Effects of exercise order on upper-body muscle activation and exercise performance.

With the purpose of manipulating training stimuli, several techniques have been employed to resistance training. Two of the most popular techniques are the pre-exhaustion (PRE) and priority system (PS). PRE involves exercising the same muscle or muscle group to the point of muscular failure using a single-joint exercise immediately before a multi-joint exercise (e.g., peck-deck followed by chest press). On the other hand, it is often recommended that the complex exercises should be performed first in a training session (i.e., chest press before peck-deck), a technique known as PS. The purpose of the present study was to compare upper-body muscle activation, total repetitions (TR), and total work (TW) during PRE and PS. Thirteen men (age 25.08 +/- 2.58 years) with recreational weight-training experience performed 1 set of PRE and 1 set of PS in a balanced crossover design. The exercises were performed at the load obtained in a 10 repetition maximum (10RM) test. Therefore, chest press and peck-deck were performed with the same load during PRE and PS. Electromyography (EMG) was recorded from the triceps brachii (TB), anterior deltoids, and pectoralis major during both exercises. According to the results, TW and TR were not significantly different (p > 0.05) between PRE and PS. Likewise, during the peck-deck exercise, no significant (p > 0.05) EMG change was observed between PRE and PS order. However, TB activity was significantly (p < 0.05) higher when chest press was performed after the peck-deck exercise (PRE). Our findings suggest that performing pre-exhaustion exercise is no more effective in increasing the activation of the prefatigued muscles during the multi-joint exercise. Also, independent of the exercise order (PRE vs. PS), TW is similar when performing exercises for the same muscle group. In summary, if the coach wants to maximize the athlete performance in 1 specific resistance exercise, this exercise should be placed at the beginning of the training session.

Compression of EMG signals with wavelet transform and artificial neural networks.

This paper presents a hybrid adaptive algorithm for the compression of surface electromyographic (S-EMG) signals recorded during isometric and/or isotonic contractions. This technique is useful for minimizing data storage and transmission requirements for applications where multiple channels with high bandwidth data are digitized, such as telemedicine applications. The compression algorithm proposed in this work uses a discrete wavelet transform for spectral decomposition and an intelligent dynamic bit allocation scheme implemented by an approach using the Kohonen layer, which improves the bit allocation for sections of the S-EMG with different characteristics. Finally, data and overhead information are packed by entropy coding. The results for the compression of isometric EMG signals showed that this algorithm has a better performance than standard wavelet compression algorithms presented in the literature (presenting a decrease of at least 5% in per cent residual difference (PRD) for the same compression ratio), and a performance that is comparable with the performance of algorithms based on an embedded zero-tree wavelet. For isotonic EMG signals, its performance is better than the performance of the algorithms based on embedded zero-tree wavelets (presenting a decrease in PRD of about 3.6% for the same compression ratios, in the useful compression range).