.png){kind=icon}
返回
Multiclass autoencoder-based active learning for sensor-based human activity recognition
.png){kind=icon}
PRE
AI
DOI：10.1016/j.future.2023.09.029.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Leveraging the enormous amounts of real-world data collected through Internet of Things (IoT) technologies, human activity recognition (HAR) has become a crucial component of numerous human-centric applications, with the aim of enhancing the quality of human life. While the recent advancements in deep learning have significantly improved HAR, the process of labeling data continues to remain a significant challenge due to the substantial costs associated with human annotation for supervised model training. Active learning (AL) addresses this issue by strategically selecting informative samples for labeling during model training, thereby enhancing model performance. Although numerous approaches have been proposed for sample selection, which consider aspects of uncertainty and representation, the difficulties in estimating uncertainty and exploiting distribution of high-dimensional data still pose a major issue. Our proposed deep learning-based active learning algorithm, called Multiclass Autoencoder-based Active Learning (MAAL), learns latent representation leveraging the capacity of Deep Support Vector Data Description (Deep SVDD). With the multiclass autoencoder which learns the normal characteristics of each activity class in the latent space, MAAL provides an informative sample selection for model training by establishing a link between the HAR model and the selection model. We evaluate our proposed MAAL using two publicly available datasets. The performance results demonstrate the improvements across the overall active learning rounds, achieving enhancements up to 3.23% accuracy and 3.67% in the F1 score. Furthermore, numerical results and analysis of sample selection are presented to validate the effectiveness of the proposed MAAL compared to the alternative comparison methods.
Keyword:
Internet of Things
Active learning
Human activity recognition
Deep learning
Multiclass autoencoder
Multivariate time series
期刊
F
IF:
6.1
论文数:
6.9K
被引数:
2.3W
机构
暂无机构信息
引用论文
Classification at the accuracy limit: facing the problem of data ambiguity精度极限的分类: 面对数据歧义的问题.png)
SCIENTIFIC REPORTS
OAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏Mechanisms of Disease: local renin–angiotensin–aldosterone systems and the pathogenesis and treatment of cardiovascular disease疾病机制: 局部肾素-血管紧张素-醛固酮系统与心血管疾病的发病机制和治疗
PREAI
分享收藏A review of uncertainty quantification in deep learning: Techniques, applications and challenges深度学习中的不确定性量化: 技术、应用与挑战INFORMATION FUSIONOAAI
INFORMATION FUSIONIF15.5
OAAI分享收藏分享收藏
查看更多内容