Nordic Walking
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.
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Nordic Walking
- 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
- 17. 17 Results cont'd Improvement MS1 → 8 | MS2 → 9 | MS3 → 8 [Strides]
- 19. Adrian Derungs | Sebastian Soller Andreas Weishäupl | Judith Bleuel Gereon Berschin | Oliver Amft