A Practical Wearable Sensor-based Human Activity Recognition Research Pipeline

Hui Liu, Yale Hartmann, Tanja Schultz
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引用次数: 18

Abstract

: Many researchers devote themselves to studying various aspects of Human Activity Recognition (HAR), such as data analysis, signal processing, feature extraction, and machine learning models. In response to the fact that few documents summarize and form intuitive paradigms for the entire HAR research pipeline, based on the purpose of sharing our years of research experience, we propose a practical, comprehensive HAR research pipeline, called HAR-Pipeline, composed of nine research aspects, aiming to reflect the overall perspective of HAR research topics to the greatest extent and indicate the sequence and relationship between the tasks. Supplemented by the outcomes of our actual series of studies as examples, we demonstrate the proposed pipeline’s rationality and feasibility.
基于实用可穿戴传感器的人体活动识别研究管道
许多研究者致力于研究人类活动识别(Human Activity Recognition, HAR)的各个方面,如数据分析、信号处理、特征提取和机器学习模型。针对目前很少有文献对整个HAR研究管道进行总结和形成直观范式的问题,基于分享我们多年研究经验的目的,我们提出了一个实用的、全面的HAR研究管道,称为HAR- pipeline,由九个研究方面组成,旨在最大程度地反映HAR研究主题的整体视角,并表明任务之间的顺序和关系。并以实际研究结果为例,论证了该管道的合理性和可行性。
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