top: paper
url: https://pdfs.semanticscholar.org/e75b/cc88fba365798e31e0d607d173f71c3af7c4.pdf
note
- how they came up with fluency (effective of the movement) and agility
- that custom definition might be a bit outdated now considering that research got published in 200x
- what is the latest approach?
- biomechanic?
- (iROZHLAS, 2019) how they calculate and came up with that conclusion about the climber’s physique advantage
- PCA and bouldering
- K-shortest path and motion planning
- XR approaches
ideas
- Collaboration climbing ? (inspired by it takes two and teaching/training)
note (jot down some keywords)
intro
- climbing motion analysis
- to optimize climbing techniques
- therapeutic application: avoid movements that are prone to cause injuries
- sensors
- human motion capture
- analysis algorithm
- RGB-D camera
- section 2: sensor techs
- section 3: review motion capture approaches
- section 4: algorithms for climbing motion analysis
- section 5: the findings
sensors
- optical sensors
- camera technology with depth sensing
- RGB-D camera
- triangulation process — depth estimation
- Time of flight (ToF)
- force sensors
motion capture
- Human pose estimation (HPE)
- input: locations of defined points (body)
- coarse body description
- fine-granular models
- 2D vs 3D representation
- CoM
- desired output:
- entropy
- velocity
- acceleration
- translated to:
- fluency
- force of the motion (2007)
- how to calculate
- weighted average of nine body segments
- 3-D skeleton
- 3-D point clouds
Pose Estimation for Climbing Analysis
- mostly rely on fine granular skeleton model
- instrumented bouldering wall
- A very popular Czech competitive climber, Adam Ondra, ”hung with sensors” to analyse what makes him such an outstanding climber (iROZHLAS, 2019)
- marker-based motion capture system
- result: Next to his long neck, his comparatively slim shoulders result in less force on his fingers.
- parsing a climber’s body area
- foreground segmentation on depth image
- used to correct the feet and hand positions of the kinetic skeleton
- seems to related with these researches
- ML based pose estimation
- problem (2018)
- finding specific dataset for climbing pose
- more keywords
- high degree of freedom
- self-occlusion
Algorithms for motion analysis
- PCA 2007
- MoCap system with passive sensors (Sibella et al., 2007
- CoM -> entropy, velocity, acceleration
- fluency: effectiveness of the movement
- means of the entropy of the climbing route
- agility
- instrumented climbing wall
- 3-D camera
- position and climber’s force
- motion sensor -> hip
- dynos
- relation between vertical and absolute velocities of the hip
- motion planning
- prediction/plan climbing motion
- anatomically possible movements
- data driven analysis
- simulating climbing behavior
- let climbers design quality routes by placing holds in a virtual climbing wall
- offline route planning
- graph based application
- k-shortest path (2017)
- depends on the anatomic characteristic of the climber
- strength
- flexibility
- reach
- contemplated limbs hang free for balancing and the use of the wall friction
- simulated agent can move more than one limb at a time
- AR
- create climbing routes for moon board with their smartphone
- VR
- share the experiences professional climbers made on extreme routes
- Adidas, 2019
- Teaching and Training
- demonstration of postures and movements / imitate
- biomechanics point of view
- visualizing reference motion
- difficulty to teach and learn simultaneously
- augmented real-time video projected on the wall
summary and outlook
- at 2018, real-time marker-less, vision-based motion capture for climbing motions is far from being solved and requires further research activities.
created on: Tue Feb 17 2026