Echo state network for 3D motion pattern indexing: A case study on tennis forehands

Abstract

Open skill sports such as tennis have a large number of swing execution techniques. This study presents a novel approach to event detection and motion pattern indexing of forehand swings captured from fixed location multi-camera represented as a 3D motion data set of multi-time series sampled at 50 Hz. The achieved results utilising Echo State Network (ESN) demonstrate 100% recognition of tennis forehands from previously unseen test data without ball impact information. In contrast to traditional, heuristic and feature extraction-based algorithmic approaches in exergames and augmented coaching technologies, the proposed ESN paradigm represents a viable and generic approach for future work in temporal and spatial detection and automated analysis of region of interest in human motion data processing.