Neural Information Processing - Letters and Reviews

Vol. 12, Nos. 1-3, January-March 2008

 

pp. 53-62

 

Evolving Connectionist Systems for Adaptive Sports Coaching

 

Boris Bacic

Department School of Computing and Mathematical Sciences & Knowledge Engineering and Discovery

Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand

E-mail: boris.bacic@aut.ac.nz

 

Abstract

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: 1) simulating human coach inference and 2) 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.

 

Keywords − Classification, Coaching Rule, CREM, Coaching Scenario, ECOS, EFuNN, iB-fold, Feature Extraction, Local Personalized Global

                    Knowledge Integration, Orchestration, Weighted Sum