Deep Transfer Learning for Cross-domain Activity Recognition

Wang, Jindong; Zheng, Vincent W.; Chen, Yiqiang; Huang, Mei‐Yu

doi:10.1145/3265689.3265705

Cited by 118 publications

(76 citation statements)

References 40 publications

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“…We also demonstrate that the learned representations from a di erent (but related) unlabeled data source can be successfully transferred to improve the performance of diverse tasks even in the case of semi-supervised learning. In terms of transfer learning, our approach also di ers signi cantly from some earlier a empts [44,69] that were concerned with features transferability from a fully-supervised model learned from inertial measurement units data, as our approach utilizes widely available smartphones and does not require labeled data. Finally, the proposed technique is also di erent from previously studied unsupervised pre-training methods such as autoencoders [37], restricted Boltzmann machines [55] and sparse coding [9] as we employ an end-to-end (self) supervised learning paradigm on multiple surrogate tasks to extract features.…”

Section: Determining Representational Similaritymentioning

confidence: 99%

Multi-task Self-Supervised Learning for Human Activity Detection

Saeed

Özçelebi

Lukkien

2019

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

248

199

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Deep learning methods are successfully used in applications pertaining to ubiquitous computing, pervasive intelligence, health, and well-being. Speci cally, the area of human activity recognition (HAR) is primarily transformed by the convolutional and recurrent neural networks, thanks to their ability to learn semantic representations directly from raw input. However, in order to extract generalizable features massive amounts of well-curated data are required, which is a notoriously challenging task; hindered by privacy issues and annotation costs. erefore, unsupervised representation learning (i.e., learning without manually labeling the instances) is of prime importance to leverage the vast amount of unlabeled data produced by smart devices. In this work, we propose a novel self-supervised technique for feature learning from sensory data that does not require access to any form of semantic labels, i.e., activity classes. We learn a multi-task temporal convolutional network to recognize transformations applied on an input signal. By exploiting these transformations, we demonstrate that simple auxiliary tasks of the binary classi cation result in a strong supervisory signal for extracting useful features for the down-stream task. We extensively evaluate the proposed approach on several publicly available datasets for smartphone-based HAR in unsupervised, semi-supervised and transfer learning se ings. Our method achieves performance levels superior to or comparable with fully-supervised networks trained directly with activity labels, and it performs signi cantly be er than unsupervised learning through autoencoders. Notably, for the semi-supervised case, the self-supervised features substantially boost the detection rate by a aining a kappa score between 0.7 − 0.8 with only 10 labeled examples per class. We get similar impressive performance even if the features are transferred from a di erent data source. Self-supervision drastically reduces the requirement of labeled activity data, e ectively narrowing the gap between supervised and unsupervised techniques for learning meaningful representations. While this paper focuses on HAR as the application domain, the proposed approach is general and could be applied to a wide variety of problems in other areas. 000:2 • A. Saeed et al.activity recognition (HAR), 1D convolutional and recurrent neural networks trained on raw labeled signals signi cantly improve the detection rate over traditional methods [20,44,68,72,73]. Despite the recent advances in the eld of HAR, learning representations from a massive amount of unlabeled data still presents a signi cant challenge. Obtaining large, well-curated activity recognition datasets is problematic due to a number of issues. First, smartphone data are privacy sensitive, which makes it hard to collect su cient amounts of user-activity instances in a real-life se ing. Second, the annotation cost and the time it takes to generate a large volume of labeled instances are prohibitive. Finally, the diversity of devices, types of embedd...

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Section: Determining Representational Similaritymentioning

confidence: 99%

Multi-task Self-Supervised Learning for Human Activity Detection

Saeed

Özçelebi

Lukkien

2019

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

248

199

View full text Add to dashboard Cite

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“…Recent studies have indicated that deep networks can learn more transferable features for domain adaptation [12], [13]. The latest advances have been achieved by embedding domain adaptation modules in the pipeline of deep feature learning to extract domain-invariant representations [14], [15], [16], [17], [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning with Dynamic Adversarial Adaptation Network

Wang

Chen

et al. 2019

2019 IEEE International Conference on Data Mining (ICDM)

Self Cite

252

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The recent advances in deep transfer learning reveal that adversarial learning can be embedded into deep networks to learn more transferable features to reduce the distribution discrepancy between two domains. Existing adversarial domain adaptation methods either learn a single domain discriminator to align the global source and target distributions, or pay attention to align subdomains based on multiple discriminators. However, in real applications, the marginal (global) and conditional (local) distributions between domains are often contributing differently to the adaptation. There is currently no method to dynamically and quantitatively evaluate the relative importance of these two distributions for adversarial learning. In this paper, we propose a novel Dynamic Adversarial Adaptation Network (DAAN) to dynamically learn domain-invariant representations while quantitatively evaluate the relative importance of global and local domain distributions. To the best of our knowledge, DAAN is the first attempt to perform dynamic adversarial distribution adaptation for deep adversarial learning. DAAN is extremely easy to implement and train in real applications. We theoretically analyze the effectiveness of DAAN, and it can also be explained in an attention strategy. Extensive experiments demonstrate that DAAN achieves better classification accuracy compared to stateof-the-art deep and adversarial methods. Results also imply the necessity and effectiveness of the dynamic distribution adaptation in adversarial transfer learning.

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“…In the work of Wang et al [33], the authors developed a DA method for different scenarios: adaptation between similar body parts on the same person, different body parts on the same person, and similar body parts on different people. Their method was evaluated with public datasets, as PAMAP2 and OPPORTUNITY, against six common alternatives performing on average better.…”

Section: Related Work a Domain Adaptationmentioning

confidence: 99%

Improving Cross-Subject Activity Recognition via Adversarial Learning

Leite

Xiao

2020

IEEE Access

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Deep learning has been widely used for implementing human activity recognition from wearable sensors like inertial measurement units. The performance of deep activity recognition is heavily affected by the amount and variability of the labeled data available for training the deep learning models. On the other hand, it is costly and time-consuming to collect and label data. Given limited training data, it is hard to maintain high performance across a wide range of subjects, due to the differences in the underlying data distribution of the training and the testing sets. In this work, we develop a novel solution that applies adversarial learning to improve cross-subject performance by generating training data that mimic artificial subjects-i.e. through data augmentation-and enforcing the activity classifier to ignore subject-dependent information. Contrary to domain adaptation methods, our solution does not utilize any data from subjects of the test set (or target domain). Furthermore, our solution is versatile as it can be utilized together with any deep neural network as the classifier. Considering the open dataset PAMAP2, nearly 10% higher crosssubject performance in terms of F1-score can be achieved when training a CNN-LSTM-based classifier with our solution. A performance gain of 5% is also observed when our solution is applied to a stateof-the-art HAR classifier composed of a combination of inception neural network and recurrent neural network. We also investigate different influencing factors of classification performance (i.e. selection of sensor modalities, sampling rates and the number of subjects in the training data), and summarize a practical guideline for implementing deep learning solutions for sensor-based human activity recognition.

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Deep Transfer Learning for Cross-domain Activity Recognition

Cited by 118 publications

References 40 publications

Multi-task Self-Supervised Learning for Human Activity Detection

Multi-task Self-Supervised Learning for Human Activity Detection

Transfer Learning with Dynamic Adversarial Adaptation Network

Improving Cross-Subject Activity Recognition via Adversarial Learning

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