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The prevalence of overweight and obesity has increased over the past 30years to be doubled in children and adolescents and become a major health problem in developing countries. This phenomenon, was often attributed to the engagement in sedentary behaviors and to the lower levels of physical activity.

Mentale und körperliche Gesundheit sind eng verbunden mit einem erfolgreichen Altern. Diese Studie untersucht die Auswirkungen eines Tanztrainings im Vergleich zu einem Kraft-/Ausdauertraining sowie der Kombination beider auf die fluide Intelligenz, die allgemeine Intelligenz und das Arbeitsgedächtnis älterer Menschen (M68 ± 4.16 Jahre). 95 Senioren wurden in folgenden Gruppen randomisiert: 1. Tanz, 2. Sport, 3. Tanz und Sport. Die Gruppe Tanz und die Gruppe Sport trainierten 90 Minuten, die Gruppe Tanz und Sport 180 Minuten für 15 Monate. Die Ergebnisse zeigen signifikante Leistungssteigerungen in allen gemessenen Merkmalen in den drei Gruppen. Post Hoc Vergleiche verdeutlichen, dass sich die Leistungssteigerungen zwischen den Gruppen signifikant nicht unterscheiden. Körperliche Bewegung jeglicher Art trägt zum erfolgreichen Altern bei.

The aim was to verify whether the gold winner presented better values than the other two teams in the final group competition with five balls at the Rhythmic Gymnastics World Championship 2011 (Montpellier, France). In other words, it has been researched if quantitative parameters played a fundamental role by determining the three final positions. Results showed that the most successful choreography does not include the highest number of quantitative parameters. Actually the performance in Rhythmic Gymnastics is influenced by quantity as well as by quality. In spite of several attempts to objectify the evaluation of performances in this discipline it is impossible to evaluate it in all its aspects up to the present. Thus, a future goal will be the objectification of the qualitative performance parameters, in order to provide more exhaustive choreography analyses.

It seems sensible to utilise performance analysis techniques in Rhythmic Gymnastics as well, as the performance structure in group routines is complex, and it is difficult to find the reasons for success or failure of a team. However, no such analyses of Rhythmic Gymnastics could be found in the published literature. Therefore, the aim of this study was to define effective methods for computerised analyses of group compositions in Rhythmic Gymnastics. These methods need to be suitable to analyse the world’s leading gymnastics groups, as well as to provide teams with feedback on their performance compared to that of the world leaders. The judgement of group competitions in rhythmic gymnastics is influenced by subjective aspects, although the evaluation is based on uniform criteria. It is a complex and difficult task because the execution of the movements and the change from one appliance to another takes place extremely fast. Therefore, one team of judges assesses the artistic aspects and another team assesses the difficulty of the choreography on the basis of a reference list. Quantitative parameters of the performances are not considered. Therefore, the performances are not exactly comparable. In order to get a detailed analysis of the actual performance of elite groups and to compare the choreography, a computer program has been developed. This program makes it possible:1. to document the participation of individual gymnasts during the execution of selected difficulties and while changing to another appliance,2. to produce an overview of the frequency of the individual elements,3. to determine the area covered, the number of appliance changes and the distances covered with each appliance, and4. to compare the synchronicity of executing the movements.Commercial software was identified that could be used to record the gymnasts’ positions and actions, and a new computer program was written to visualise the path of each gymnast. A complete analysis of the routines of 4 teams was performed, including the distance covered by each gymnast and the number of actions and interactions of the gymnasts was executed on the basis of videos from the World Cup of Rhythmic Gymnastics in Düsseldorf (Germany) 2005. A first analysis showed that the top teams differ from the German team in terms of the:1. number of big appliance changes,2. average width of the throws,3. distance run by the gymnasts,4. number of elements with high difficulties executed by all competitors,5. number of formation forms and6. practice speed.

Mental training aims to improve the foundation for cognitive regulation of complex movement performance. When mental representation of a sport skill is improved with the aid of systematic techniques, performance is enhanced considerably. The computer-aided tool “Gymnastic-Mental” should be tested during training practice to make mental training more effective and to improve motor performance. As a result of the testing, we looked for a training means that could be used in rhythmic gymnastics to optimize the training technique. In the “Gymnastic-Mental”-programme are selected model examples of sport-specific body techniques and combination techniques as figures. This system can influence the internal movement representations over the visual and acoustic analyzer. The pictures of the technique are to be arranged in the correct order for the movement. Indicators for quantifying the effects of practising and of cognitive learning gain are defined using the “error rate” as a measurement for accuracy of action and “cognitive time consumption” as a measurement for time taken to make decisions and hence availability of representation pattern. A group of 8 top-level gymnasts were tested. The cognitive and motor components were concurrently examined in pre- and post-tests. Mental Training was carried out on a daily basis over a one-week period. Practice was being done during this week, as well as for another seven weeks. The athletes were video recorded before and after the period of practicing. Data were collected and analysed with SIMI Motion. We looked for biomechanical parameters which are demonstrated the quality of the movement implementation. For our selected technique “double diving turn backward” a low back bend and big amplitude of the legs are important parameters. A significant improvement in learning was found. After one week of mental practice, 95% of the pictures were assigned correctly during the post-test conditions. The time required to select the pictures decreased on average about 80.6%. A significant improvement of the accomplishment could be proved after a period of seven weeks. The back bend increased on average about 15° and the angle between the legs about 8°. After one-week period of mental training exercise, it can be assumed that the programme supports mental learning (basic structure of the movement) and allows the control of movement association. As a result, mistakes associated with an incorrect movement can be assessed and removed. It becomes clear that the motor implementation still shows considerable deficienies. At the time a quantitative evaluation of the biomecanical parameters takes place.

Zusammenfassung.Die Theorie zur Wechselwirkung zwischen auditiven Wahrnehmung und motorischen Reaktionen bildet die Basis zur Annahme, dass zwischen musikerzeugenden Bewegungen und Bewegungsinterpretation von Musik ein existentieller Zusammenhang besteht. Durch das Bewegungsverhalten des Pianisten wird ein Klangbild erzeugt, welches die Tänzerin als motoakustische Information aufgreift. Dieses Klangbild enthält bewegungsdefinierende Merkmale und dient als Orientierungsgrundlage für die Bewegungsgestaltung. Es lassen sich Ähnlichkeiten zwischen den musikerzeugenden (Pianist) und den musikgestaltenden Bewegungsparametern (Tänzerin) in unterschiedlich definierten Räumen anhand der Körpergeometrie nachweisen. Daraus resultiert die Annahme, dass durch die “Handschrift” des Musikers eine Tanzchoreographie wesentlich geprägt wird.

Brain volume decrease is usually connected to neurodegeneration and aging. In this environment, an important percentage of elderly persons suffer from mild cognitive impairment (MCI), a kind of dementia that can lead to Alzheimer's disease (AD). Since the symptoms of cognitive impairment are scarcely discernible, developing a safe and effective method for early MCI detection has emerged as an important challenge. According to this regard, numerous cognitive training tests can be targeted to help aging people retain a good quality of life, especially in the case of fragility disorders. A Consortium to Establish a Registry for Alzheimer's Disease (CERAD) task was initially created to detect the early stages of AD. This task specifically targets various tests related to specific cognitive domains. However, it has since developed into a popular diagnostic tool for many kinds of dementia, such as MCI. Several low-cost equipment, such as electroencephalography (EEG) and heart rate variability (HRV), may be useful for predicting MCI. On the other side, various machine learning (ML) models can be employed to extract/analyze relevant features from biomedical and physiological signals, especially in the context of anomaly detection and classification. To this regard, we developed a new method based on ML models to categorize MCI and healthy control (HC) patients during the CERAD task using EEG and HRV multimodal data. Our dataset includes 15 subjects who were randomly assigned to training and testing groups of 7 HC and 8 MCI, respectively. Our raw EEG and HRV data are analyzed to extract time, frequency, and non-linear features. A scaling step is employed to reduce the significant disparity between features. For the classification task, five ML models are evaluated, including support vector machine (SVM), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), and gradient boosting (GB). To enhance accuracy, a hybrid ML model with a voting system is developed, combining the top ML models with the highest accuracy rates. A comparison step is performed between the use of ML and hybrid ML models. The experimental findings demonstrated the efficacy of our proposed technique, which included a hybrid ML model. An average accuracy of 93.86%, a sensitivity of 93.87%, and a specificity of 93.53% are achieved. The obtained results allow one to conclude that the first CERAD test plays a prominent role as a novel biomarker with an ultra-short duration for early MCI identification through the combination of EEG and HRV signals. [ABSTRACT FROM AUTHOR]