在日常活动任务中使用可穿戴传感器进行人体运动检测

Olga Politi, I. Mporas, V. Megalooikonomou
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引用次数: 21

摘要

在这篇文章中,我们提出了一个人体运动检测框架,该框架基于来自单个三轴加速度计的数据。该框架使用一组不同的预处理方法来生成数据表示,这些数据表示分别由统计特征和物理特征参数化。然后将这些特征连接起来,并使用众所周知的运动识别问题的分类算法进行分类。实验评估是根据受试者相关情景进行的,这意味着每个受试者使用自己的数据分别进行分类,并计算所有个体的平均准确率。使用USC-HAD数据库,对14种日常人体运动活动的最佳检测性能约为95%。结果与相同数据库的93.1%的最佳报告性能相比具有竞争力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Human motion detection in daily activity tasks using wearable sensors
In this article we present a human motion detection frame-work, based on data derived from a single tri-axial accelerometer. The framework uses a set of different pre-processing methods that produce data representations which are respectively parameterized by statistical and physical features. These features are then concatenated and classified using well-known classification algorithms for the problem of motion recognition. Experimental evaluation was carried out according to a subject-dependent scenario, meaning that the classification is performed for each subject separately using their own data and the average accuracy for all individuals is computed. The best achieved detection performance for 14 everyday human motion activities, using the USC-HAD database, was approximately 95%. The results compare favorably are competitive to the best reported performance of 93.1% for the same database.
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