Activation of the shoulder and arm muscles during axial load exercises on a stable base of support and on a medicine ball

The purpose of this study was to compare SEMG activities during axial load exercises on a stable base of support and on a medicine ball (relatively unstable). Twelve healthy male volunteers were tested (x = 23 +/- 7y). Surface EMG was recorded from the biceps brachii, anterior deltoid, clavicular portion of pectoralis major, upper trapezius and serratus anterior using surface differential electrodes. All SEMG data are reported as percentage of RMS mean values obtained in maximal voluntary contractions for each muscle studied. A 3-way within factor repeated measures analysis of variance was performed to compare RMS normalized values. The RMS normalized values of the deltoid were always greater during the exercises performed on a medicine ball in relation to those performed on a stable base of support. The trapezius showed greater mean electric activation amplitude values on the wall-press exercise on a medicine ball, and the pectoralis major on the push-up. The serratus and biceps did not show significant differences of electric activation amplitude in relation to both tested bases of support. Independent of the base of support, none of the studied muscles showed significant differences of electric activation amplitude during the bench-press exercise. The results contribute to the identification of the levels of muscular activation amplitude during exercises that are common in clinical practice of rehabilitation of the shoulder and the differences in terms of type of base of support used. (C) 2006 Elsevier Ltd. All rights reserved.

An association rule-based method to support medical image diagnosis with efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.

Customer emotions in service failure and recovery encounters

Emotions play a significant role in the workplace, and considerable attention has been given to the study of employee emotions. Customers also play a central function in organizations, but much less is known about customer emotions. This chapter reviews the growing literature on customer emotions in employee–customer interfaces with a focus on service failure and recovery encounters, where emotions are heightened. It highlights emerging themes and key findings, addresses the measurement, modeling, and management of customer emotions, and identifies future research streams. Attention is given to emotional contagion, relationships between affective and cognitive processes, customer anger, customer rage, and individual differences.