A5074627930- Heart Rate Variability and Autonomic Control
- Non-Invasive Vital Sign Monitoring
- Neural dynamics and brain function
- Neuroscience and Neural Engineering
- Diabetes Management and Research
- Digital Mental Health Interventions
- Mobile Health and mHealth Applications
- ECG Monitoring and Analysis
- EEG and Brain-Computer Interfaces
- Diabetes and associated disorders
- Neural Networks and Applications
- Mechanical Circulatory Support Devices
- Optical Imaging and Spectroscopy Techniques
- Pancreatic function and diabetes
- Analog and Mixed-Signal Circuit Design
- Cardiac Arrest and Resuscitation
- Cardiac Structural Anomalies and Repair
- Blind Source Separation Techniques
- Topic Modeling
- Cardiovascular and exercise physiology
- Behavioral Health and Interventions
- Human Pose and Action Recognition
- Advanced Sensor and Energy Harvesting Materials
- Generative Adversarial Networks and Image Synthesis
- Advanced Chemical Sensor Technologies
Duke University
2018-2022
North Carolina State University
2015-2016
As wearable technologies are being increasingly used for clinical research and healthcare, it is critical to understand their accuracy determine how measurement errors may affect conclusions impact healthcare decision-making. Accuracy of has been a hotly debated topic in both the popular science literature. Currently, technology companies responsible assessing reporting products, but little information about evaluation method made publicly available. Heart rate measurements from wearables...
We present our efforts toward enabling a wearable sensor system that allows for the correlation of individual environmental exposures with physiologic and subsequent adverse health responses. This will permit better understanding impact increased ozone levels other pollutants on chronic asthma conditions. discuss inefficiency existing commercial off-the-shelf components to achieve continuous monitoring system-level nano-enabled improving wearability power consumption. Our consists wristband,...
A scalable neural interface technology projected to last at least 6 years in the body samples over a thousand brain sites using flexible electronics.
Abstract Introduction: Digital health is rapidly expanding due to surging healthcare costs, deteriorating outcomes, and the growing prevalence accessibility of mobile (mHealth) wearable technology. Data from Biometric Monitoring Technologies (BioMeTs), including mHealth wearables, can be transformed into digital biomarkers that act as indicators outcomes used diagnose monitor a number chronic diseases conditions. There are many challenges faced by biomarker development, lack regulatory...
Abstract Prediabetes affects one in three people and has a 10% annual conversion rate to type 2 diabetes without lifestyle or medical interventions. Management of glycemic health is essential prevent progression diabetes. However, there currently no commercially-available noninvasive method for monitoring aid self-management prediabetes. There critical need innovative, practical strategies improve management health. In this study, using dataset 25,000 simultaneous interstitial glucose...
Currently, there are no presymptomatic screening methods to identify individuals infected with a respiratory virus prevent disease spread and predict their trajectory for resource allocation.To evaluate the feasibility of using noninvasive, wrist-worn wearable biometric monitoring sensors detect viral infection after exposure severity in patients exposed H1N1 influenza or human rhinovirus.The cohort challenge study was conducted during 2018; data were collected from September 11, 2017, May...
The clinical use of microsignals recorded over broad cortical regions is largely limited by the chronic reliability implanted interfaces.
The dynamic time warping (DTW) algorithm is widely used in pattern matching and sequence alignment tasks, including speech recognition series clustering. However, DTW algorithms perform poorly when aligning sequences of uneven sampling frequencies. This makes it difficult to apply practical problems, such as signals that are recorded simultaneously by sensors with different, uneven, As multi-modal sensing technologies become increasingly popular, necessary develop methods for high quality...
Objective.Brain functions such as perception, motor control, learning, and memory arise from the coordinated activity of neuronal assemblies distributed across multiple brain regions. While major progress has been made in understanding function individual neurons, circuit interactions remain poorly understood. A fundamental obstacle to deciphering is limited availability research tools observe manipulate large, populations humans. Here we describe development, validation, dissemination...
Digital health technologies, such as smartphones and wearable devices, promise to revolutionize disease prevention, detection, treatment. Recently, there has been a surge of digital studies where data are collected through bring-your-own-device (BYOD) approach, in which participants who already own specific technology may voluntarily sign up for the study provide their data. BYOD design accelerates collection from larger number than cohort design; this is possible because researchers not...
Introduction Diabetes prevalence continues to grow and there remains a significant diagnostic gap in one-third of the US population that has pre-diabetes. Innovative, practical strategies improve monitoring glycemic health are desperately needed. In this proof-of-concept study, we explore relationship between non-invasive wearables metrics demonstrate feasibility using estimate metrics, including hemoglobin A1c (HbA1c) glucose variability metrics. Research design methods We recorded over 25...
We present a wearable sensor system consisting of wristband and chest patch to enable the correlation individual environmental exposure health response for understanding impacts ozone on chronic asthma conditions. The wrist worn device measures ambient concentration, heart rate via plethysmography (PPG), three-axis acceleration, temperature, relative humidity. electrocardiography (ECG) PPG, respiratory wheezing microphone, acceleration. data from each is continually streamed peripheral...
Background The Pfizer-BioNTech COVID-19 vaccine uses a novel messenger RNA technology to elicit protective immune response. Short-term physiologic responses the have not been studied using wearable devices. Objective We aim characterize changes in response vaccination small cohort of participants device (WHOOP Strap 3.0). This is proof concept for consumer-grade devices monitor vaccines. Methods In this prospective observational study, data from 19 internal medicine residents at single...
Goal: Continuous glucose monitoring (CGM) is commonly used in Type 1 diabetes management by clinicians and patients research to understand how factors of longitudinal variability relate disease onset severity the efficacy interventions. CGM data presents unique bioinformatic challenges because longitudinal, temporal, there are infinite ways summarize use this data. There over 25 metrics clinically research, not standardized, little validation exists across studies. The primary goal work...
Injury rates in student athletes are high and often unpredictable. risk factors not agreed upon validated. Here, we present a random-forest machine learning methodology for identifying the most significant injury develop model of lower extremity musculoskeletal with physical performance metrics spanning joint strength measured force transducers, postural stability using plate, flexibility, goniometer, combined previous athlete demographics. We tested our population 122 system achieved an...
Objective. Large channel count surface-based electrophysiology arrays (e.g. µECoG) are high-throughput neural interfaces with good chronic stability. Electrode spacing remains ad hoc due to redundancy and nonstationarity of field dynamics. Here, we establish a criterion for electrode based on the expected accuracy predicting unsampled potential from sampled sites.Approach. We applied spatial covariance modeling prediction techniques geospatial kriging quantify sufficient sampling thousands...
Personalized medicine has exposed wearable sensors as new sources of biomedical data which are expected to accrue annual storage costs approximately $7.2 trillion by 2020 (>2000 exabytes). To improve the usability devices in healthcare, it is necessary determine minimum amount needed for accurate health assessment.Here, we present a generalizable optimization framework determining sampling rate and apply our method optimal optical blood volume pulse rate. We implement t-tests, Bland-Altman...
This is a response to the Matters Arising (MA) that examines our original article, 'Investigating inaccuracies in wearable optical heart rate sensors' 1 .We performed this study address concern there was inadequate published research on potential effect of skin tone device accuracy.The central hypothesis tested darker tones have decreased photoplethysmography-based measurement accuracy as compared with lighter tones.The MA suggests improvements surrounding two aspects study: sample size and...
Recently, companies such as Apple Inc, Fitbit and Garmin Ltd have released new wearable blood oxygenation measurement technologies. Although the release of these technologies has great potential for generating health-related information, it is important to acknowledge repercussions consumer-targeted biometric monitoring (BioMeTs), which in practice, are often used medical decision making. BioMeTs bodily connected digital medicine products that process data captured by mobile sensors use...
Background The field of digital medicine has seen rapid growth over the past decade. With this unfettered growth, challenges surrounding interoperability have emerged as a critical barrier to translating into practice. In order understand how mitigate in research and practice, community must landscape professionals, which tools are being used how, user perspectives on current medicine. Objective primary objective study is provide information that working establish frameworks best practices...
A Premature Ventricular Contraction (PVC) is an irregular heartbeat caused by the spontaneous firing of Purkinje fibers when Sinoatrial node fails to establish normal pacing heart. PVCs can impair cardiac function and lead greater risk cardiovascular disease. are usually monitored with a Holter monitor or event monitor, which bulky be uncomfortable for user. This paper outlines design, development, testing wearable chest band that captures wearer's ECG signal wirelessly transmits information...