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首页> 外文会议>International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques >A Deep Gait Classification Approach for an Early Recognition of Huntington Diseases
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A Deep Gait Classification Approach for an Early Recognition of Huntington Diseases

机译：一种深入的步态分类方法，即早期识别亨廷顿疾病

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摘要

Huntington disease (HD) is a progressive disorder of motor, cognitive, and psychiatric disturbances. A general lack of coordination and an unsteady gait often follow motor speed, fine motor control, and gait are affected. Gait disturbance is one of the main factors contributing to a negative impact on quality of life of patients. The state-of-the-art of assessment approaches for the evaluation and recognition of this type of disease are expensive ambulation-based performed under the supervision of clinicians. Our research aim at overcoming these issues by defining an in-house self-test mobile solution able to detect anomalies in the gait dynamics of elderly. In this paper, we present the preliminary results of our research exploring a deep learning-based model for the automatic assessment of the gaits dynamics of elderly people. The gait dynamics signal is measured by means of a temporal time series of the acceleration values of the patient's acceleration movements along the (x,y,z) axes. Our experiments show classification results reaching a good accuracy rate at 0.75% with a recall an precision rate at 0.70% and 0.75%.
机译：亨廷顿病（HD）是一种渐进式电机，认知和精神障碍。一般缺乏协调和不稳定的步态通常遵循电机速度，精细电机控制和步态受到影响。步态障碍是对患者生活质量产生负面影响的主要因素之一。评估和识别这种类型疾病的最先进的评估方法是昂贵的临床医生监督下的昂贵的行动。我们的研究旨在通过定义内部自检移动解决方案来克服这些问题，能够在老年人的步态动态中检测到异常。在本文中，我们展示了我们研究的初步结果，探索了基于深度学习的模型，用于自动评估老年人的Gaits Dynamics。步态动力学信号通过沿（x，y，z）轴的患者的加速运动的加速度值的时间序列序列来测量。我们的实验显示分类结果达到0.75％的良好精度率，召回精度率为0.70％和0.75％。

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