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

• URL: http://arxiv.org/abs/2211.02680v2
• Date: Thu, 7 Mar 2024 11:36:54 GMT
• Title: Climbing Routes Clustering Using Energy-Efficient Accelerometers Attached to the Quickdraws
• Authors: Sadaf Moaveninejad, Andrea Janes, Camillo Porcaro, Luca Barletta, Lorenzo Mucchi, Massimiliano Pierobon
• Abstract summary: A prototype is developed to collect data using accelerometer sensors attached to a piece of climbing equipment mounted on the wall. The corresponding sensors are configured to be energy-efficient, hence becoming practical in terms of expenses and time consumption for replacement. 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.
• Score: 7.47577255773279
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Benchmarking Egocentric Visual-Inertial SLAM at City Scale [50.1245744173948]
  This paper introduces a new dataset and benchmark for visual-inertial SLAM with egocentric, multi-modal data.<n>We record hours and kilometers of trajectories through a city center with glasses-like devices equipped with various sensors.<n>We show that state-of-the-art systems developed by academia are not robust to these challenges and we identify components that are responsible for this.
  arXiv  Detail & Related papers  (2025-09-30T17:59:31Z)
• Efficient Motion Prompt Learning for Robust Visual Tracking [58.59714916705317]
  We propose a lightweight and plug-and-play motion prompt tracking method.<n>It can be easily integrated into existing vision-based trackers to build a joint tracking framework.<n>Experiments on seven tracking benchmarks demonstrate that the proposed motion module significantly improves the robustness of vision-based trackers.
  arXiv  Detail & Related papers  (2025-05-22T07:22:58Z)
• The Way Up: A Dataset for Hold Usage Detection in Sport Climbing [1.3812010983144802]
  We introduce a dataset of 22 annotated climbing videos, providing ground-truth labels for hold locations, usage order, and time of use.<n>We also explore the application of keypoint-based 2D pose-estimation models for detecting hold usage in sport climbing.
  arXiv  Detail & Related papers  (2025-05-19T08:41:18Z)
• Raising the Bar(ometer): Identifying a User's Stair and Lift Usage Through Wearable Sensor Data Analysis [4.453838343573515]
  This research describes a new exploratory dataset, to examine the patterns and behaviors related to using stairs and lifts. We collected data from 20 participants while climbing and descending stairs and taking a lift in a variety of scenarios. Our method is highly accurate at classifying stair and lift operations with an accuracy of 87.61% and a multi-class weighted F1-score of 87.56% over 8-second time windows.
  arXiv  Detail & Related papers  (2024-09-18T06:26:50Z)
• ASAG: Building Strong One-Decoder-Layer Sparse Detectors via Adaptive Sparse Anchor Generation [50.01244854344167]
  We bridge the performance gap between sparse and dense detectors by proposing Adaptive Sparse Anchor Generator (ASAG) ASAG predicts dynamic anchors on patches rather than grids in a sparse way so that it alleviates the feature conflict problem. Our method outperforms dense-d ones and achieves a better speed-accuracy trade-off.
  arXiv  Detail & Related papers  (2023-08-18T02:06:49Z)
• Energy-Based Models for Cross-Modal Localization using Convolutional Transformers [52.27061799824835]
  We present a novel framework for localizing a ground vehicle mounted with a range sensor against satellite imagery in the absence of GPS. We propose a method using convolutional transformers that performs accurate metric-level localization in a cross-modal manner. We train our model end-to-end and demonstrate our approach achieving higher accuracy than the state-of-the-art on KITTI, Pandaset, and a custom dataset.
  arXiv  Detail & Related papers  (2023-06-06T21:27:08Z)
• Lowering Detection in Sport Climbing Based on Orientation of the Sensor Enhanced Quickdraw [3.9504711818127425]
  A prototype is developed to collect data using accelerometer sensors attached to a piece of climbing equipment mounted on the wall. The corresponding sensors are configured to be energy-efficient, hence become practical in terms of expenses and time consumption for replacement. 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.
  arXiv  Detail & Related papers  (2023-01-17T15:06:33Z)
• Extrinsic Camera Calibration with Semantic Segmentation [60.330549990863624]
  We present an extrinsic camera calibration approach that automatizes the parameter estimation by utilizing semantic segmentation information. Our approach relies on a coarse initial measurement of the camera pose and builds on lidar sensors mounted on a vehicle. We evaluate our method on simulated and real-world data to demonstrate low error measurements in the calibration results.
  arXiv  Detail & Related papers  (2022-08-08T07:25:03Z)
• Learning Perceptual Locomotion on Uneven Terrains using Sparse Visual Observations [75.60524561611008]
  This work aims to exploit the use of sparse visual observations to achieve perceptual locomotion over a range of commonly seen bumps, ramps, and stairs in human-centred environments. We first formulate the selection of minimal visual input that can represent the uneven surfaces of interest, and propose a learning framework that integrates such exteroceptive and proprioceptive data. We validate the learned policy in tasks that require omnidirectional walking over flat ground and forward locomotion over terrains with obstacles, showing a high success rate.
  arXiv  Detail & Related papers  (2021-09-28T20:25:10Z)
• Benchmarking high-fidelity pedestrian tracking systems for research, real-time monitoring and crowd control [55.41644538483948]
  High-fidelity pedestrian tracking in real-life conditions has been an important tool in fundamental crowd dynamics research. As this technology advances, it is becoming increasingly useful also in society. To successfully employ pedestrian tracking techniques in research and technology, it is crucial to validate and benchmark them for accuracy. We present and discuss a benchmark suite, towards an open standard in the community, for privacy-respectful pedestrian tracking techniques.
  arXiv  Detail & Related papers  (2021-08-26T11:45:26Z)
• Embedding and generation of indoor climbing routes with variational autoencoder [0.0]
  We employ a variational autoencoder to climbing routes in a standardized training apparatus MoonBoard. 22 generated problems are uploaded to the Moonboard app for user review.
  arXiv  Detail & Related papers  (2020-09-16T23:23:04Z)
• CNN-based Speed Detection Algorithm for Walking and Running using Wrist-worn Wearable Sensors [1.6708670748115966]
  In this paper, we design, implement and evaluate a convolutional neural network based algorithm that leverages accelerometer and sensory data from the wrist-worn device to detect the speed with high precision. Our speed detection algorithm achieved $4.2%$ and $9.8%$ MAPE (Mean Absolute Error Percentage) value using $70-15-15$ train-test-evaluation split and leave-one-out cross-validation evaluation strategy respectively.
  arXiv  Detail & Related papers  (2020-06-03T15:53:46Z)
• Deep Soft Procrustes for Markerless Volumetric Sensor Alignment [81.13055566952221]
  In this work, we improve markerless data-driven correspondence estimation to achieve more robust multi-sensor spatial alignment. We incorporate geometric constraints in an end-to-end manner into a typical segmentation based model and bridge the intermediate dense classification task with the targeted pose estimation one. Our model is experimentally shown to achieve similar results with marker-based methods and outperform the markerless ones, while also being robust to the pose variations of the calibration structure.
  arXiv  Detail & Related papers  (2020-03-23T10:51:32Z)
• augKlimb: Interactive Data-Led Augmentation of Bouldering Training [0.0]
  Climbing is a popular sport, especially indoors, where climbers can train on man-made routes using artificial holds. Various aspects of adding computer-interaction to climbing have been studied in recent years. There is a large space for research into lightweight tools to aid recreational intermediate climbers.
  arXiv  Detail & Related papers  (2020-01-22T10:26:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.