Tracking climbers' activity to improve services and make the best use of their infrastructure is a concern for climbing gyms. Each climbing session must be analyzed from beginning till lowering of the climber. Therefore, spotting the climbers descending is crucial since it indicates when the ascent has come to an end. This problem must be addressed while preserving 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 become practical in terms of expenses and time consumption for replacement when using in large quantity in a climbing gym. This paper describes hardware specifications, studies data measured by the sensors in ultra-low power mode, detect sensors' orientation patterns during lowering different routes, and develop an supervised approach to identify lowering.

翻译：追踪攀岩者的活动以优化服务并充分利用设施，是攀岩馆关注的重点。每次攀岩过程需从开始至运动员下降的全阶段进行分析。因此，识别攀岩者下降时刻至关重要，因其标志着攀登阶段的终结。该问题需在保障攀岩者隐私便利性及攀岩馆成本控制的前提下解决。为此，我们开发了硬件原型设备，通过安装在岩壁快挂（连接攀岩绳与锚栓的器材）上的加速度传感器采集数据。采用低功耗配置的传感器在实际应用中能耗经济，且适用于攀岩馆大规模部署时的更换成本与时间效率考量。本文论述硬件规格参数，研究超低功耗模式下传感器的测量数据，检测不同路线下降过程中的传感器姿态模式，并提出基于监督学习的下降识别方法。