Climbing Activity Recognition and Measurement with Sensor Data Analysis

The automatic detection of climbers activities can be the basis of software systems able to support trainers to assess the climbers performance and to define more effective training programs. We propose an initial building block of such a system, for the unobtrusive identification of the activity of someone pulling a rope after finishing the ascent. We use a novel type of quickdraw, augmented with a tri-axial accelerometer sensor. The acceleration data generated by the quickdraw during the climbs are used by a Machine Learning classifier for detecting the rope pulling activity. The obtained results show that this activity can be detected automatically with high accuracy, particularly by a Random Forest classifier. Moreover, we show that data acquired by the quickdraw sensor, as well as the detected rope pulling, can also be used to benchmark climbers.