Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmark

Unsupervised Domain Adaptation (UDA) aims to harness labeled source data to train models for unlabeled target data. Despite extensive research in domains like computer vision and natural language processing, UDA remains underexplored for time series data, which has widespread real-world applications ranging from medicine and manufacturing to earth observation and human activity recognition. Our paper addresses this gap by introducing a comprehensive benchmark for evaluating UDA techniques for time series classification, with a focus on deep learning methods. We provide seven new benchmark datasets covering various domain shifts and temporal dynamics, facilitating fair and standardized UDA method assessments with state of the art neural network backbones (e.g. Inception) for time series data. This benchmark offers insights into the strengths and limitations of the evaluated approaches while preserving the unsupervised nature of domain adaptation, making it directly applicable to practical problems. Our paper serves as a vital resource for researchers and practitioners, advancing domain adaptation solutions for time series data and fostering innovation in this critical field. The implementation code of this benchmark is available at https://github.com/EricssonResearch/UDA-4-TSC

Unsupervised domain adaptation in sensor-based human activity recognition

Sensor-based human activity recognition (HAR) is to recognise human daily activities through a collection of ambient and wearable sensors. Sensor-based human activity recognition is having a significant impact in a wide range of applications in smart city, smart home, and personal healthcare. Such wide deployment of HAR systems often faces the annotation-scarcity challenge; that is, most of the HAR techniques, especially the deep learning techniques, require a large number of training data while annotating sensor data is very time- and effort-consuming. Unsupervised domain adaptation has been successfully applied to tackle this challenge, where the activity knowledge from a well-annotated domain can be transferred to a new, unlabelled domain. However, existing techniques do not perform well on highly heterogeneous domains. To address this problem, this thesis proposes unsupervised domain adaptation models for human activity recognition. The first model presented is a new knowledge- and data-driven technique to achieve coarse- and fine-grained feature alignment using variational autoencoders. This proposed approach demonstrates high recognition accuracy and robustness against sensor noise, compared to the state-of-the-art domain adaptation techniques. However, the limitations with this approach are that knowledge-driven annotation can be inaccurate and also the model incurs extra knowledge engineering effort to map the source and target domain. This limits the application of the model. To tackle the above limitation, we then present another two data-driven unsupervised domain adaptation techniques. The first method is based on bidirectional generative adversarial networks (Bi-GAN) to perform domain adaptation. In order to improve the matching between the source and target domain, we employ Kernel Mean Matching (KMM) to enable covariate shift correction between transformed source data and original target data so that they can be better aligned. This technique works well but it does not separate classes that have similar patterns. To tackle this problem, our second method includes contrastive learning during the adaptation process to minimise the intra-class discrepancy and maximise the inter-class margin. Both methods are validated with high accuracy results on various experiments using three HAR datasets and multiple transfer learning tasks in comparison with 12 state-of-the-art techniques

Nekoliko crkvenih pučkih napjeva iz Solina

Sensor-based human activity recognition is to recognise human daily activities through a collection of ambient and wearable sensors. It is the key enabler for many healthcare applications, especially in ambient assisted living. The advance of sensing and communication technologies has driven the deployment of sensors in many residential and care home settings. However, the challenge still resides in the lack of sufficient, high-quality activity annotations on sensor data, which most of the existing activity recognition algorithms rely on. In this paper, we propose an Unsupervised Domain adaptation technique for Activity Recognition, called UDAR, which supports sharing and transferring activity models from one dataset to another heterogeneous dataset without the need of activity labels on the latter. This approach has combined knowledge- and data-driven techniques to achieve coarse- and fine-grained feature alignment. We have evaluated UDAR on five third-party, real-world datasets and have demonstrated high recognition accuracy and robustness against sensor noise, compared to the state-of-the-art domain adaptation techniques.PostprintPeer reviewe

ContrasGAN : unsupervised domain adaptation in Human Activity Recognition via adversarial and contrastive learning

Human Activity Recognition (HAR) makes it possible to drive applications directly from embedded and wearable sensors. Machine learning, and especially deep learning, has made significant progress in learning sensor features from raw sensing signals with high recognition accuracy. However, most techniques need to be trained on a large labelled dataset, which is often difficult to acquire. In this paper, we present ContrasGAN, an unsupervised domain adaptation technique that addresses this labelling challenge by transferring an activity model from one labelled domain to other unlabelled domains. ContrasGAN uses bi-directional generative adversarial networks for heterogeneous feature transfer and contrastive learning to capture distinctive features between classes. We evaluate ContrasGAN on three commonly-used HAR datasets under conditions of cross-body, cross-user, and cross-sensor transfer learning. Experimental results show a superior performance of ContrasGAN on all these tasks over a number of state-of-the-art techniques, with relatively low computational cost.PostprintPeer reviewe

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly