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LSTM for Diagnosis of Neurodegenerative Diseases Using Gait Data

机译：使用步态数据诊断神经退行性疾病的LSTM

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Neurodegenerative diseases (NDs) usually cause gait disorders and postural disorders, which provides an important basis for NDs diagnosis. By observing and analyzing these clinical manifestations, medical specialists finally give diagnostic results to the patient, which is inefficient and can be easily affected by doctors' subjectivity. In this paper, we propose a two-layer Long Short-Term Memory (LSTM) model to learn the gait patterns exhibited in the three NDs. The model was trained and tested using temporal data that was recorded by force-sensitive resistors including time series, such as stride interval and swing interval. Our proposed method outperforms other methods in literature in accordance with accuracy of the predicted diagnostic result. Our approach aims at providing the quantitative assessment so that to indicate the diagnosis and treatment of these neurodegenerative diseases in clinic.
机译：神经退行性疾病（NDs）通常会导致步态障碍和姿势障碍，这为NDs诊断提供了重要依据。通过观察和分析这些临床表现，医学专家最终为患者提供诊断结果，该诊断结果效率低下并且容易受到医生主观性的影响。在本文中，我们提出了一个两层长短期记忆（LSTM）模型来学习在三个ND中表现出的步态模式。使用时间数据对模型进行训练和测试，该时间数据由力敏感电阻器记录，包括时间序列，例如步幅和摆动间隔。根据预测的诊断结果的准确性，我们提出的方法优于文献中的其他方法。我们的方法旨在提供定量评估，以指示临床中这些神经退行性疾病的诊断和治疗。

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