Arias Valdivia Javiera T, Gatica Rojas Valeska, Astudillo César A
Doctorado en Sistemas de Ingeniería, Faculty of Engineering, Universidad de Talca, Curicó, 3340000, Chile.
Faculty of Health Sciences, University of Talca, Talca, 3460000, Chile.
Sci Rep. 2025 Mar 14;15(1):8811. doi: 10.1038/s41598-025-93166-3.
Cerebral palsy (CP) is a neurological condition that affects mobility and motor control, presenting significant challenges for accurate diagnosis, particularly in cases of hemiplegia and diplegia. This study proposes a method of classification utilizing Recurrent Neural Networks (RNNs) to analyze time series force data obtained via an AMTI platform. The proposed research focuses on optimizing these models through advanced techniques such as automatic parameter optimization and data augmentation, improving the accuracy and reliability in classifying these conditions. The results demonstrate the effectiveness of the proposed models in capturing complex temporal dynamics, with the Bidirectional Gated Recurrent Unit (BiGRU) and Long Short-Term Memory (LSTM) model achieving the highest performance, reaching an accuracy of 76.43%. These results outperform traditional approaches and offer a valuable tool for implementation in clinical settings. Moreover, significant differences in postural stability were observed among patients under different visual conditions, underscoring the importance of tailoring therapeutic interventions to each patient's specific needs.
脑瘫（CP）是一种影响活动能力和运动控制的神经疾病，对准确诊断提出了重大挑战，尤其是在偏瘫和双瘫病例中。本研究提出了一种利用循环神经网络（RNN）对通过AMTI平台获得的时间序列力数据进行分类的方法。拟议的研究侧重于通过自动参数优化和数据增强等先进技术对这些模型进行优化，提高对这些病症分类的准确性和可靠性。结果表明，所提出的模型在捕捉复杂的时间动态方面是有效的，双向门控循环单元（BiGRU）和长短期记忆（LSTM）模型表现最佳，准确率达到76.43%。这些结果优于传统方法，并为临床应用提供了一个有价值的工具。此外，在不同视觉条件下的患者中观察到姿势稳定性存在显著差异，这突出了根据每个患者的特定需求定制治疗干预措施的重要性。
