Transportation mode plays an important role in enabling us to derive a mobile user's context, and to adapt intelligent services to this. However, current methods have two key limitations: a low recognition accurac...
详细信息
ISBN:
(纸本)9781479906529
Transportation mode plays an important role in enabling us to derive a mobile user's context, and to adapt intelligent services to this. However, current methods have two key limitations: a low recognition accuracy and coarse-grained recognition capability. In this paper, we propose a new post correction algorithm (PoCoA) that is applied after the use of typical classifiers to address these limitations. We evaluated the use of PoCoA for the following transportation modes, walking, cycling, bus passenger, metro passenger, car passenger, and car driver. PoCoA enhances a typical accelerometer-based transportation recognition method with a more accurate sub-classification of motorized transportation modes when tested on a dataset obtained from 15 individuals. Overall accuracy improved from 69% to 88% when comparing with a state of the art two-stage classifier (Decision Tree + Discrete Hidden Markov Model).
Transportation mode plays an important role in enabling us to derive a mobile user's context, and to adapt intelligent services to this. However, current methods have two key limitations: a low recognition accurac...
详细信息
ISBN:
(纸本)9781479906505
Transportation mode plays an important role in enabling us to derive a mobile user's context, and to adapt intelligent services to this. However, current methods have two key limitations: a low recognition accuracy and coarse-grained recognition capability. In this paper, we propose a new post correction algorithm (PoCoA) that is applied after the use of typical classifiers to address these limitations. We evaluated the use of PoCoA for the following transportation modes, walking, cycling, bus passenger, metro passenger, car passenger, and car driver. PoCoA enhances a typical accelerometer-based transportation recognition method with a more accurate subclassification of motorized transportation modes when tested on a dataset obtained from 15 individuals. Overall accuracy improved from 69% to 88% when comparing with a state of the art two-stage classifier (Decision Tree + Discrete Hidden Markov Model).
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