Performance anxiety and the plasticity of emotional responses.

Current psychological theories of performance anxiety focus heavily on relating performers' physiological and mental states to their abilities to maintain focus and execute learned skills. How task-specific expertise and past experiences moderate the degree to which individuals become anxious in a given performance context are not well accounted for within these theories. This review considers how individual differences arising from learning may shape the psychobiological, emotional, and cognitive processes that modulate anxious states associated with the performance of highly trained skills. Current approaches to understanding performance anxiety are presented, followed by a critique of these approaches. A connectionist model is proposed as an alternative approach to characterising performance anxiety by viewing performers' anxious states at a specific time point as jointly determined by experience-dependent plasticity, competition between motivational systems, and ongoing cognitive and somatic states. Clarifying how experience-dependent plasticity contributes to the emergence of socio-evaluative anxiety in challenging situations can not only help performers avoid developing maladaptive emotional responses, but may also provide new clues about how memories of past events and imagined future states interact with motivational processes to drive changes in emotional states and cognitive processing.

Development of a Strategy to Predict and Detect Falls Using Wearable Sensors.

Falls are a prevalent problem in actual society. Some falls result in injuries and the cost associated with their treatment is high. This is a complex problem that requires several steps in order to be tackled. Firstly, it is crucial to develop strategies that recognize the locomotion mode, indicating the state of the subject in various situations. This article aims to develop a strategy capable of identifying normal gait, the pre-fall condition, and the fall situation, based on a wearable system (IMUs-based). This system was used to collect data from healthy subjects that mimicked falls. The strategy consists, essentially, in the construction and use of classifiers as tools for recognizing the locomotion modes. Two approaches were explored. Associative Skill Memories (ASMs) based classifier and a Convolutional Neural Network (CNN) classifier based on deep learning. Finally, these classifiers were compared, providing for a tool with a good accuracy in recognizing the locomotion modes. Results have shown that the accuracy of the classifiers was quite acceptable. The CNN presented the best results with 92.71% of accuracy considering the pre-fall step different from normal steps, and 100% when not considering.