Vital Sign Fusion

Another model that has been effective in capturing both long and short term trends is temporal fusion transformer TFT. In previous works, both methods have been used for vital sign forecasting in cardiovascular diseases and sepsis patients. However, the feasibility of implementing these approaches is limited by the fact that a separate model

In this work, we extend the framework temporal fusion transformer TFT, a multi-horizon time series prediction tool, and propose TFT-multi, an end-to-end framework that can predict multiple vital trajectories simultaneously. We apply TFT-multi to forecast 5 vital signs recorded in the intensive care unit blood pressure, pulse, SpO2

The proposed device to measure stress levels and vital signs based on sensor fusion is able to measure a person's mental health stress levels and physical health vital signs simultaneously. This tool can be connected to a mobile application via the Internet to facilitate health monitoring, and measurement data are stored in a cloud

Remote physiology, which involves monitoring vital signs without the need for physical contact, has great potential for various applications. Current remote physiology methods rely only on a single camera or radio frequency RF sensor to capture the microscopic signatures from vital movements.

In this work, we extend the framework temporal fusion transformer TFT, a multi-horizon time series prediction tool, and propose TFT-multi, a global model that can predict multiple vital trajectories simultaneously. We apply TFT-multi to forecast 5 vital signs recorded in the intensive care unit blood pressure, pulse, SpO2, temperature and

To attract researchers from different fields to contribute to multimodal fusion research on contactless vital sign detection, this paper provides a comprehensive overview of advancements in vision and RF modalities, as well as recent multi-sensory approaches. We summarize measurement principles, compare single-modality and multimodal systems

Remote physiology, which involves monitoring vital signs without the need for physical contact, has great potential for various applications. Current remote physiology methods rely only on a single camera or radio frequency RF sensor to capture the microscopic signatures from vital movements. However, our study shows that fusing deep RGB and RF features from both sensor streams can further

In this work, we extend the framework temporal fusion transformer TFT, a multi-horizon time series prediction tool, and propose TFT-multi, a global model that can predict multiple vital

Respiratory rate RR is an important vital sign indicating various pathological conditions, such as clinical deterioration, pneumonia, and adverse cardiac arrest. Traditional RR measurement methods are normally intrusive and inconvenient for ubiquitous continuous monitoring. There have been studies on RR estimation by extracting respiratory modulated components RMCs from wearable accessible

Contact-free vital sign monitoring, which uses wireless signals for recognizing human vital signs i.e, breath and heartbeat, is an attractive solution to health and security. However, the subject's body movement and the change in actual environments can result in inaccurate frequency estimation of heartbeat and respiratory. In this paper, we propose a robust mmWave radar and camera fusion