Thorsten Dahmen, Stephan Mantler · 2016. 3. 30. · Thorsten Dahmen, Stephan Mantler 1 Project...

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