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One of the challenges for climbing gyms is to find out popular routes for theclimbers to improve their services and optimally use their infrastructure. Thisproblem must be addressed preserving both the privacy and convenience of theclimbers and the costs of the gyms. To this aim, a hardware prototype isdeveloped to collect data using accelerometer sensors attached to a piece ofclimbing equipment mounted on the wall, called quickdraw, that connects theclimbing rope to the bolt anchors. The corresponding sensors are configured tobe energy-efficient, hence becoming practical in terms of expenses and timeconsumption for replacement when used in large quantities in a climbing gym.This paper describes hardware specifications, studies data measured by thesensors in ultra-low power mode, detect patterns in data during climbingdifferent routes, and develops an unsupervised approach for route clustering.

Climbing Routes Clustering Using Energy-Efficient Accelerometers Attached to the Quickdraws