Pattern recognition neural classifier for fall detection in rock climbing

Abstract

From an athlete’s perspective, the identification of falls during rock climbing is of major importance. It constitutes a solid performance indicator, but more importantly, it could be used to trigger an instantaneous alarm to rescue teams, thus reducing the negative health consequences for the climber. In this context, an artificial neural network–based technique for fall detection during rock climbing is presented in this study. The output of this tool could be used for safety and performance monitoring purposes. The proposed method exploits a neural network for binary pattern recognition. This network is fed with a set of features extracted in real time from the acceleration and altitude signals acquired by means of a wearable device. The classifier is trained and validated with experimental datasets recorded during real climbing sessions of eight athletes through different route grades and conditions. This article illustrates the architecture of the proposed algorithm, feature extraction process, and evaluation of its accuracy. In addition, an analysis of the severity level of the detected falls is conducted. The method is able to identify real fall events with a high success rate, while yielding very few false positive indications of a fall.