Estimating Distracted Pedestrian from Deviated Walking Considering Consumption of Working Memory
Estimating Distracted Pedestrian from Deviated Walking Considering Consumption of Working Memory
复制标题
考虑工作记忆的消耗，估计因偏离行走而分心的行人
DOI：
10.1109/csci.2016.0220
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
H. Shimakawa
中科院分区：
文献类型：
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作者：
Y. Uemura;Yusuke Kajiwara;H. Shimakawa
提出了一种利用行人行走时的加速度和角速度来区分分心行人和正常行人的方法。该方法使用附着在行人背部的加速度传感器。得到了行人行走时的加速度和角速度。除此之外，基于所获得的数据计算行走特征。一些研究指出行人的分心与工作记忆的消耗有关。我们假设考虑工作记忆的消耗和步行行为之间的关系，建议有效地估计分心的行人。当每个行人一边走一边消耗工作记忆时，例如思考一些事情，他们的行走就会偏离正常。机器学习方法，随机森林，被应用到分类行人是否分心使用步行的特征。实验结果表明，我们可以完全区分分心状态和正常状态下的行走特征。实验结果表明，该方法能够发现工作记忆高度消耗的分心行人。我们讨论了为什么我们可以区分分心的行人步行功能组件的变量的重要性。此外，我们还对显著特征成分进行了回归分析，以找出原因。最后，我们讨论了我们提出的方法的可行性。
This paper proposes a method to distinguish distracted pedestrians from normal pedestrians, using the acceleration and the angular velocity while walking. This method uses an acceleration sensor attached on the back of the pedestrian. The acceleration and the angular velocity are obtained while the pedestrian is walking. In addition to that, walking features are calculated based on the obtained data. Some studies points out distraction of the pedestrian relates to consumption of working memory. We assume considering the relationship between consumption of working memory and walking behavior suggest the effectiveness to estimate distraction of the pedestrian. When each pedestrian is walking while consuming working memory, for example thinking about something, their walk deviates from normal. Machine Learning method, Random Forest, is applied to classify whether the pedestrian is distracted using features of walking. An experiment suggests we can distinguish walking features represent both distracted state and normal state completely. The result indicates the method can find distracted pedestrians whose working memory is highly consumed. We discuss why we can distinguish the distraction of the pedestrian from walking feature components with the variable importance. In addition, we have conducted regression analysis on the significant feature components to figure out the reasons. Finally, we discuss the feasibility of our proposed method.