Thorsten Dahmen, Stephan Mantler · 2016. 3. 30. · Thorsten Dahmen, Stephan Mantler 1 Project...
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Explorative Analysis and Visualization of Large Information Spaces Realistic Simulation and Optimization of Race Bike Training Thorsten Dahmen, Stephan Mantler 1 Project Goals (both lab and field) a)System for data acquisition, analysis, visualization and evaluation of performance parameters with cycling b)Design and validation of a mathematical model for these parameters c) Real-time and post-training analysis and visualization for bio-feedback training d) Learning of tactic approaches for a given training course profile 2 Current Progress on the Bicycle Simulator 2.1 Setup Cadence Measurement Exchangable Frame Pulse Measurement SRM Interface Simulated Gear Display Brake Unit Control Unit Elastic Suspension - Based on Cyclus 2 ergometer and our own PC-based software - Realistic pedal resistance according to GPS-based height profiles -Synchronized Video display of cycling track - Simulation of arbitrary gearshifts - Acquisition of power, cadence, heart rate - Visualization of course overviews and physiological parameters 2.2 Experiments a) Own simulator and outdoor rides, 20-25 participants - How does performance increase on specific course? - Is the simulation realistic and does it improve outdoor performance? -Which are the most effective indoor parameter displays? b) Bicycle Laboratory Freiburg - Can one conclude the pedal forces from the pedal motion? 3 Data exploration -Personal data (age, weight, size, questionnaire) -GPS, heart rate, cadence, gear, pedal force (currently 1 Hz sampling) - Motion capturing (upto 1000 Hz, 3 mm) - Lactate concentration, oxygen consumption -3D acceleration measurement to track upper body movement (approx. 50 Hz sampling) - Saddle pressure measurements - High-resolution elevation data of the Bodensee and Thurgau regions 4 Visualization Tasks existing novel on-exercise - Virtual 3D landscape or syn- chronized video playback - Superposition of values and graphics also of previous per- formances - Biofeedback information - Evaluation of efficiency of dis- plays post-exercise -Graphing values over time or distance - Few support course display as map overlay - Integration of geospatial infor- mation - Interactive visual exploration of performance data of several athletes and several training session in geospatial context - Powerwall 5 Modeling and Simulation of Adaptation Processes 5.1 Established Performance Models degra- dation recovery return to normal strain exceeded capacity Load Rate Strain Potential Response Potential Performance Potential hypertrophy limit collapse overflow + - - atrophy - Supercompensation Performance Potential 5.2 Discussion of models Feature \ Model Fit-Fat Biosynth. PerPot simulation of supercompensation ✔ ✔ ✔ stable hypertrophy ✖ ✔ ✔ overload collapse ✖ ✔ ✔ atrophy ✖ ✔ ✔ parameter access ✔ ✖ ✔ performance prediction short term ✔ ✖ ✔ long term ✖ ✖ ✖ specification for disciplines iron level, fatigue, running, tapering ✖ heart rate, hemoglobin, running, rowing, geospatial data ✖ ✖ for marathon 5.3 Ideas for advanced model a)System of differential equation (similar to PerPot but accounting for multidimensional data and including geospatial information) b) State space model (discrete hidden variables: HMM, continuous Gaussian hidden variables: Linear Dynamical System) c) Conditional Random Fields References [1]D. Saupe et al. Analysis and visualization of space-time variant parameters in endurance sports training. In Proceedings of the 6th International Symposium on Computer Science in Sports (IACSS), 2007. DFG Colloquium Thorsten Dahmen — PhD Track — Membership since 15.10.2007 Konstanz Work Group — Multimedia Signal Processing 26 June, 2008 Research Training Group 1042 (GK) — Explorative Analysis and Visualization of Large Information Spaces
Transcript of Thorsten Dahmen, Stephan Mantler · 2016. 3. 30. · Thorsten Dahmen, Stephan Mantler 1 Project...
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Explorative Analysisand Visualization of
Large Information Spaces
Realistic Simulation and Optimizationof Race Bike Training
Thorsten Dahmen, Stephan Mantler
1 Project Goals (both lab and field)
a) System for data acquisition, analysis, visualization and evaluation ofperformance parameters with cycling
b) Design and validation of a mathematical model for these parametersc) Real-time and post-training analysis and visualization for
bio-feedback trainingd) Learning of tactic approaches for a given training course profile
2 Current Progress on the Bicycle Simulator
2.1 Setup
CadenceMeasurement
ExchangableFrame
PulseMeasurement
SRM
Interface
SimulatedGear
Display
Brake Unit
ControlUnit
ElasticSuspension
- Based on Cyclus 2 ergometer and our own PC-based software- Realistic pedal resistance according to GPS-based height profiles- Synchronized Video display of cycling track- Simulation of arbitrary gearshifts- Acquisition of power, cadence, heart rate- Visualization of course overviews and physiological parameters
2.2 Experiments
a) Own simulator and outdoor rides, 20-25 participants- How does performance increase on specific course?- Is the simulation realistic and does it improve outdoorperformance?
- Which are the most effective indoor parameter displays?b) Bicycle Laboratory Freiburg
- Can one conclude the pedal forces from the pedal motion?
3 Data exploration
- Personal data (age, weight, size, questionnaire)- GPS, heart rate, cadence, gear, pedal force (currently 1 Hzsampling)
- Motion capturing (upto 1000 Hz, 3 mm)- Lactate concentration, oxygen consumption- 3D acceleration measurement to track upper body movement(approx. 50 Hz sampling)
- Saddle pressure measurements- High-resolution elevation data of the Bodensee and Thurgau regions
4 Visualization Tasks
existing novel
on-e
xerc
ise - Virtual 3D landscape or syn-
chronized video playback- Superposition of values andgraphics also of previous per-formances
- Biofeedback information- Evaluation of efficiency of dis-plays
post
-exe
rcis
e - Graphing values over time ordistance
- Few support course displayas map overlay
- Integration of geospatial infor-mation
- Interactive visual exploration ofperformance data of severalathletes and several trainingsession in geospatial context
- Powerwall
5 Modeling and Simulation of Adaptation Processes
5.1 Established Performance Models
degra-dation
recoveryreturn to normal
strain exceededcapacity
Load Rate
StrainPotential
ResponsePotential
Performance Potential
hypertrophylimit
collapseoverflow +-
-atrophy
-
Supercompensation Performance Potential
5.2 Discussion of models
Feature \ Model Fit-Fat Biosynth. PerPotsimulation of
supercompensation 4 4 4stable hypertrophy 6 4 4overload collapse 6 4 4atrophy 6 4 4
parameter access 4 6 4performance prediction
short term 4 6 4long term 6 6 6
specificationfor disciplines
iron level,fatigue,running,tapering
6
heart rate,hemoglobin,running,rowing,
geospatial data 6 6 for marathon
5.3 Ideas for advanced model
a) System of differential equation (similar to PerPot but accounting formultidimensional data and including geospatial information)
b) State space model (discrete hidden variables: HMM, continuousGaussian hidden variables: Linear Dynamical System)
c) Conditional Random Fields
References
[1] D. Saupe et al. Analysis and visualization of space-time variantparameters in endurance sports training. In Proceedings of the 6thInternational Symposium on Computer Science in Sports (IACSS),2007.
DFG Colloquium Thorsten Dahmen — PhD Track — Membership since 15.10.2007Konstanz Work Group — Multimedia Signal Processing
26 June, 2008 Research Training Group 1042 (GK) — Explorative Analysis and Visualization of Large Information Spaces
