Detecting Sleep Deprivation from Running Biomechanics Using Machine Learning Classification: A Comparison Between Wearable and Laboratory Motion Capture

Authors

Seynaeve, M., Hendrickx, K., Vanwanseele, B., de Beukelaar, T.

Abstract

Sleep deprivation is associated with impaired endurance performance and an increased risk of running-related injury. Previous research has identified alterations in running biomechanics following a single night of sleep deprivation under laboratory conditions. However, whether these biomechanical changes can be detected using wearable technology remains unknown. Twenty-one recreationally active runners completed submaximal treadmill running under both normal sleep and total sleep deprivation conditions in a randomized crossover design. Biomechanical features were extracted simultaneously using a full-body motion capture system and a trunk-mounted wearable sensor. Five machine learning classifiers were evaluated in two classification tasks: a within-subject task using paired recordings from the same individual, and a between-subject task performed without individual baseline data. Within-subject classification consistently exceeded chance level for both measurement systems, with best accuracies of 85% for the wearable sensor (Logistic Regression) and 83% for the motion capture system (Random Forest). These findings indicate that sleep deprivation produces a systematic and individually consistent biomechanical signature during running. In contrast, between-subject classification failed across nearly all models and systems, with accuracies remaining close to chance level (~50%), demonstrating that inter-individual variability obscures the sleep-deprivation signal in the absence of personalized baseline data. Both systems converged on temporal organization, loading-related variables, and stride-to-stride variability as the most discriminative feature domains. Contrary to expectations, the laboratory motion capture system did not outperform the wearable sensor. Together, these findings demonstrate that individualized, baseline-referenced monitoring is essential for detecting sleep-deprivation-related changes in running gait, and suggest that a single trunk-mounted wearable sensor may provide a practical solution for real-world monitoring when paired recordings are available.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 16 Jul 2026.

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