Evolving connectionist systems for adaptive sport coaching

Contemporary computer assisted coaching software operates either on a particular sub-space of the wider problem or requires expert(s) to operate and provide explanations and recommendations. This paper introduces a novel motion data processing methodology oriented to the provision of future generation sports coaching software. The main focus of investigation is the development of techniques that facilitate processing automation, incremental learning from initially small data sets, and robustness of architecture with a degree of interpretation on individual sport performers’ motion techniques. Findings from a case study using tennis motion data verify the prospect of building similar models and architectures for other sports or entertainment areas in which the aims are to improve human motion efficacy and to prevent injury. A central feature is the decoupling of the high-level analytical architecture from the low-level processing of motion data acquisition hardware, meaning that the system will continue to work with future motion acquisition devices

BRIDGING THE GAP BETWEEN BIOMECHANICS AND ARTIFICIAL INTELLIGENCE

In contrast to widely researched areas of convergence such as between Artificial Intelligence (AI) and medicine there is minimal evidence of AI in biomechanics for sports. The main focus of this paper is the development of AI coaching systems with a high degree of autonomy that can discover new knowledge from data. This paper relates to areas of AI, biomechanical data, and qualitative analysis of human movement. It first provides an overall rationale for possible AI implementations then reports machine learning related findings from AI golf coaching software and a tennis coaching prototype. A point of view, presented as a scope, is that the future role of AI in sport coaching is about automation, knowledge discovery and enhanced human-like interaction

Pilot Your Life Decisively for Well-Being and Flourishing

I have been a pilot, aviation instructor and FAA Pilot Examiner for over 40 years. Aviation requires a “pilot in command” mindset consistent with the tenets of positive psychology. This paper explains and advocates for this daily empowered, adaptive decision making process used by pilots in aviation as a necessary life skill to eliminate mind wandering and disengagement and optimize human performance consistent with the goals of positive psychology. Exploring the concepts of “pilot-in-command” (decisive control and self-efficacy) and “situational awareness” (alert mental functioning) I will offer techniques and suggestions for developing and deploying these critical skills in everyday life. I will examine the heuristic-based, “fast and frugal” (time and data limited) decision-making used every day in aviation and apply this to life for optimal performance and flourishing for individual lives and organizational effectiveness

Life Long Learning In Sparse Learning Environments

Life long learning is a machine learning technique that deals with learning sequential tasks over time. It seeks to transfer knowledge from previous learning tasks to new learning tasks in order to increase generalization performance and learning speed. Real-time learning environments in which many agents are participating may provide learning opportunities but they are spread out in time and space outside of the geographical scope of a single learning agent. This research seeks to provide an algorithm and framework for life long learning among a network of agents in a sparse real-time learning environment. This work will utilize the robust knowledge representation of neural networks, and make use of both functional and representational knowledge transfer to accomplish this task. A new generative life long learning algorithm utilizing cascade correlation and reverberating pseudo-rehearsal and incorporating a method for merging divergent life long learning paths will be implemented

THE RELATIONSHIP BETWEEN DELIBERATE PRACTICE AND READING ABILITY

Many students are not prepared to meet the literacy demands of college and career as defined by the Common Core State Standards (2010). Literacy researchers have struggled to define the frequency and type of reading practice necessary to nurture the development of reading ability. The principles of deliberate practice provide a theoretical framework that could describe the type of practice necessary to develop expertise in reading. The purpose of this study was to explore the relationship between deliberate practice and reading ability. In this study, an educational technology, Learning Oasis, was used to deliver deliberate practice and monitor change in student reading ability over time. The hypotheses were that participants that engaged in more deliberate practice, as operationalized in this study, would experience more rapid growth and achieve higher levels of reading ability. Participants in this study (N = 1,369) ranged from grades one through twelve and were from a suburban school district in Mississippi. Each participant had at least three measurement occasions separated by at least three months each. The Lexile Framework for Reading was used to estimate participant reading ability during this research. Given the longitudinal nature of the data, a multilevel model was used to explore individual change over time. A negative exponential functional form was determined to best model change in participant reading ability over time. The results showed that, on average, participants that engaged in more deliberate practice (i.e., targeted practice with immediate feedback completed intensely over a long period of time) grew more rapidly and reached a higher ability level than participants that completed less deliberate practice. Implications for educators, educational technology designers, and researchers are discussed along with potential future areas of research.Doctor of Philosoph

Western Oregon University 2005-2006 Course Catalog

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Cognition in orienteering

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