Human Activity Recognition Using Deep Models and Its Analysis from Domain Adaptation Perspective

2019 ◽  
pp. 189-202
Author(s):  
Nikita Gurov ◽  
Adil Khan ◽  
Rasheed Hussain ◽  
Asad Khattak
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Domain Adaptation ◽  
Human Activity Recognition ◽  
Its Analysis

AdaptNet: Human Activity Recognition via Bilateral Domain Adaptation using Semi-Supervised Deep Translation Networks

2021 ◽  
pp. 1-1
Author(s):  
Sungtae An ◽  
Alessio Medda ◽  
Michael N. Sawka ◽  
Clayton J. Hutto ◽  
Mindy L. Millard-Stafford ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Domain Adaptation ◽  
Human Activity Recognition

scholarly journals Data Augmentation vs. Domain Adaptation—A Case Study in Human Activity Recognition

Technologies ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 55
Author(s):  
Evaggelos Spyrou ◽  
Eirini Mathe ◽  
Georgios Pikramenos ◽  
Konstantinos Kechagias ◽  
Phivos Mylonas
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Data Augmentation ◽  
Domain Adaptation ◽  
Recognition Task ◽  
Human Activity Recognition ◽  
Video Data ◽  
Training Dataset ◽  
Data Systems ◽  
Big Data Systems

Recent advances in big data systems and databases have made it possible to gather raw unlabeled data at unprecedented rates. However, labeling such data constitutes a costly and timely process. This is especially true for video data, and in particular for human activity recognition (HAR) tasks. For this reason, methods for reducing the need of labeled data for HAR applications have drawn significant attention from the research community. In particular, two popular approaches developed to address the above issue are data augmentation and domain adaptation. The former attempts to leverage problem-specific, hand-crafted data synthesizers to augment the training dataset with artificial labeled data instances. The latter attempts to extract knowledge from distinct but related supervised learning tasks for which labeled data is more abundant than the problem at hand. Both methods have been extensively studied and used successfully on various tasks, but a comprehensive comparison of the two has not been carried out in the context of video data HAR. In this work, we fill this gap by providing ample experimental results comparing data augmentation and domain adaptation techniques on a cross-viewpoint, human activity recognition task from pose information.

scholarly journals A Semi-Supervised Transfer Learning with Dynamic Associate Domain Adaptation for Human Activity Recognition Using WiFi Signals

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8475
Author(s):  
Yuh-Shyan Chen ◽  
Yu-Chi Chang ◽  
Chun-Yu Li
Keyword(s):  
Transfer Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Domain Adaptation ◽  
Noise Removal ◽  
Human Activity Recognition ◽  
Fine Tuning ◽  
Target Domain ◽  
Adaptation Algorithm ◽  
Learning Stage

Human activity recognition without equipment plays a vital role in smart home applications, freeing humans from the shackles of wearable devices. In this paper, by using the channel state information (CSI) of the WiFi signal, semi-supervised transfer learning with dynamic associate domain adaptation is proposed for human activity recognition. In order to improve the CSI quality and denoising of CSI, we carried out missing packet filling, burst noise removal, background estimation, feature extraction, feature enhancement, and data augmentation in the data pre-processing stage. This paper considers the problem of environment-independent human activity recognition, also known as domain adaptation. The pre-trained model is trained from the source domain by collecting a complete labeled dataset of all of the CSI of human activity patterns. Then, the pre-trained model is transferred to the target environment through the semi-supervised transfer learning stage. Therefore, when humans move to different target domains, a partial labeled dataset of the target domain is required for fine-tuning. In this paper, we propose a dynamic associate domain adaptation called DADA. By modifying the existing associate domain adaptation algorithm, the target domain can provide a dynamic ratio of labeled dataset/unlabeled dataset, while the existing associate domain adaptation algorithm only allows target domains with the unlabeled dataset. The advantage of DADA is that it provides a dynamic strategy to eliminate different effects on different environments. In addition, we further designed an attention-based DenseNet model, or AD, as our training network, which is modified by an existing DenseNet by adding the attention function. The solution we proposed was simplified to DADA-AD throughout the paper. The experimental results show that for domain adaptation in different domains, the accuracy of human activity recognition of the DADA-AD scheme is 97.4%. It also shows that DADA-AD has advantages over existing semi-supervised learning schemes.

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