Neuromuscular response to whole body vibration treatment during static and dynamic squat exercises

The impact of whole body vibrations (vibration stimulus mechanically transferred to the body) on muscular activity and neuromuscular response has been widely studied but without standard protocol and by using different kinds of exercises and parameters. In this study, we investigated how whole body vibration treatments affect electromyographic signal of rectus femoris during static and dynamic squat exercises. The aim was the identification of squat exercise characteristics useful to maximize neuromuscular activation and hence progress in training efficacy. Fourteen healthy volunteers performed both static and dynamic squat exercises without and with vibration treatments. Surface electromyographic signals of rectus femoris were recorded during the whole exercise and processed to reduce artifacts and to extract root mean square values. Paired t-test results demonstrated an increase of the root mean square values (p<0.05) in both static and dynamic squat exercises with vibrations respectively of 63% and 108%. For each exercise, subjects gave a rating of the perceived exertion according to the Borg's scale but there were no significant changes in the perceived exertion rate between exercises with and without vibration. Finally, results from analysis of electromyographic signals identified the static squat with WBV treatment as the exercise with higher neuromuscular system response. © 2012 IEEE.

Stretching the life of Twitter classifiers with time-stamped semantic graphs

Social media has become an effective channel for communicating both trends and public opinion on current events. However the automatic topic classification of social media content pose various challenges. Topic classification is a common technique used for automatically capturing themes that emerge from social media streams. However, such techniques are sensitive to the evolution of topics when new event-dependent vocabularies start to emerge (e.g., Crimea becoming relevant to War Conflict during the Ukraine crisis in 2014). Therefore, traditional supervised classification methods which rely on labelled data could rapidly become outdated. In this paper we propose a novel transfer learning approach to address the classification task of new data when the only available labelled data belong to a previous epoch. This approach relies on the incorporation of knowledge from DBpedia graphs. Our findings show promising results in understanding how features age, and how semantic features can support the evolution of topic classifiers.

A study protocol of a randomised controlled trial to measure the effects of an augmented prescribed exercise programme (APEP) for frail older medical patients in the acute setting

Background: Older adults experience functional decline in hospital leading to increased healthcare burden and morbidity. The benefits of augmented exercise in hospital remain uncertain. The aim of this trial is to measure the short and longer-term effects of augmented exercise for older medical in-patients on their physical performance, quality of life and health care utilisation. Design and Methods: Two hundred and twenty older medical patients will be blindly randomly allocated to the intervention or sham groups. Both groups will receive usual care (including routine physiotherapy care) augmented by two daily exercise sessions. The sham group will receive stretching and relaxation exercises while the intervention group will receive tailored strengthening and balance exercises. Differences between groups will be measured at baseline, discharge, and three months. The primary outcome measure will be length of stay. The secondary outcome measures will be healthcare utilisation, activity (accelerometry), physical performance (Short Physical Performance Battery), falls history in hospital and quality of life (EQ-5D-5 L). Discussion: This simple intervention has the potential to transform the outcomes of the older patient in the acute setting.