet transforms and fast fourier transform yield, we extracted two features (power spectral density, spectral centroid frequency) of different wavelets (db4, db8, sym8, and coif5). Mean ± SD was calculated and analysis of variance (ANOVA) was performed. A fuzzy inference system classifier was applied to different wavelets using the two extracted features. [Results] The results indicate that the two features of sym8 posses highly significant discrimination across the four levels of distraction, and the best average accuracy achieved by the subtractive fuzzy classifier was 79.21% using the power spectral density feature extracted using the sym8 wavelet. [Conclusion] These findings suggest that EEG signals can be used to monitor distraction level intensity in order to alert drivers to high levels of distraction.

Effects of anthropometric variables and electrode placement on the SEMG activity of the biceps brachii muscle during submaximal isometric contraction in arm wrestling.

Surface electromyography (SEMG) has been widely used to analyze the biceps brachii (BB) muscle during voluntary contraction, and the effect of the interelectrode distance has been studied. However, the effect of anthropometric variations and the placement of electrodes on the BB activity during arm wrestling (i.e., during isometric contraction at a submaximal intensity) has seldom been investigated. In this study, the BB strength throughout this type of static contraction was evaluated. The SEMG signals were recorded from three locations on the BB: the muscle belly (M), near proximal (P), and distal tendon (L) regions. Twenty subjects who participated in the experiment were divided into five groups (A, B, C, D, and E). The average SEMG, root mean square, and variability of the signal were calculated using the coefficient of variance. The results indicated that the M region was more active and exhibited increased signal consistency (10.91%) compared with the other two regions (P: 24.47% and L: 19.13%). Significant differences were observed between the L and P regions and between the M and P regions (p<0.05); however, there were no differences between the M and L regions (p>0.05). The increase in the SEMG value in groups B and C was significant (p<0.05), whereas groups A, D, and E did not exhibit a significant increase (p>0.05). In addition, muscle size was the strongest predictor of strength compared with body weight and height. The results suggest that the M region displays considerable SEMG effects and signal reliability. Furthermore, the SEMG measurements were found to correlate strongly with the strength of the contractions and the muscle size, and not with weight and height.

Gait disorder rehabilitation using vision and non-vision based sensors: a systematic review.

Even though the amount of rehabilitation guidelines has never been greater, uncertainty continues to arise regarding the efficiency and effectiveness of the rehabilitation of gait disorders. This question has been hindered by the lack of information on accurate measurements of gait disorders. Thus, this article reviews the rehabilitation systems for gait disorder using vision and non-vision sensor technologies, as well as the combination of these. All papers published in the English language between 1990 and June, 2012 that had the phrases "gait disorder", "rehabilitation", "vision sensor", or "non vision sensor" in the title, abstract, or keywords were identified from the SpringerLink, ELSEVIER, PubMed, and IEEE databases. Some synonyms of these phrases and the logical words "and", "or", and "not" were also used in the article searching procedure. Out of the 91 published articles found, this review identified 84 articles that described the rehabilitation of gait disorders using different types of sensor technologies. This literature set presented strong evidence for the development of rehabilitation systems using a markerless vision-based sensor technology. We therefore believe that the information contained in this review paper will assist the progress of the development of rehabilitation systems for human gait disorders.