Related papers: BERT: Accelerating Vital Signs Measurement for Bio…
Received signal strength based respiration rate monitoring is emerging as an alternative non-contact technology. These systems make use of the radio measurements of short-range commodity wireless devices, which vary due to the inhalation…
Screening and surveillance are routinely used in medicine for early detection of disease and close monitoring of progression. Biomarkers are one of the primarily tools used for these tasks, but their successful translation to clinical…
This dissertation advances the state of the art for AR/VR tracking systems by increasing the tracking frequency by orders of magnitude and proposes an efficient algorithm for the problem of edge-aware optimization. AR/VR is a natural way of…
The successful application of large pre-trained models such as BERT in natural language processing has attracted more attention from researchers. Since the BERT typically acts as an end-to-end black box, classification systems based on it…
Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations and longer…
Voice, as input, has progressively become popular on mobiles and seems to transcend almost entirely text input. Through voice, the voice search (VS) system can provide a more natural way to meet user's information needs. However, errors…
This work introduces a new approach to the Epileptic Spasms (ESES) detection based on the EEG signals using Vision Transformers (ViT). Classic ESES detection approaches have usually been performed with manual processing or conventional…
Cancer is one of the leading health challenges for women, specifically breast and ovarian cancer. Early detection can help improve the survival rate through timely intervention and treatment. Traditional methods of detecting cancer involve…
Respiratory rate (RR) is a vital sign with significant diagnostic value. Existing RR monitors often suffer from baseline drift over time, breaths can be occluded by limb or body movements, and many systems struggle to resolve shallow or…
Signal-based early detection of illnesses has been a key topic in research and hospital settings; it reduces technological costs and paves the way for quick and effective patient-care operations. Elementary machine learning and signal…
The extraction of labels from radiology text reports enables large-scale training of medical imaging models. Existing approaches to report labeling typically rely either on sophisticated feature engineering based on medical domain knowledge…
The bidirectional encoder representations from transformers (BERT) model has recently advanced the state-of-the-art in passage re-ranking. In this paper, we analyze the results produced by a fine-tuned BERT model to better understand the…
Deviations in respiratory rate often precede abnormalities in other vital signs. However, continuously monitoring respiratory rates outside clinical settings remains challenging due to the obtrusive nature and sensitivity to body motions in…
Biomedical signals provide insights into various conditions affecting the human body. Beyond diagnostic capabilities, these signals offer a deeper understanding of how specific organs respond to an individual's emotions and feelings. For…
Real-time vital sign monitoring is gaining immense significance not only in the medical field but also in personal health management. Facing the needs of different application scenarios of the smart and healthy city in the future, the…
In recent years, BERT has made significant breakthroughs on many natural language processing tasks and attracted great attentions. Despite its accuracy gains, the BERT model generally involves a huge number of parameters and needs to be…
Self-report measures (e.g., Likert scales) are widely used to evaluate subjective health perceptions. Recently, the visual analog scale (VAS), a slider-based scale, has become popular owing to its ability to precisely and easily assess how…
Vision-based target motion estimation is a fundamental problem in many robotic tasks. The existing methods have the limitation of low observability and, hence, face challenges in tracking highly maneuverable targets. Motivated by the aerial…
Reaction time (RT) is a fundamental measure in cognitive and neurophysiological assessment, yet most existing RT systems require active user engagement and controlled environments, limiting their use in real-world settings. This paper…
This research presents a novel method for noninvasive arterial blood pressure ABP prediction using speech signals employing a BERT based regression model Arterial blood pressure is a vital indicator of cardiovascular health and accurate…