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首页> 外文会议>Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Selection of Plantar-Pressure and Ankle-Acceleration Features for Freezing of Gait Detection in Parkinson's Disease using Minimum-Redundancy Maximum-Relevance
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Selection of Plantar-Pressure and Ankle-Acceleration Features for Freezing of Gait Detection in Parkinson's Disease using Minimum-Redundancy Maximum-Relevance

机译：使用最小冗余最大相关性的帕金森氏病步态冻结的足底压力和踝关节加速特征选择

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Freezing of gait (FOG) is a major hindrance to daily mobility and can lead to falling in people with Parkinson's disease. While wearable accelerometers and gyroscopes have been commonly used for FOG detection, foot plantar pressure distribution could also be considered for this application, given its usefulness in previous gait-based classification. This research examined 325 plantar-pressure based features and 132 acceleration-based features extracted from the walking data of five males with Parkinson's disease who experienced FOG. A set of 61 features calculated from the time domain, Fast Fourier transform (FFT), and wavelet transform (WT) were extracted from multiple input signals; including, total ground reaction force, foot centre of pressure (COP) position, COP velocity, COP acceleration, and 3D ankle acceleration. Minimum-redundancy maximum relevance (mRMR) feature selection was used to rank all features. Plantar-pressure based features accounted for 4 of the top 5 features (ranks 2, 3, 4, 5); the remaining feature was an ankle acceleration based feature (rank 1). The three highest ranked features were the freeze index (calculated from ankle acceleration), total power in the frequency domain (calculated using the FFT from COP velocity), and mean of the WT detail coefficients (calculated from COP velocity). This preliminary analysis demonstrated that features calculated from plantar pressure, specifically COP velocity, performed comparably to ankle acceleration features. Thus, feature sets for FOG detection may benefit from plantar-pressure based features.
机译：步态冻结（FOG）是日常活动的主要障碍，可能导致帕金森氏症患者跌倒。尽管可穿戴式加速度计和陀螺仪已普遍用于FOG检测，但鉴于此方法在以前基于步态的分类中很有用，因此也可考虑将脚底压力分布用于此应用。这项研究检查了从5名患有FOG的帕金森氏病男性的步行数据中提取的325个基于足底压力的特征和132个基于加速度的特征。从多个输入信号中提取了一组从时域，快速傅里叶变换（FFT）和小波变换（WT）计算出的61个特征;包括总地面反作用力，脚的压力中心（COP）位置，COP速度，COP加速度和3D脚踝加速度。最小冗余最大相关性（mRMR）功能选择用于对所有功能进行排名。基于足底压力的特征在前5个特征中占4个（等级2、3、4、5）;其余功能是基于脚踝加速度的功能（等级1）。排名最高的三个特征是冻结指数（根据脚踝加速度计算），频域中的总功率（根据COP速度使用FFT计算）和WT细节系数的平均值（根据COP速度计算）。初步分析表明，从足底压力（特别是COP速度）计算出的功能与踝部加速功能相当。因此，用于FOG检测的特征集可受益于基于足底压力的特征。

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