Ataxia is a kind of external characteristic that appears on human body harmoniously undesirable and balance obstacle, it often indicates that some parts of the body have effected from diseased. There are many internal factors leading to ataxia. Currently, doctors often observe the external characteristics of patients with naked eyes and judge the cause of ataxia by combining their own experience, as a result, sensory ataxia is often misdiagnosed as cerebellar ataxia, which makes patients unable to get accurate and effective treatment and cause of delayed recovery. However, using modern high-precision medical instruments for detection is mostly expensive, which increases the economic burden on patients. In this paper, wireless sensing technology is used to detect and distinguish sensory ataxia and cerebellar ataxia. Firstly, the data of patients in Romberg's test and gait detection are collected by a wireless sensing system composed of omnidirectional antennas, then the data is preprocessed, finally, three machine learning algorithms are used to train the model. The experimental results show that the prediction accuracy of most algorithms can reach above 96%, which proves that the technical scheme described in this paper is feasible and effective.

中文翻译：

---

感觉性共济失调和小脑性共济失调的临床认识

共济失调是一种在人体上和谐地出现的有害的，平衡的障碍物的外部特征，它通常表明人体的某些部位已患病。有许多内部因素导致共济失调。目前，医生经常观察裸眼患者的外在特征，并结合自身经验判断共济失调的原因，因此，感觉性共济失调常被误诊为小脑性共济失调，使患者无法得到准确有效的治疗并引起恢复延迟。然而，使用现代的高精度医疗仪器进行检测通常是昂贵的，这增加了患者的经济负担。本文采用无线传感技术来检测和区分感觉性共济失调和小脑性共济失调。首先，通过由全向天线组成的无线传感系统收集Romberg测试和步态检测中的患者数据，然后对数据进行预处理，最后，使用三种机器学习算法训练模型。实验结果表明，大多数算法的预测精度可以达到96％以上，证明了本文所描述的技术方案是可行和有效的。