Development of Accelerator-based Activity Recognition Technique Robust for Missing Data

这项研究检验了即使在部分传感器数据丢失时也能执行的活动识别技术。从模式识别技术的过程出发，研究了缺失传感器数据的补全、缺失特征值的补全、剩余特征的分类。对于缺失传感器数据的补全，采用ARAR模型进行序列数据预测。为了补全缺失的特征值，利用了多元回归和核回归。对于剩余特征的分类，在每个传感器上人工省略传感器数据的情况下学习多个分类器，并在传感器丢失的情况下进行适当的选择。从一些实验来看，在很多情况下，补全缺失的传感器数据或特征值比处理分类器的选择获得了更好的性能。此外，根据缺失传感器的位置不同，适当的方法也不同。然后，发现使用合适的缺失传感器位置方法处理缺失传感器数据的性能与没有缺失情况或f-measure下降约0.03点几乎相同。

This study examined the activity recognition technique that can perform even when a part of sensor data is missing. From the process of pattern recognition technique, which is used in most existing activity recognition techniques, completion of missing sensor data, completion of missing feature value, classification with remaining features are examined. For the completion of missing sensor data, the sequential data prediction with the ARAR model is used. For the completion of missing feature value, multiple regression and kernel regression are exploited. For the classification with remaining features, multiple classifiers are learned with sensor data artificially omitted on each sensor, and are selected appropriately in the situation of the loss of sensor. From some experiments, the completion of missing sensor data or feature value achieves better performance than coping with the selection of classifiers in many cases. In addition, the appropriate method differs depending on the position of missing sensor. Then, it was found that using appropriate method for the position of missing sensor achieves almost same performance as no missing case or degradation of about 0.03 point in f-measure for coping with missing sensor data.