首页> 外文期刊>Intelligent automation and soft computing >Feature Selection for Activity Recognition from Smartphone Accelerometer Data
【24h】

Feature Selection for Activity Recognition from Smartphone Accelerometer Data

机译:通过智能手机加速度计数据进行活动识别的功能选择

获取原文
获取原文并翻译 | 示例

摘要

We use the public Human Activity Recognition Using Smartphones (HARUS) data-set to investigate and identify the most informative features for determining the physical activity performed by a user based on smartphone accelerometer and gyroscope data. The HARUS data-set includes 561 time domain and frequency domain features extracted from sensor readings collected from a smartphone carried by 30 users while performing specific activities. We compare the performance of a decision tree, support vector machines, Naive Bayes, multilayer perceptron, and bagging. We report the various classification performances of these algorithms for subject independent cases. Our results show that bagging and the multilayer perceptron achieve the highest classification accuracies across all feature sets. In addition, the signal from gravity contains the most information for classification of activities in the HARUS data-set.
机译:我们使用公开的使用智能手机的人类活动识别(HARUS)数据集来调查和识别最有用的功能,以根据智能手机加速度计和陀螺仪数据确定用户执行的身体活动。 HARUS数据集包括561个时域和频域特征,这些特征是从30名用户在执行特定活动时从智能手机收集的传感器读数中提取的。我们比较了决策树,支持向量机,朴素贝叶斯,多层感知器和装袋的性能。我们报告这些算法对主题无关情况的各种分类性能。我们的结果表明,套袋和多层感知器在所有功能集上均实现了最高的分类精度。此外,来自重力的信号还包含用于HARUS数据集中活动分类的最多信息。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号