Feature Selection Algorithm Considering Trial and Individual Differences for Machine Learning of Human Activity Recognition
Feature Selection Algorithm Considering Trial and Individual Differences for Machine Learning of Human Activity Recognition
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DOI：
10.20965/jaciii.2017.p0813
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
Yuto Omae;Hirotaka Takahashi
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文献类型：
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作者：
Yuto Omae;Hirotaka Takahashi
近年来，利用惯性传感器和机器学习相结合的方法，对基于惯性传感器数据的人体运动自动分类进行了大量的研究，在传感器数据和人体运动相互对应的情况下，需要训练数据。由于担心受试者的疲劳或受伤，在较长的时间内进行涉及大量受试者的实验可能很困难。因此，许多研究允许少数受试者在分类的情况下执行重复的身体运动，以获取用于建立训练数据的数据。使用这种训练数据构造的任何分类器都会存在一些问题，这些问题与个体和试验差异造成的泛化错误有关。为了抑制这种泛化误差，必须获得不太可能由于个体差异和试验差异而产生泛化误差的特征空间。为了获得这样的特征空间，我们需要指标来评估特征空间由于个别错误和试错而产生泛化错误的可能性。因此，本文旨在从这些角度设计这样的评价指标。本文提出的评价指标可以通过构造表示个体差异和试验差异的数据概率分布，然后使用这些概率分布来计算产生泛化误差的任何风险来获得。通过对蝶泳和蛙泳传感器数据的应用，验证了该评价方法的有效性。为了便于比较，我们还采用了几种现有的评估方法。将支持向量机应用于已有方法得到的特征空间，构建了蝶泳和蛙泳的分类器。基于对测试数据进行的精度验证，我们发现该方法产生的F-度量明显高于现有方法。这证明了所提出的评价指标的使用使我们能够获得一个不太可能由于个体和试验差异而产生泛化误差的特征空间。
In recent years, many studies have been performed on the automatic classification of human body motions based on inertia sensor data using a combination of inertia sensors and machine learning; training data is necessary where sensor data and human body motions correspond to one another. It can be difficult to conduct experiments involving a large number of subjects over an extended time period, because of concern for the fatigue or injury of subjects. Many studies, therefore, allow a small number of subjects to perform repeated body motions subject to classification, to acquire data on which to build training data. Any classifiers constructed using such training data will have some problems associated with generalization errors caused by individual and trial differences. In order to suppress such generalization errors, feature spaces must be obtained that are less likely to generate generalization errors due to individual and trial differences. To obtain such feature spaces, we require indices to evaluate the likelihood of the feature spaces generating generalization errors due to individual and trial errors. This paper, therefore, aims to devise such evaluation indices from the perspectives. The evaluation indices we propose in this paper can be obtained by first constructing acquired data probability distributions that represent individual and trial differences, and then using such probability distributions to calculate any risks of generating generalization errors. We have verified the effectiveness of the proposed evaluation method by applying it to sensor data for butterfly and breaststroke swimming. For the purpose of comparison, we have also applied a few available existing evaluation methods. We have constructed classifiers for butterfly and breaststroke swimming by applying a support vector machine to the feature spaces obtained by the proposed and existing methods. Based on the accuracy verification we conducted with test data, we found that the proposed method produced significantly higher F-measure than the existing methods. This proves that the use of the proposed evaluation indices enables us to obtain a feature space that is less likely to generate generalization errors due to individual and trial differences.