基于运动数据的人类活动识别问题的聚类方法

2015 Federated Conference on Computer Science and Information Systems (FedCSIS) Pub Date : 2015-10-11 DOI:10.15439/2015F424

Szymon Wawrzyniak, Wojciech Niemiro

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引用次数: 10

摘要

本文描述了作者对AAIA'15数据挖掘竞赛任务集的解决方案:标记火灾现场的消防员活动(https://knowledgepit.fedcsis.org/contest/view.php?id=106)。该方法采用K-Means聚类，采用独特的标签变换技术对预处理后的时间序列数据进行LDA分类。数据收集自加速度计和陀螺仪读数。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Clustering approach to the problem of human activity recognition using motion data

This paper describes authors' solution to the task set in AAIA'15 Data Mining Competition: Tagging Firefighter Activities at a Fire Scene (https://knowledgepit.fedcsis.org/contest/view.php?id=106). Method involves LDA classification on a preprocessed time series data with a unique label transformation technique using K-Means clustering. Data were collected from accelerometer and gyroscope readings.

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2015 Federated Conference on Computer Science and Information Systems (FedCSIS)

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