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Classification of Changes in Speed and
Inclination during Running

                     Bjoern Eskofier, Dr. Benno Nigg
                     Human Performances Lab (HPL)
                     University of Calgary, Canada

                     Martin Wagner
                     Chair of Pattern Recognition
                     University of Erlangen, Germany

                     Mark Oleson, Ian Munson
                     adidas innovation team, adidas AG




A Digital Sports Embedded Classification Task
September 24, 2009
Digital Revolution – Even In Sports


                     The adidas_1: “the world‘s
                     first intelligent shoe”

                     Direct processing of sport-
                     specific information

                     Microprocessor adapts the
                     shoe to the run situation

                     Intelligence modeled by
                     Pattern Recognition

B. Eskofier          Running Speed and Inclination Classification
September 24, 2009                         IACSS09, Canberra, Australia
Overview of the Talk

Introduction

Data collection

Methods

Results

Discussion

Acknowledgments
 B. Eskofier          Running Speed and Inclination Classification
 September 24, 2009                         IACSS09, Canberra, Australia
Introduction

Runners need different cushioning

Inclination, speed, …: changing demands

adidas_1 developed for that

Ideal cushioning: pattern recognition

Embedded task: cheap computations

 B. Eskofier          Running Speed and Inclination Classification
 September 24, 2009                         IACSS09, Canberra, Australia
Introduction – adidas_1

3 adidas_1 main parts
      Cushioning element (01)
      with compression
      measurement
      Microcontroller (02)
      Motor for cushioning
      adaptation (03)




  B. Eskofier               Running Speed and Inclination Classification
  September 24, 2009                              IACSS09, Canberra, Australia
Introduction – Pattern Recognition


               Sensors   Preprocessing     Features          Classification



        Online analysis

       Offline analysis
                                 Data Sample              Learning



Challenges:               - Hardware environment
                          - Real-time classification

 B. Eskofier                    Running Speed and Inclination Classification
 September 24, 2009                                   IACSS09, Canberra, Australia
Data Collection

84 runners (30 ♀, 54 ♂) - 56 usable
Measurement device 1: Polar RS 800
Measurement device 2: Cell phone




                                       Eskofier et el., 2008
 B. Eskofier          Running Speed and Inclination Classification
 September 24, 2009                         IACSS09, Canberra, Australia
Data Collection – adidas_1

Measurement device 3:
adidas_1

Magnet at bottom,
Hall sensor at top of
cushioning element

Measures compression
of the heel
  B. Eskofier          Running Speed and Inclination Classification
  September 24, 2009                         IACSS09, Canberra, Australia
Methods – Features
                                                Eskofier et al., 2009
Step detection:
linear filter

11 features

+ Mean {4,8,16}
+ SD {4,8,16}
+ Gradients {16}
  88 features

 B. Eskofier          Running Speed and Inclination Classification
 September 24, 2009                         IACSS09, Canberra, Australia
Methods – Labels

Definition of 3 inclination classes
                 α < -3°
         -3° ≤ α ≤ 3°
         3° < α
Definition of 3        speed classes [m/s]
         0   ≤          v < 2.5
         2.5 ≤          v < 3.6
         3.6 ≤          v
  B. Eskofier             Running Speed and Inclination Classification
  September 24, 2009                            IACSS09, Canberra, Australia
Methods – Classifiers

Bayes Classifier (BC)
Polynomial Classifier (PC)
Linear Discriminant Analysis (LDA)
Support Vector Machine (SVM)
Multilayer Perceptron (MLP)



 B. Eskofier          Running Speed and Inclination Classification
 September 24, 2009                         IACSS09, Canberra, Australia
Methods – Classification

5 fold Cross-Validation

105 random vectors from each class

Feature selection: dynamic programming
with Mahalanobis distance G
                                          T
                      Gk ,l = (µk − µ l ) Σ −1 (µk − µ l )



 B. Eskofier                         Running Speed and Inclination Classification
 September 24, 2009                                        IACSS09, Canberra, Australia
Results – Inclination Classification




B. Eskofier          Running Speed and Inclination Classification
September 24, 2009                         IACSS09, Canberra, Australia
Results – Speed Classification




B. Eskofier          Running Speed and Inclination Classification
September 24, 2009                         IACSS09, Canberra, Australia
Discussion

Inclination classifier not implemented

Speed classifier implemented
     accuracy rise for 2 features (all methods)
     further accuracy rise: 4 features (BC, SVM, MLP)
     two feature approach (µ16(F1), µ16(F3))
     SVM as classifier


Microcontroller implementation successful

 B. Eskofier             Running Speed and Inclination Classification
 September 24, 2009                            IACSS09, Canberra, Australia
Acknowledgments

Running study participants

Colleagues and reviewers

Pascal Kuehner (University of Landau)

adidas innovation team ait.



 B. Eskofier          Running Speed and Inclination Classification
 September 24, 2009                         IACSS09, Canberra, Australia
B. Eskofier          Running Speed and Inclination Classification
September 24, 2009                         IACSS09, Canberra, Australia

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Classification of Changes in Speed

  • 1. Classification of Changes in Speed and Inclination during Running Bjoern Eskofier, Dr. Benno Nigg Human Performances Lab (HPL) University of Calgary, Canada Martin Wagner Chair of Pattern Recognition University of Erlangen, Germany Mark Oleson, Ian Munson adidas innovation team, adidas AG A Digital Sports Embedded Classification Task September 24, 2009
  • 2. Digital Revolution – Even In Sports The adidas_1: “the world‘s first intelligent shoe” Direct processing of sport- specific information Microprocessor adapts the shoe to the run situation Intelligence modeled by Pattern Recognition B. Eskofier Running Speed and Inclination Classification September 24, 2009 IACSS09, Canberra, Australia
  • 3. Overview of the Talk Introduction Data collection Methods Results Discussion Acknowledgments B. Eskofier Running Speed and Inclination Classification September 24, 2009 IACSS09, Canberra, Australia
  • 4. Introduction Runners need different cushioning Inclination, speed, …: changing demands adidas_1 developed for that Ideal cushioning: pattern recognition Embedded task: cheap computations B. Eskofier Running Speed and Inclination Classification September 24, 2009 IACSS09, Canberra, Australia
  • 5. Introduction – adidas_1 3 adidas_1 main parts Cushioning element (01) with compression measurement Microcontroller (02) Motor for cushioning adaptation (03) B. Eskofier Running Speed and Inclination Classification September 24, 2009 IACSS09, Canberra, Australia
  • 6. Introduction – Pattern Recognition Sensors Preprocessing Features Classification Online analysis Offline analysis Data Sample Learning Challenges: - Hardware environment - Real-time classification B. Eskofier Running Speed and Inclination Classification September 24, 2009 IACSS09, Canberra, Australia
  • 7. Data Collection 84 runners (30 ♀, 54 ♂) - 56 usable Measurement device 1: Polar RS 800 Measurement device 2: Cell phone Eskofier et el., 2008 B. Eskofier Running Speed and Inclination Classification September 24, 2009 IACSS09, Canberra, Australia
  • 8. Data Collection – adidas_1 Measurement device 3: adidas_1 Magnet at bottom, Hall sensor at top of cushioning element Measures compression of the heel B. Eskofier Running Speed and Inclination Classification September 24, 2009 IACSS09, Canberra, Australia
  • 9. Methods – Features Eskofier et al., 2009 Step detection: linear filter 11 features + Mean {4,8,16} + SD {4,8,16} + Gradients {16} 88 features B. Eskofier Running Speed and Inclination Classification September 24, 2009 IACSS09, Canberra, Australia
  • 10. Methods – Labels Definition of 3 inclination classes α < -3° -3° ≤ α ≤ 3° 3° < α Definition of 3 speed classes [m/s] 0 ≤ v < 2.5 2.5 ≤ v < 3.6 3.6 ≤ v B. Eskofier Running Speed and Inclination Classification September 24, 2009 IACSS09, Canberra, Australia
  • 11. Methods – Classifiers Bayes Classifier (BC) Polynomial Classifier (PC) Linear Discriminant Analysis (LDA) Support Vector Machine (SVM) Multilayer Perceptron (MLP) B. Eskofier Running Speed and Inclination Classification September 24, 2009 IACSS09, Canberra, Australia
  • 12. Methods – Classification 5 fold Cross-Validation 105 random vectors from each class Feature selection: dynamic programming with Mahalanobis distance G T Gk ,l = (µk − µ l ) Σ −1 (µk − µ l ) B. Eskofier Running Speed and Inclination Classification September 24, 2009 IACSS09, Canberra, Australia
  • 13. Results – Inclination Classification B. Eskofier Running Speed and Inclination Classification September 24, 2009 IACSS09, Canberra, Australia
  • 14. Results – Speed Classification B. Eskofier Running Speed and Inclination Classification September 24, 2009 IACSS09, Canberra, Australia
  • 15. Discussion Inclination classifier not implemented Speed classifier implemented accuracy rise for 2 features (all methods) further accuracy rise: 4 features (BC, SVM, MLP) two feature approach (µ16(F1), µ16(F3)) SVM as classifier Microcontroller implementation successful B. Eskofier Running Speed and Inclination Classification September 24, 2009 IACSS09, Canberra, Australia
  • 16. Acknowledgments Running study participants Colleagues and reviewers Pascal Kuehner (University of Landau) adidas innovation team ait. B. Eskofier Running Speed and Inclination Classification September 24, 2009 IACSS09, Canberra, Australia
  • 17. B. Eskofier Running Speed and Inclination Classification September 24, 2009 IACSS09, Canberra, Australia