Contrastive learning has emerged as a competent approach for unsupervised representation learning. However, the design of an optimal augmentation strategy, although crucial for contrastive learning, is less explored for time series classification tasks. Existing predefined time-domain augmentation methods are primarily adopted from vision and are not specific to time series data. Consequently, this cross-modality incompatibility may distort the semantically relevant information of time series by introducing mismatched patterns into the data. To address this limitation, we present a novel perspective from the frequency domain and identify three advantages for downstream classification: 1) the frequency component naturally encodes global features, 2) the orthogonal nature of the Fourier basis allows easier isolation and independent modifications of critical and unimportant information, and 3) a compact set of frequency components can preserve semantic integrity. To fully utilize the three properties, we propose the lightweight yet effective Frequency-Refined Augmentation (FreRA) tailored for time series contrastive learning on classification tasks, which can be seamlessly integrated with contrastive learning frameworks in a plug-and-play manner. Specifically, FreRA automatically separates critical and unimportant frequency components. Accordingly, we propose semantic-aware Identity Modification and semantic-agnostic Self-adaptive Modification to protect semantically relevant information in the critical frequency components and infuse variance into the unimportant ones respectively. Theoretically, we prove that FreRA generates semantic-preserving views. Empirically, we conduct extensive experiments on two benchmark datasets, including UCR and UEA archives, as well as five large-scale datasets on diverse applications. FreRA consistently outperforms ten leading baselines on time series classification, anomaly detection, and transfer learning tasks, demonstrating superior capabilities in contrastive representation learning and generalization in transfer learning scenarios across diverse datasets. The code is available at https://github.com/Tian0426/FreRA.

Self-supervised learning establishes a new paradigm of learning representations with much fewer or even no label annotations. Recently there has been remarkable progress on large-scale contrastive learning models which require substantial computing resources, yet such models are not practically optimal for small-scale tasks. To fill the gap, we aim to study contrastive learning on the wearable-based activity recognition task. Specifically, we conduct an in-depth study of contrastive learning from both algorithmic-level and task-level perspectives. For algorithmic-level analysis, we decompose contrastive models into several key components and conduct rigorous experimental evaluations to better understand the efficacy and rationale behind contrastive learning. More importantly, for task-level analysis, we show that the wearable-based signals bring unique challenges and opportunities to existing contrastive models, which cannot be readily solved by existing algorithms. Our thorough empirical studies suggest important practices and shed light on future research challenges. In the meantime, this paper presents an open-source PyTorch library CL-HAR, which can serve as a practical tool for researchers. The library is highly modularized and easy to use, which opens up avenues for exploring novel contrastive models quickly in the future.

The ability to simultaneously distinguish objects, materials, and their associated physical properties is one fundamental function of the sense of touch. Recent advances in the development of tactile sensors and machine learning techniques allow more accurate and complex modelling of robotic tactile sensations. However, many state-of-the-art (SotA) approaches focus solely on constructing black-box models to achieve ever higher classification accuracy and fail to adapt across sensors with unique spatial-temporal data formats. In this work, we propose an Explainable and Sensor-Adaptable Recurrent Networks (ExSARN) model for tactile texture representation. The ExSARN model consists of a two-stage recurrent networks fed by a sensor-specific header network. The first stage recurrent network emulates our human touch receptors and decouples sensor-specific tactile sensations into different frequency response bands, while the second stage codes the overall temporal signature as a variational recurrent autoen-coder. We infuse the latent representation with ternary labels to qualitatively represent texture properties (e.g. roughness and stiffness), which facilitates representation learning and provide explainability to the latent space. The ExSARN model is tested on texture datasets collected with two different tactile sensors. Our results show that the proposed model not only achieves higher accuracy, but also provides adaptability across sensors with different sampling frequencies and data formats. The addition of the crudely obtained qualitative property labels offers a practical approach to enhance the interpretability of the latent space, facilitate property inference on unseen materials, and improve the overall performance of the model.