Motif Discovery consists of finding repeated patterns and locating their occurrences in a time
series without prior knowledge about their shape or location. Most state-of-the-art algorithms …

This paper proposes a three-computing-layer architecture consisting of Edge, Fog, and
Cloud for remote health vital signs monitoring. The novelty of this architecture is in using the …

As monitoring and diagnostic tools for long COVID-19 cases, wearable systems and
supervised learning-based medical image analysis have proven to be useful. Current …

Aligning physiological parameter labels with large-scale photoplethysmographic (PPG) data
for deep learning is challenging and resource-intensive. While self-supervised …

Photoplethysmography (PPG) signals, typically acquired from wearable devices, hold
significant potential for continuous fitness-health monitoring. In particular, heart conditions …

By generating synthetic biosignals, the quantity and variety of health data can be increased.
This is especially useful when training machine learning models by enabling data …

We have been witnessing an unprecedented increase in the aging population in human
history. It is nontrivial to ensure the health and safety of seniors living alone. The prohibitive …

Finding repeating or anomalous subsequences in long time series is a crucial task in
numerous data analysis pipelines. Most of those methods share a common step where they …

The core premise of cancelable biometrics lies in the creation of a distinct biometric template
for every individual, which can be either canceled or regenerated as needed. This process …

Heartbeat serves as a vital sign of health, aiding the diagnosis of various health issues. The
autonomic nervous system (ANS) is responsible for regulating heartbeat, and physical …