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This research was funded by Escuela Universitaria de Fisioterapia ONCE-UAM and TACTIC project-FORTALECE ISCIII grant no. FORT23/00034, Grant CNS2023-144775 funded by MICIU/AEI/10.13039/501100011033 by "European Union NextGenerationEU/PRTR". This project has also received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant Agreement No. 851255). This work has been partially supported by the Maria de Maeztu project CEX2021-001164-M funded by the MCIN/AEI/10.13039/501100011033 and FEDER, EU. This research has been supported by a grant for an international stay from the Universidad Autonoma de Madrid travel grants Program for participation in International Research Conferences.

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Deep Learning Unravels Differences Between Kinematic and Kinetic Gait Cycle Time Series from Two Control Samples of Healthy Children Assessed in Two Different Gait Laboratories

Publicado en:SENSORS. 25 (1): 110- - 2025-01-01 25(1), DOI: 10.3390/s25010110

Autores: de Gorostegui, Alfonso; Kiernan, Damien; Martin-Gonzalo, Juan-Andres; Lopez-Lopez, Javier; Pulido-Valdeolivas, Irene; Rausell, Estrella; Zanin, Massimiliano; Gomez-Andres, David

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We investigate the application of deep learning in comparing gait cycle time series from two groups of healthy children, each assessed in different gait laboratories. Both laboratories used similar gait analysis protocols with minimal differences in data collection. Utilizing a ResNet-based deep learning model, we successfully identified the source laboratory of each dataset, achieving a high classification accuracy across multiple gait parameters. To address the inter-laboratory differences, we explored various pre-processing methods and time series properties that may have been detected by the algorithm. We found that the standardization of the time series values was a successful approach to decrease the ability of the model to distinguish between the two centers. Our findings also reveal that differences in the power spectra and autocorrelation structures of the datasets play a significant role in the model performance. Our study emphasizes the importance of standardized protocols and robust data pre-processing to enhance the transferability of machine learning models across clinical settings, particularly for deep learning approaches.

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