Image-based Activity Recognition from IMU Data

被引：0

作者：

Guinea, Alejandro Sanchez ^{[1
]}

Sarabchian, Mehran ^{[1
]}

Muhlhauser, Max ^{[1
]}

机构：

[1] Tech Univ Darmstadt, Darmstadt, Germany

来源：

2021 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS AND OTHER AFFILIATED EVENTS (PERCOM WORKSHOPS) | 2021年

关键词：

Activity Recognition; Image Representation; Image Processing; Wearable sensors; NEURAL-NETWORKS;

D O I：

10.1109/PERCOMWORKSHOPS51409.2021.9430990

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper we propose an approach to improve the performance of human activity recognition (HAR) from inertial sensors data, based on image processing techniques. Our approach creates image representations of the time-series data to take advantage of the strengths that convolutional neural networks (CNNs) have shown when dealing with image data. We have conducted an evaluation using benchmark datasets that are considered among the most relevant in HAR. Our results show that our approach is able to outperform the state of the art in all cases.

引用

页码：14 / 19

页数：6

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