Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3101-3104. doi: 10.1109/EMBC46164.2021.9631093.
Cerebellar ataxia (CA) is defined by disrupted coordination of movement suffering from disease of the cerebellum. It reflects fragmented movements of the eyes, vocal, upper limbs, balance, gait, and lower limbs. This study aims to use a motion sensor to form a simple yet effective CA quantitative assessment framework. We suggest a pendant device to use a single kinematic sensor attached to the wearer's chest to investigate the balance capability. Via a standard neurological test (Romberg's standing), the device may reveal an early symptom of Cerebellar Ataxia tailoring toward rehabilitation or therapeutic program. We adopt a transformed-image based approach to leverage the advantage of state-of-the-art deep learning models into diagnosis CA. Three transform techniques are employed including recurrence plot, melspectrogram, and Poincaré plot. Experiment results show that melspectrogram transform technique performs best in implementation with MobileNetV2 to diagnose CA with an average validation accuracy of 89.99%.
小脑性共济失调（CA）是指由于小脑疾病而导致运动协调受损。它反映了眼睛、声音、上肢、平衡、步态和下肢运动的不连贯。本研究旨在使用运动传感器构建一个简单而有效的 CA 定量评估框架。我们建议使用一个吊坠设备，该设备使用一个附在佩戴者胸部的运动传感器来研究平衡能力。通过标准的神经学测试（Romberg 站立测试），该设备可以揭示出小脑共济失调的早期症状，从而针对康复或治疗计划进行定制。我们采用基于变换图像的方法，利用最先进的深度学习模型的优势来诊断 CA。我们采用了三种变换技术，包括递归图、梅尔频谱图和庞加莱图。实验结果表明，在使用 MobileNetV2 实现时，梅尔频谱图变换技术的表现最佳，用于诊断 CA 的平均验证准确率为 89.99%。
