.png){kind=icon}
返回
Improving Wearable-Based Activity Recognition Using Image Representations
.png){kind=icon}
OA
AI
DOI：10.3390/s22051840.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Activity recognition based on inertial sensors is an essential task in mobile and ubiquitous computing. To date, the best performing approaches in this task are based on deep learning models. Although the performance of the approaches has been increasingly improving, a number of issues still remain. Specifically, in this paper we focus on the issue of the dependence of today's state-of-the-art approaches to complex ad hoc deep learning convolutional neural networks (CNNs), recurrent neural networks (RNNs), or a combination of both, which require specialized knowledge and considerable effort for their construction and optimal tuning. To address this issue, in this paper we propose an approach that automatically transforms the inertial sensors time-series data into images that represent in pixel form patterns found over time, allowing even a simple CNN to outperform complex ad hoc deep learning models that combine RNNs and CNNs for activity recognition. We conducted an extensive evaluation considering seven benchmark datasets that are among the most relevant in activity recognition. Our results demonstrate that our approach is able to outperform the state of the art in all cases, based on image representations that are generated through a process that is easy to implement, modify, and extend further, without the need of developing complex deep learning models.
Keyword:
human activity recognition
image representation
CNNs
IMU
inertial sensors
wearable sensors
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
Distilling the Knowledge From Handcrafted Features for Human Activity Recognition从手工特征中提取知识用于人类活动识别OAAI
OAAI分享收藏分享收藏The impact of solid solution composition on kinetics and mechanism of [2 + 2] photodimerization of cinnamic acid derivatives.png)
CrystEngComm
OAAI
CrystEngCommIF0
OAAI分享收藏Weakly Supervised Multi-Task Representation Learning for Human Activity Analysis Using WearablesOAAI
OAAI分享收藏Automated detection of the head-twitch response using wavelet scalograms and a deep convolutional neural networkSCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏
查看更多内容