Search Results for author:
Found 27 papers, 10 papers with code
LabelAId: Just-in-time AI Interventions for Improving Human Labeling Quality and Domain Knowledge in Crowdsourcing Systems
Crowdsourcing platforms have transformed distributed problem-solving, yet quality control remains a persistent challenge.
HeAR -- Health Acoustic Representations
Health acoustic sounds such as coughs and breaths are known to contain useful health signals with significant potential for monitoring health and disease, yet are underexplored in the medical machine learning community.
Demographic Bias of Expert-Level Vision-Language Foundation Models in Medical Imaging
Such demographic biases present over a wide range of pathologies and demographic attributes.
Towards Accurate Differential Diagnosis with Large Language Models
Comparing the two assisted study arms, the DDx quality score was higher for clinicians assisted by our LLM (top-10 accuracy 51. 7%) compared to clinicians without its assistance (36. 1%) (McNemar's Test: 45. 7, p < 0. 01) and clinicians with search (44. 4%) (4. 75, p = 0. 03).
From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models
Passively collected behavioral health data from ubiquitous sensors holds significant promise to provide mental health professionals insights from patient's daily lives; however, developing analysis tools to use this data in clinical practice requires addressing challenges of generalization across devices and weak or ambiguous correlations between the measured signals and an individual's mental health.
Training Robust Deep Physiological Measurement Models with Synthetic Video-based Data
However, there exists a significant gap between synthetic and real-world data, which hinders the generalization of neural models trained on these synthetic datasets.
Exploring and Characterizing Large Language Models For Embedded System Development and Debugging
We leverage this finding to study how human programmers interact with these tools, and develop an human-AI based software engineering workflow for building embedded systems.
Large Language Models are Few-Shot Health Learners
Large language models (LLMs) can capture rich representations of concepts that are useful for real-world tasks.
BigSmall: Efficient Multi-Task Learning for Disparate Spatial and Temporal Physiological Measurements
As an example, perception occurs at different scales both spatially and temporally, suggesting that the extraction of salient visual information may be made more effective by paying attention to specific features at varying scales.
Motion Matters: Neural Motion Transfer for Better Camera Physiological Measurement
Our findings illustrate the usefulness of motion transfer as a data augmentation technique for improving the generalization of models for camera-based physiological sensing.
EarCough: Enabling Continuous Subject Cough Event Detection on Hearables
Subject cough event detection is the foundation for continuous cough monitoring.
Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images
Modeling the trade-off of privacy preservation and machine recognition performance can guide future privacy-preserving computer vision systems using low-resolution image sensors.
MMPD: Multi-Domain Mobile Video Physiology Dataset
Second, most datasets are relatively small and therefore are limited in diversity, both in appearance (e. g., skin tone), behaviors (e. g., motion) and environment (e. g., lighting conditions).
GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization
We envision our multi-year datasets can support the ML community in developing generalizable longitudinal behavior modeling algorithms.
MMTSA: Multimodal Temporal Segment Attention Network for Efficient Human Activity Recognition
The efficiency evaluation on an edge device showed that MMTSA achieved significantly better accuracy, lower computational load, and lower inference latency than SOTA methods.
rPPG-Toolbox: Deep Remote PPG Toolbox
Camera-based physiological measurement is a fast growing field of computer vision.
Federated Remote Physiological Measurement with Imperfect Data
The growing need for technology that supports remote healthcare is being acutely highlighted by an aging population and the COVID-19 pandemic.
SpiroMask: Measuring Lung Function Using Consumer-Grade Masks
According to the World Health Organisation (WHO), 235 million people suffer from respiratory illnesses and four million people die annually due to air pollution.
MobilePhys: Personalized Mobile Camera-Based Contactless Physiological Sensing
Camera-based contactless photoplethysmography refers to a set of popular techniques for contactless physiological measurement.
Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detection
We then translate the out-of-distribution score into a human interpretable CONFIDENCE SCORE to investigate its effect on the users' interaction with health ML applications.
RGB Camera-based Physiological Sensing: Challenges and Future Directions
Numerous real-world applications have been driven by the recent algorithmic advancement of artificial intelligence (AI).
EfficientPhys: Enabling Simple, Fast and Accurate Camera-Based Vitals Measurement
Camera-based physiological measurement is a growing field with neural models providing state-the-art-performance.
Face Detection
Photoplethysmography (PPG) heart rate estimation
EfficientPhys: Enabling Simple, Fast, and Accurate Camera-Based Vitals Measurement
Camera-based physiological measurement is a growing field with neural models providing state-the-art-performance.
SplitSR: An End-to-End Approach to Super-Resolution on Mobile Devices
In this work, we demonstrate state-of-the-art latency and accuracy for on-device super-resolution using a novel hybrid architecture called SplitSR and a novel lightweight residual block called SplitSRBlock.
MetaPhys: Few-Shot Adaptation for Non-Contact Physiological Measurement
There are large individual differences in physiological processes, making designing personalized health sensing algorithms challenging.
Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement
Telehealth and remote health monitoring have become increasingly important during the SARS-CoV-2 pandemic and it is widely expected that this will have a lasting impact on healthcare practices.
Heterogeneous Bitwidth Binarization in Convolutional Neural Networks
Recent work has shown that fast, compact low-bitwidth neural networks can be surprisingly accurate.
