Efficient and effective change principles in active videogames

Active videogames have the potential to enhance population levels of physical activity but have not been successful in achieving this aim to date. This article considers a range of principles that may be important to the design of effective and efficient active videogames from diverse discipline areas, including behavioral sciences (health behavior change, motor learning, and serious games), business production (marketing and sales), and technology engineering and design (human–computer interaction/ergonomics and flow). Both direct and indirect pathways to impact on population levels of habitual physical activity are proposed, along with the concept of a game use lifecycle. Examples of current active and sedentary electronic games are used to understand how such principles may be applied. Furthermore, limitations of the current usage of theoretical principles are discussed. A suggested list of principles for best practice in active videogame design is proposed along with suggested research ideas to inform practice to enhance physical activity.

Three algorithms for learning artificial neural network: A comparison for induction motor flux estimation

This paper presents a comparative study of three algorithms for learning artificial neural network. As neural estimator, back-propagation (BP) algorithm, uncorrelated real time recurrent learning (URTRL) algorithm and correlated real time recurrent learning (CRTRL) algorithm are used in the present work to learn the artificial neural network (ANN). The approach proposed here is based on the flux estimation of high performance induction motor drives. Simulation of the drive system was carried out to study the performance of the motor drive. It is observed that the proposed CRTRL algorithm based methodology provides better performance than the BP and URTRL algorithm based technique. The proposed method can be used for accurate measurement of the rotor flux.

Motor fault detection and diagnosis using a hybrid FMM-CART model with online learning

In this paper, a hybrid online learning model that combines the fuzzy min-max (FMM) neural network and the Classification and Regression Tree (CART) for motor fault detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, incorporates the advantages of both FMM and CART for undertaking data classification (with FMM) and rule extraction (with CART) problems. In particular, the CART model is enhanced with an importance predictor-based feature selection measure. To evaluate the effectiveness of the proposed online FMM-CART model, a series of experiments using publicly available data sets containing motor bearing faults is first conducted. The results (primarily prediction accuracy and model complexity) are analyzed and compared with those reported in the literature. Then, an experimental study on detecting imbalanced voltage supply of an induction motor using a laboratory-scale test rig is performed. In addition to producing accurate results, a set of rules in the form of a decision tree is extracted from FMM-CART to provide explanations for its predictions. The results positively demonstrate the usefulness of FMM-CART with online learning capabilities in tackling real-world motor fault detection and diagnosis tasks. © 2014 Springer Science+Business Media New York.

Fuzzy system with tabu search learning for classification of motor imagery data

This paper introduces an approach to classify EEG signals using wavelet transform and a fuzzy standard additive model (FSAM) with tabu search learning mechanism. Wavelet coefficients are ranked based on statistics of the Wilcoxon test. The most informative coefficients are assembled to form a feature set that serves as inputs to the tabu-FSAM. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II are employed for the experiments. Classification performance is evaluated using accuracy, mutual information, Gini coefficient and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The proposed tabu-FSAM method considerably dominates the competitive classifiers, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II.

Retroactive interference and mental practice effects on motor performance : a pilot study

This study examined the effect of similar versus dissimilar retroactive interference on the mental practice effects for performing a novel motor skill. Research has shown that mental practice of a motor task can interfere with learning and performance of the task; however, little is known about how different retroactive interference activities affect mental practice effects. 90 volunteers ages 18 to 51 years (M=26.8, SD=9.6) completed a pre-test and post-test of 10 sets of five trials of a throwing task with the non-preferred hand. In the practice phase, participants mentally practiced the throwing task and then mentally practiced a task that was similar, dissimilar, or completed an unrelated reading task. Performance for all groups improved from pre- to post-test; however, there were no differences in increases for the three groups. The findings suggest that mental practice of similar and dissimilar tasks produced no significant interference in performance.

Genetic algorithm based PI controller tuning for induction motor drive with ANN flux estimator

This paper presents a Genetic Algorithm (GA) based fast speed response controller for poly-phase induction motor drive. Here the proportional and integral gains of PI controller are optimized by GA to achieve quick speed response. An adaptive Recurrent Neural Network (RNN) with Real Time Recurrent Learning (RTRL) algorithm is proposed to estimate rotor flux. An online tuning scheme to update the weight of RNN is presented to overcome stator resistance variation problem. This tuning scheme requires torque estimator to calculate the torque error. Space vector modulation (SVM) technique is used to produce the motor input voltage. Simulation tests have been performed to study the dynamic performances of the drive system for both the classical PI and the genetic algorithm based PI controllers.

An examination of the influence of visuomotor associations on interpersonal motor resonance

The adaptation account of mirror neurons in humans proposes that mirror systems have been selected for in evolution to facilitate social cognition. By contrast, a recent "association" account of mirror neurons in humans argues that mirror systems are not the result of a specific adaptation, but of sensorimotor learning arising from concurrent visual and motor activity. Here, we used transcranial magnetic stimulation (TMS) and electromyography (EMG) to evaluate whether visuomotor associations affect interpersonal motor resonance, a putative measure of mirror system activity. 18 participants underwent two TMS sessions exploring whether visuomotor associations established throughout one׳s lifespan, namely common movements and movements generated from one׳s own perspective, are associated with increased putative mirror system activity. Our results showed no overall difference in interpersonal motor resonance to common versus uncommon actions, or actions presented from an egocentric (self) versus an allocentric (other) perspective. We did, however, observe increased interpersonal motor resonance within the abductor digiti minimi (ADM) muscle in response to allocentric compared to egocentric movements. As the association model predicts stronger mirror system response to actions with stronger visuomotor associations, such as common movements and those presented from an egocentric perspective, our findings provide little evidence to support the association model.

Kinematic and kinetic improvements associated with action observation facilitated learning of the power clean in Australian footballers

This study investigated the effectiveness of action observation (AO) on facilitating learning of the power clean technique (kinematics) compared with traditional strength coaching methods and whether improvements in performance (kinetics) were associated with an improvement in lifting technique. Fifteen subjects (age, 20.9 ± 2.3 years) with no experience in performing the power clean exercise attended 12 training and testing sessions over a 4-week period. Subjects were assigned to 2 matched groups, based on preintervention power clean performance and performed 3 sets of 5 repetitions of the power clean exercise at each training session. Subjects in the traditional coaching group (TC; n = 7) received the standard coaching feedback (verbal cues and physical practice), whereas subjects in the AO group (n = 8) received similar verbal coaching cues and physical practice but also observed a video of a skilled model before performing each set. Kinematic data were collected from video recordings of subjects who were fitted with joint center markings during testing, whereas kinetic data were collected from a weightlifting analyzer attached to the barbell. Subjects were tested before intervention, at the end of weeks 2 and 3, and at after intervention at the end of week 4. Faster improvements (3%) were observed in power clean technique with AO-facilitated learning in the first week and performance improvements (mean peak power of the subject's 15 repetitions) over time were significant (p < 0.001). In addition, performance improvement was significantly associated (R = 0.215) with technique improvements. In conclusion, AO combined with verbal coaching and physical practice of the power clean exercise resulted in significantly faster technique improvements and improvement in performance compared with traditional coaching methods.