Automatic diagnosing of Cerebral Palsy (CP) gait is crucial in quantitative evaluation of a therapeutic intervention. Existing systems for such gait assessment are expensive and require user intervention. This study proposes a low-cost gait assessment system equipped with multiple Kinect sensors. Forty subjects (20 CP patients and 20 normal) were recruited for the experiment. To remove outlier frames from the combined gait signal of multiple sensors a data driven algorithm was proposed. Different supervised classifiers along with extreme learning machine were investigated to diagnose CP gait. In addition, a feature level analysis was also performed. Several spatio-temporal features (i.e. step length, stride length, stride time, etc.) were extracted. The strength of walking ratio, a speed invariant feature, to detect CP gait was thoroughly analyzed. The proposed system outperformed state-of-the-art with ≈98% of accuracy (sensitivity: 100%, and specificity: 96.87%). Results indicate a substantial improvement in abnormality detection performance after outlier removal. Based on ReliefF feature ranking algorithm, walking ratio ranked the best among other classical gait features. Performance of all classifiers increased substantially using walking ratio as a feature. Extreme learning machine demonstrated a competing performance in all cases. The higher classification accuracy of this low-cost system using only a single feature makes it attractive for CP gait detection.

中文翻译：

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基于计算智能技术的脑瘫步态自动诊断：一种低成本的多传感器方法

脑瘫（CP）步态的自动诊断对于定量评估治疗干预至关重要。用于这种步态评估的现有系统是昂贵的并且需要用户干预。这项研究提出了一种配备多个Kinect传感器的低成本步态评估系统。该实验招募了40名受试者（20名CP患者和20名正常）。为了从多个传感器的组合步态信号中去除异常帧，提出了一种数据驱动算法。研究了不同的监督分类器以及极限学习机，以诊断CP步态。另外，还进行了特征级分析。提取了几个时空特征（即步长，步幅长度，步幅时间等）。步行率的强度，速度不变的特征，以检测CP步态进行了彻底的分析。拟议的系统以约98％的准确度（灵敏度：100％，特异性：96.87％）优于最新技术。结果表明，异常值移除后异常检测性能有了实质性的提高。基于ReliefF特征排名算法，步行率在其他经典步态特征中排名最高。使用步行比率作为功能，所有分类器的性能都得到了显着提高。极限学习机在所有情况下都表现出竞争优势。这种仅使用单个功能的低成本系统的更高分类精度，使其对CP步态检测具有吸引力。基于ReliefF特征排名算法，步行率在其他经典步态特征中排名最高。使用步行比率作为功能，所有分类器的性能都得到了显着提高。极限学习机在所有情况下都表现出竞争优势。这种仅使用单个功能的低成本系统的更高分类精度，使其对CP步态检测具有吸引力。基于ReliefF特征排名算法，步行率在其他经典步态特征中排名最高。使用步行比率作为功能，所有分类器的性能都得到了显着提高。极限学习机在所有情况下都表现出竞争优势。这种仅使用单个功能的低成本系统的更高分类精度，使其对CP步态检测具有吸引力。