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首页> 外文期刊>IEEE sensors journal >A Random Forest Approach for Quantifying Gait Ataxia With Truncal and Peripheral Measurements Using Multiple Wearable Sensors
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A Random Forest Approach for Quantifying Gait Ataxia With Truncal and Peripheral Measurements Using Multiple Wearable Sensors

机译：一种随机森林方法，用于使用多个可穿戴传感器的特断和外围测量量化步态和外围测量

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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 capturedby 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.
机译：步态障碍是小脑疾病（CD）最明显和致残的症状之一。通常，步态研究在自然行走速度下量化人的步态特征，同时主要考虑上半身的运动。因此，本研究的主要目的是探讨不同步行速度对上半身不同步态参数的影响，因为小脑共济失调（CA）引起的残疾。我们采用可穿戴传感器技术来识别最佳识别CA中看到的步态异常的运动特征。测量以自选的缓慢，优选和快速的行走速度进行。速度不规则和谐振频率特性分别被识别为Truncal和下肢运动的关键特征。随后，组合了躯干和下肢运动的差异特征，以产生患者和正常对象之间的更大分离，以及与专家临床评估（ECA）（0.86）的相关性更好地相关，评估规模和共济失调（SARA）的评级（0.62）。不同的行走条件速度导致不同程度的分离和相关性。此外，使用随机林算法检查提取的特征的贡献。临床观察到的间断MEDIO-横向运动表达了相对缓慢的步态速度的残疾，而感觉机制捕获的前后运动表征跨所有步行速度的残疾。所选主导特征来自躯干和下肢的重要性表明，整体临床评估主要受到下半身外周运动的影响，特别是在更高的节奏。

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