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

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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.