Climbing Routes Clustering Using Energy-Efficient Accelerometers Attached to the Quickdraws
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866916159047073792 |
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| author | Moaveninejad, Sadaf Janes, Andrea Porcaro, Camillo Barletta, Luca Mucchi, Lorenzo Pierobon, Massimiliano |
| author_facet | Moaveninejad, Sadaf Janes, Andrea Porcaro, Camillo Barletta, Luca Mucchi, Lorenzo Pierobon, Massimiliano |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2211_02680 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Climbing Routes Clustering Using Energy-Efficient Accelerometers Attached to the Quickdraws Moaveninejad, Sadaf Janes, Andrea Porcaro, Camillo Barletta, Luca Mucchi, Lorenzo Pierobon, Massimiliano Signal Processing Artificial Intelligence Machine Learning 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. |
| title | Climbing Routes Clustering Using Energy-Efficient Accelerometers Attached to the Quickdraws |
| topic | Signal Processing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2211.02680 |