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首页> 外文期刊>Advances in Experimental Medicine and Biology >Multi-complexity Ensemble Measures for Gait Time Series Analysis: Application to Diagnostics, Monitoring and Biometrics
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Multi-complexity Ensemble Measures for Gait Time Series Analysis: Application to Diagnostics, Monitoring and Biometrics

机译：步态时间序列分析的多复杂度综合度量：在诊断，监测和生物识别中的应用

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摘要

Previously, we have proposed to use complementary complexity measures discovered by boosting-like ensemble learning for the enhancement of quantitative indicators dealing with necessarily short physiological time series. We have confirmed robustness of such multi-complexity measures for heart rate variability analysis with the emphasis on detection of emerging and intermittent cardiac abnormalities. Recently, we presented preliminary results suggesting that such ensemble-based approach could be also effective in discovering universal meta-indicators for early detection and convenient monitoring of neurological abnormalities using gait time series. Here, we argue and demonstrate that these multi-complexity ensemble measures for gait time series analysis could have significantly wider application scope ranging from diagnostics and early detection of physiological regime change to gait-based biometrics applications.
机译：以前，我们已经提出将通过类似增强的整体学习发现的互补性复杂度度量用于处理必然较短的生理时间序列的定量指标的增强。我们已经证实了这种用于心率变异性分析的多复杂性测量方法的鲁棒性，重点是对新兴和间歇性心脏异常的检测。最近，我们提出了初步结果，表明这种基于集合的方法也可能有效地发现通用的元指标，以便使用步态时间序列对神经异常进行早期检测和方便地监测。在这里，我们争论并证明，用于步态时间序列分析的这些多复杂性集成度量可能具有更广泛的应用范围，从诊断和早期检测生理状态变化到基于步态的生物识别应用。

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