Climbing Routes Clustering Using Energy-Efficient Accelerometers Attached to the Quickdraws
This addresses a specific challenge for climbing gyms to optimize services and infrastructure, but it is incremental as it applies existing sensor and clustering methods to a new domain.
The paper tackled the problem of identifying popular climbing routes in gyms by developing an energy-efficient accelerometer sensor attached to quickdraws to collect data, and it resulted in an unsupervised clustering approach for route analysis.
One of the challenges for climbing gyms is to find out popular routes for the climbers to improve their services and optimally use their infrastructure. This problem must be addressed preserving both the privacy and convenience of the climbers and the costs of the gyms. To this aim, a hardware prototype is developed to collect data using accelerometer sensors attached to a piece of climbing equipment mounted on the wall, called quickdraw, that connects the climbing rope to the bolt anchors. The corresponding sensors are configured to be energy-efficient, hence becoming practical in terms of expenses and time consumption for replacement when used in large quantities in a climbing gym. This paper describes hardware specifications, studies data measured by the sensors in ultra-low power mode, detect patterns in data during climbing different routes, and develops an unsupervised approach for route clustering.