Gait disturbance is one of the most pronounced and disabling symptoms of cerebellar disease (CD). Generally, gait studies quantify human gait characteristics under natural walking speeds while mainly considering upper body movements. Therefore, the primary goal of this study was to investigate the influence of different walking speeds on different gait parameters of both the upper and lower body, as a result of disabilities caused by Cerebellar Ataxia (CA). We employed wearable sensor technology to identify the kinematic characteristics which best identify the gait abnormalities seen in CA. Measurements were made at self-selected slow, preferred and fast walking speeds. Velocity irregularity and resonant frequency characteristics were identified as key features of truncal and lower limb movements respectively. Subsequently, the differentiating features for both trunk and lower limb movements were combined to produce an even greater separation between the patients and the normal subjects, as well as better correlation with the expert clinical assessment (ECA) (0.86) and the Scale for the Assessment and Rating of Ataxia (SARA) (0.62). The different speed of walking conditions resulted in varying degrees of the separation and the correlation. Moreover, the contribution of the extracted features was examined using the random forest algorithm. Clinically observable truncal medio-lateral movements express the disability at relatively slow gait speeds while the anterior-posterior movements captured by the sensory mechanisms characterises the disability across all walking speeds. The importance of selected dominant features from the trunk and lower limb suggest that overall clinical assessments are predominantly influenced by the lower body peripheral movements, particularly at higher cadences.

中文翻译：

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使用多个可穿戴传感器通过躯干和周边测量量化步态共济失调的随机森林方法

步态障碍是小脑疾病 (CD) 最显着和致残的症状之一。通常，步态研究在主要考虑上半身运动的同时量化自然步行速度下的人类步态特征。因此，本研究的主要目标是调查不同步行速度对上半身和下半身不同步态参数的影响，这是由小脑共济失调 (CA) 引起的残疾的结果。我们采用可穿戴传感器技术来识别最能识别 CA 中看到的步态异常的运动学特征。测量是在自行选择的慢速、首选和快速步行速度下进行的。速度不规则和共振频率特性分别被确定为躯干和下肢运动的关键特征。随后，躯干和下肢运动的差异特征相结合，使患者和正常受试者之间产生更大的分离，以及与专家临床评估 (ECA) (0.86) 和评估和评级量表的更好相关性共济失调 (SARA) (0.62)。不同速度的步行条件导致不同程度的分离和相关性。此外，使用随机森林算法检查提取特征的贡献。临床上可观察到的躯干中外侧运动以相对较慢的步态速度表达残疾，而感觉机制捕获的前后运动表征了所有步行速度的残疾。