Design of a sensor network for the quantitative analysis of sport climbing
Bibliographic Data
| ID | 5285329 |
|---|---|
| Authors | Alessandro Colombo (0009-0009-0244-7854), Ramon Maj, Marita Canina (0000-0002-0036-9428), Francesca Fedeli, Nicolò Dozio (0000-0003-3201-2519), Francesco Ferrise (0000-0001-8951-8807) |
| Year | 2023 |
| Volume | 5 |
| Pages | 1114539-1114539 |
| Publication date | 2023-02-20 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Sports and Active Living (JOURNAL) |
| Journal identifiers | ISSN: 2624-9367 • E-ISSN: 2624-9367 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fspor.2023.1114539 |
| PMID | 36891129 |
| OpenAlex | W4321376449 |
| Language | EN |
| Citations received | 2 |
| References cited | 32 |
We describe the design of a modular sensorized climbing wall for motion analysis in a naturalistic environment. The wall is equipped with force sensors to measure interaction forces between the athlete and the wall, which can be used by experienced instructors, athletes, or therapists, to gain insights into the quality of motion. A specifically designed triaxial load cell is integrated into each hold placement, invisible to the climber, and compatible with standard climbing holds. Data collected through the sensors is sent to an app running on a portable device. The wall can be adapted to different uses. To validate our design, we recorded a repeated climbing activity of eleven climbers with varying degrees of expertise. Analysis of the interaction forces during the exercise demonstrates that the sensor network design can provide valuable information to track and analyze exercise performance changes over time. Here we report the design process as well as the validation and testing of the sensorized climbing wall
Climbing · Modular design · Motion (physics · Physical medicine and rehabilitation · Process (computing · Simulation · Sports biomechanics · Stair climbing · Structural engineering · Advanced Sensor and Energy Harvesting Materials · Computer Science · Engineering · Muscle activation and electromyography studies · Sports Performance and Training · Artificial Intelligence
| Unique citing works | 2 |
|---|---|
| Citations per year | 2 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |