Medical time series are sequential data collected over time that measures health-related
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …

Recently, Self-Supervised Representation Learning (SSRL) has attracted much attention in
the field of computer vision, speech, natural language processing (NLP), and recently, with …

A major bottleneck in training robust Human-Activity Recognition models (HAR) is the need
for large-scale labeled sensor datasets. Because labeling large amounts of sensor data is …

Machine learning and deep learning have shown great promise in mobile sensing
applications, including Human Activity Recognition. However, the performance of such …

Self-Supervised Learning (SSL) is a new paradigm for learning discriminative
representations without labeled data, and has reached comparable or even state-of-the-art …

The emergence of self-supervised learning in the field of wearables-based human activity
recognition (HAR) has opened up opportunities to tackle the most pressing challenges in the …

Feature extraction is crucial for human activity recognition (HAR) using body-worn
movement sensors. Recently, learned representations have been used successfully, offering …

Learning time-series representations when only unlabeled data or few labeled samples are
available can be a challenging task. Recently, contrastive self-supervised learning has …

Extracting emotions from physiological signals has become popular over the past decade.
Recent advancements in wearable smart devices have enabled capturing physiological …

Label-efficient time series representation learning, which aims to learn effective
representations with limited labeled data, is crucial for deploying deep learning models in …