Intelligent assistant system for the automatic assessment of fall processes in sports climbing for injury prevention based on inertial sensor data

Abstract

In this work, an assistant system is presented for the automatic assessment of falling and belaying in sport climbing. Both climber and belayer are equipped with inertial measurement unit (IMU) sensors. Forces as well as movements in the form of multi-dimensional accelerations on the legs and torso are captured. It can be shown that forces can be estimated by means of IMU sensors, thus eliminating a complex force measurement unit in the safety chain. Furthermore, the data can be used to assess both falling and belaying by automatic segmentation and evaluation algorithms. The sensor data should later be evaluated automatically in order to objectively measure faulty behavior by climber or belayer (for example wrong jump-off behavior, too hard protection, etc.). The overall goal is to provide quantified feedback in fall training for injury and accident prevention.