Paper: Regression-based, mistake-driven movement skill estimation in Nordic Walking using wearable inertial sensors

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The paper has been accepted for inclusion in the Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom 2018) and will be presented in Athens during March 19-23, 2018.


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 configuration using leave-one-participant-out (LOPO) cross-validation. Results showed that all mistakes can be estimated with an 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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Friedrich-Alexander-Universität Erlangen-Nürnberg