A challenge 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 privacy and convenience of the climbers. To this aim we developed a hardware and software prototype for the SALSA project. The hardware prototype collects climbing data using accelerometer sensors attached to a piece of climbing equipment named quickdraw that connects the climbing rope to the bolt anchors. The corresponding sensors are configured to be energy efficient, hence become practical when using in large quantity in a climbing gym. Regarding the software prototype, we analyzed data measured by the sensors, detect patterns during climbing of different routes, and develop an unsupervised approach for route clustering.
Climbing route clustering using energy efficient sensors