We propose a mistake-driven skill estimation approach for movement analysis in sports using wearable inertial measurement units (IMUs) and continuous regression models. As a motion-quality oriented sport, we focus on Nordic Walking. Nordic Walking involves complex body part interactions, which when wrongly performed, increase injury risk and reduce training effectiveness. We present an approach to assess three mistakes related to health risk which are typical in beginners. Based on a stride segmentation, features relevant to the mistakes were extracted and selected in a dedicated feature selection step. Subsequently, Bayesian Ridge Regression (BRR) and alternative regression models were trained for each mistake type. Models were evaluated in our pattern analysis architecture supporting parallel and continuous step-wise estimation of all mistakes in movement skill grades ranging from 1 (correct) to 3 (incorrect). We evaluated our skill estimation approach in a study including 10 Nordic Walking beginners and 11247 expert-annotated strides derived from 50 recording sessions. We investigate seven practical wearable sensor placement configurations using leave-one participant-out (LOPO) cross-validation. Results showed that all mistakes can be estimated with a normalised RMSE of 24.15 % across all participants. Additional analysis of trends suggest that participants could improve skilfulness in training sessions during our study.

1. Regression-based, mistake-driven movement
skill estimation in Nordic Walking using
wearable inertial sensors
Adrian Derungs1,2
| Sebastian Soller3
| Andreas Weishäupl3
|
Judith Bleuel3
| Gereon Berschin3
| Oliver Amft1,2
1
ACTLab research group | Chair of eHealth and mHealth | Friedrich-Alexander Universität Erlangen-Nürnberg
2
ACTLab research group | Chair of Sensor Technology | Universität Passau
3
Sports centre | Universität Passau
PerCom 2018

2. 2

3. 3
Challenges in NW
[Knobloch & Vogt, 2006]

4. 4
Nordic Walking techniques
[Kocur et al., 2009]

5. 5
Mistakes
MS1:
Matching gear
MS2:
Pole use
MS3:
Arm swing
Right pole + Left foot
Left pole + Right foot

6. 6
Mistakes
MS1 = Matching gear
MS2 = Pole use
MS3 = Arm swing
incorrect = 3
correct = 1
incorrect = 3
correct = 1
in-between = 2
incorrect = 3
correct = 1
in-between = 2

7. 7
Regression-based
skill estimation
Stride segmentation
using acceleration data
Feature extraction
and selection
Hill climb
S1 S2 S3 S4
t
S1S2S3S4
MS1
Regression
Skill[Grade]
1
2
3
MS2
Regression
1
2
3
MS3
Regression
1
2
3
Strides Strides Strides
Trend Trend Trend
MS1
Feature selection
Feature matrix
Features from IMU (acc, gyro, mag)
MS2
MS3

8. 8
Algorithms
Leave-one-participant-out cross-validation
Feature evaluation
PCA | Gradient descent
→ Decision fusion
(PCA and Gradient descent)
Skill grade estimation
Bayesian Ridge
Linear
Support Vector
AdaBoost
Stride

9. 9
Linear trend estimation
Trend
slope
estimated skill grade for each stride
Skill[Grade]
Strides
1
2
3
Trend
offset

10. 10
Evaluation study
10 participants
9 males 26.4 years
5 session → 15 min.
Total 50 sessions
11247 annotated strides

11. 11
Nordic Walking strip
T T
Side cam view
Frontcamview
Mat
EvaluationAc-/ De-celeration Ac-/ De-celeration

12. 12

13. 13
Sensor combinations
1 2 3 4
5 6 7

14. 14
Metrics
Pearson correlation coefficient r
ŝi
= estimated skill grade
si
= reference skill grade
smax
= max. skill grade → 3
smin
= min. skill grade → 1

15. 15
Results

16. 16
Results cont'd
5

17. 17
Results cont'd
Improvement
MS1 → 8 | MS2 → 9 | MS3 → 8
[Strides]

18. 18
Conclusion
Virtual coachImprove training

19. Adrian Derungs | Sebastian Soller
Andreas Weishäupl | Judith Bleuel
Gereon Berschin | Oliver Amft