What are the main findings? a) A 1D-CNN accurately detected 100% of ground contacts during sprint acceleration. b) Ground contact times and event estimates were as accurate or better than previous methods. What are the implications of the main findings? a) Deep learning 1D-CNN provides superior accuracy and reliability for ground contact detection in sprint acceleration. b) Deep learning approaches should be implemented in real-time field systems to enhance practical performance analysis. Abstract: Background: Ground contact (GC) detection is essential for sprint performance analysis. Inertial measurement units (IMUs) enable field-based assessment, but their reliability during sprint acceleration remains limited when using heuristic and recently used machine learning algorithms. This study introduces a deep learning one-dimensional convolutional neural network (1D-CNN) to improve GC event and GC times detection in sprint acceleration. Methods: Twelve sprint-trained athletes performed 60 m sprints while bilateral shank-mounted IMUs (1125 Hz) and synchronized high-speed video (250 Hz) captured the first 15 m. Video-derived GC events served as reference labels for model training, validation, and testing, using resultant acceleration and angular velocity as model inputs. Results: The optimized model (18 inception blocks, window = 100, stride = 15) achieved mean Hausdorff distances = 6 ms and 100% precision and recall for both validation and test datasets (Rand Index = 0.977). Agreement with video references was excellent (bias < 1 ms, limits of agreement ± 15 ms, r > 0.90, p < 0.001). Conclusions: The 1D-CNN surpassed heuristic and prior machine learning approaches in the sprint acceleration phase, offering robust, near-perfect GC detection. These findings highlight the promise of deep learning-based time-series models for reliable, real-world biomechanical monitoring in sprint acceleration tasks.
© Copyright 2026 Sensors. Alle Rechte vorbehalten.