Toward application of immunity-based model to gait recognition using smart phone sensors: a study of various walking states
Toward application of immunity-based model to gait recognition using smart phone sensors: a study of various walking states
复制标题
基于免疫的模型在使用智能手机传感器的步态识别中的应用：对各种行走状态的研究
DOI：
10.1016/j.procs.2015.08.296
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Yuji Watanabe
中科院分区：
文献类型：
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作者：
Dieter Gollmann;Atsuko Miyaji;Hiroaki Kikuchi;南和宏;南和宏;Yuji Watanabe and Liu Kun;南 和宏;Yuji Watanabe and San Sara;Yuji Watanabe
在我们之前的步态识别研究中，我们在iOS上为智能手机开发了应用程序，不仅可以记录加速度，还可以记录更多数据，例如手机围绕3个轴的旋转。然后，我们使用该应用程序进行了初步实验，以收集用户生成的加速度数据，当手机被拿着打电话和触摸以及在口袋里。然而，一天仅记录了4名受试者的加速度数据。在本研究中，我们进行了额外的实验，增加了受试者的数量，并增加了其他一天记录的数据。结果表明，虽然正确的分类率时，手机在口袋里，还没有受到影响的人数，在呼叫和触摸的情况下，性能的影响，受试者人数少。有趣的是，我们观察到，在其他一天的额外数据有不同的影响识别性能根据手机持有状态。我们还研究了一个主题的各种行走状态的影响。此外，我们讨论了基于免疫诊断模型的步态识别集成多个智能手机传感器的识别结果的应用。
In our previous study on gait recognition, we developed the application on iOS for smart phone to record not only the acceleration but also more data such as the rotation of the phone around the 3 axes. And then we performed preliminary experiments using the application to collect the user-generated acceleration data on walking when the phone was held calling and touching as well as in the pocket. However, the acceleration data were recorded for only 4 subjects on one day. In this study, we carry out additional experiments increasing the number of subjects and adding the data recorded on other day. The results show that although the correctly classified rate when the phone is in the pocket is not yet affected by the number of subjects, the performance in the cases of calling and touching is influenced by the small number of subjects. Interestingly, we observe that the additional data on other day have a different effect on identification performance according to phone holding states. We also examine the influence of various walking states for one subject. Furthermore, we discuss the application of an immunity-based diagnosis model to gait recognition to integrate the identification results from multiple smart phone sensors.