Application of inertial measurement units and machine learning for fatigue assessment in badminton athletes

Offline binary fatigue vs non-fatigue classification from IMU acceleration features during badminton forehand smashes: 70 recordings from 25 athletes (22 non-fatigue, 48 fatigue; fatigue = CMJ drop >=20% and/or Borg CR-10 RPE >=5). Five classical classifiers under athlete-grouped nested CV: fold-mean AUC 0.655-0.846 with seven sensors and 0.715-0.846 with the right forearm alone; macro-F1 up to 0.775. Authors explicitly do not claim real-time monitoring or sensor equivalence.

• Form factor: multi-site body-worn IMU (7 sensors) incl. right forearm

Objective

To evaluate offline binary classification of recorded fatigue conditions using inertial measurement unit (IMU) acceleration features and five machine-learning algorithms.

Methods

The author-confirmed performance dataset contained 32 observations per condition. The revised primary IMU dataset comprised 70 recordings from 25 roster-linked athletes, with 22 non-fatigue and 48 fatigue labels after correction of four misclassified recordings. Seven-sensor and right-forearm configurations provided 231 and 33 candidate features. A newly reconstructed analysis retained five documented outer athlete-grouped folds and used three grouped inner folds for training-only preprocessing, sparse logistic feature selection and hyperparameter tuning.

Results

CMJ was 40.01 ± 8.51 and 30.27 ± 7.29 in the non-fatigue and fatigue conditions, respectively. Across classifiers, fold-mean AUC ranged from 0.655 to 0.846 for seven sensors and from 0.715 to 0.846 for the right forearm. Macro F1 ranged from 0.600 to 0.775 and from 0.655 to 0.775, respectively. Specificity ranged from 0.483 to 0.700 and from 0.550 to 0.650. Code, inclusion decisions, actual inner and outer assignments and held-out predictions are supplied for this new run.

Conclusion

The results support further investigation of IMU-based condition classification, but do not establish algorithm superiority, sensor equivalence, physiological validity of every recording label, or real-time monitoring performance.

Verbatim from the publisher's DOI record (opens in a new tab) · Publisher licence: https://creativecommons.org/licenses/by/4.0/

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