Smoothness Metrics in Complex Movement Tasks
Smoothness Metrics in Complex Movement Tasks
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DOI：
10.3389/fneur.2018.00615
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发表时间：
2018-09-12
影响因子：
3.4
通讯作者：
Hermsdoerfer, Joachim
中科院分区：
文献类型：
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作者：
Gulde, Philipp;Hermsdoerfer, Joachim
流畅是以目标为导向的人体运动的一个主要特征。量化运动平稳性的方法的适用性取决于分析的信号的结构。近年来，日常生活活动(ADL)在衰老和神经康复方面的研究引起了人们的浓厚兴趣。这类任务具有复杂的信号结构，需要调整运动学参数。在目前的研究中，我们考察了四种不同的方法来量化ADL中的运动平稳性。我们通过比较8名健康老年人(67.1a+/-7.1a)和8名健康年轻人(26.9a+/-2.1a)日常生活活动(泡茶)的运动信号，测试了这些方法的适当性，即每米速度峰值(NOP)、频谱弧长(SAL)、速度度量(SM)和对数无因次挺举(LDJ)。所有方法均能识别组内光滑度差异(Cohen‘s d NOP=2.53，SAL=1.95，SM=1.69，LDJ=4.19)，3种方法显示高到极高的敏感性(z-Score：NOP=1.96+/-0.55，SAL=1.60+/-0.64，SM=3.41+/-3.03，LDJ=5.28+/-1.52)，3种方法显示组内差异(NOP=0.72，SAL=0.60，SM=0.11，Ldj=0.71)，前半部分和后半部分有较强的相关性(试验内R(2)S：NOP=0.22n.S，SAL=0.33，SM=0.36，Ldj=0.91)，一个与其他运动学参数(SM)无关，三个具有较强的多元线性回归模型(R(2)S：NOP=0.61，SAL=0.48，Ldj=0.70)。基于我们的结果，我们对使用检验的光滑性度量提出了建议。总体而言，只要控制试验持续时间，原木无因次挺举被证明是最合适的ADL。
Smoothness is a main characteristic of goal-directed human movements. The suitability of approaches quantifying movement smoothness is dependent on the analyzed signal's structure. Recently, activities of daily living (ADL) received strong interest in research on aging and neurorehabilitation. Such tasks have complex signal structures and kinematic parameters need to be adapted. In the present study we examined four different approaches to quantify movement smoothness in ADL. We tested the appropriateness of these approaches, namely the number of velocity peaks per meter (NoP), the spectral arc length (SAL), the speed metric (SM) and the log dimensionless jerk (LDJ), by comparing movement signals from eight healthy elderly (67.1a +/- 7.1a) with eight healthy young (26.9a +/- 2.1a) participants performing an activity of daily living (making a cup of tea). All approaches were able to identify group differences in smoothness (Cohen's d NoP = 2.53, SAL = 1.95, SM = 1.69, LDJ = 4.19), three revealed high to very high sensitivity (z-scores: NoP = 1.96 +/- 0.55, SAL = 1.60 +/- 0.64, SM = 3.41 +/- 3.03, LDJ = 5.28 +/- 1.52), three showed low within-group variance (NoP = 0.72, SAL = 0.60, SM = 0.11, LDJ = 0.71), two showed strong correlations between the first and the second half of the task execution (intra-trial R(2)s: NoP = 0.22 n. s., SAL = 0.33, SM = 0.36, LDJ = 0.91), and one was independent of other kinematic parameters (SM), while three showed strong models of multiple linear regression (R(2)s: NoP = 0.61, SAL = 0.48, LDJ = 0.70). Based on our results we make suggestion toward use examined smoothness measures. In total the log dimensionless jerk proved to be the most appropriate in ADL, as long as trial durations are controlled.