scholarly journals Using Wearable Devices for Non-invasive, Inexpensive Physiological Data Collection

2017 ◽  
Author(s):  
James Gaskin ◽  
Jeffrey Jenkins ◽  
Thomas Meservy ◽  
Jacob Steffen ◽  
Katherine Payne
Keyword(s):  
Data Collection ◽  
Wearable Devices ◽  
Physiological Data ◽  
Non Invasive

scholarly journals Detecting Pathogen Exposure During the Non-symptomatic Incubation Period Using Physiological Data: Proof of Concept in Non-human Primates

2021 ◽  
Vol 12 ◽  
Author(s):  
Shakti Davis ◽  
Lauren Milechin ◽  
Tejash Patel ◽  
Mark Hernandez ◽  
Greg Ciccarelli ◽  
...  
Keyword(s):  
Random Forest ◽  
Early Warning ◽  
Clinical Symptoms ◽  
Characteristic Curve ◽  
Wearable Devices ◽  
Contact Tracing ◽  
Physiological Data ◽  
Non Invasive ◽  
Pathogen Exposure ◽  
Human Primate

Background and Objectives: Early warning of bacterial and viral infection, prior to the development of overt clinical symptoms, allows not only for improved patient care and outcomes but also enables faster implementation of public health measures (patient isolation and contact tracing). Our primary objectives in this effort are 3-fold. First, we seek to determine the upper limits of early warning detection through physiological measurements. Second, we investigate whether the detected physiological response is specific to the pathogen. Third, we explore the feasibility of extending early warning detection with wearable devices.Research Methods: For the first objective, we developed a supervised random forest algorithm to detect pathogen exposure in the asymptomatic period prior to overt symptoms (fever). We used high-resolution physiological telemetry data (aortic blood pressure, intrathoracic pressure, electrocardiograms, and core temperature) from non-human primate animal models exposed to two viral pathogens: Ebola and Marburg (N = 20). Second, to determine reusability across different pathogens, we evaluated our algorithm against three independent physiological datasets from non-human primate models (N = 13) exposed to three different pathogens: Lassa and Nipah viruses and Y. pestis. For the third objective, we evaluated performance degradation when the algorithm was restricted to features derived from electrocardiogram (ECG) waveforms to emulate data from a non-invasive wearable device.Results: First, our cross-validated random forest classifier provides a mean early warning of 51 ± 12 h, with an area under the receiver-operating characteristic curve (AUC) of 0.93 ± 0.01. Second, our algorithm achieved comparable performance when applied to datasets from different pathogen exposures – a mean early warning of 51 ± 14 h and AUC of 0.95 ± 0.01. Last, with a degraded feature set derived solely from ECG, we observed minimal degradation – a mean early warning of 46 ± 14 h and AUC of 0.91 ± 0.001.Conclusion: Under controlled experimental conditions, physiological measurements can provide over 2 days of early warning with high AUC. Deviations in physiological signals following exposure to a pathogen are due to the underlying host’s immunological response and are not specific to the pathogen. Pre-symptomatic detection is strong even when features are limited to ECG-derivatives, suggesting that this approach may translate to non-invasive wearable devices.

scholarly journals Using wearable devices and mobile surveys for child and youth development in Malawi: An implementation study of high-frequency data collection based in multi-modal approach. (Preprint)

2020 ◽  
Author(s):  
Onicio Leal Neto ◽  
Simon Hanni ◽  
John Phuka ◽  
Laura Ozella ◽  
Daniela Paolotti ◽  
...  
Keyword(s):  
Child Development ◽  
Data Collection ◽  
High Frequency ◽  
Wearable Devices ◽  
Implementation Study ◽  
Modal Approach ◽  
Face To Face ◽  
Non Invasive ◽  
Contact Patterns ◽  
Ecg Data

BACKGROUND Multi-modal approaches have been shown to be a promising way to collect data on child development at high frequency, combining different data inputs – from phone surveys to signals from non-invasive bio-markers – to understand children’s health and development outcomes more integrally, from multiple perspectives. OBJECTIVE The objective of this work is to describe an implementation study using a multi-modal approach combining non-invasive biomarkers, social contact patterns, mobile surveying and face-to-face interviews in order to validate technologies that help us better understand child development in poor countries at high frequency. METHODS We carried out a mixed study based on a transversal descriptive analysis and a longitudinal prospective analysis in Malawi. In each village, children were sampled to participate in weekly sessions in which data signals were collecting through wearable devices (ECG hand pads and EEG headbands). Additionally, wearable proximity sensors to elicit the social network were deployed in children and their caregivers. Mobile surveys using Interactive Voice Response calls were also used as an additional layer of data collection. An end line face-to-face survey was conducted at the end of the study. RESULTS During the implementation, 82 EEG/ECG data entry points were collected across the four villages. The sampled children for EEG/ECG were 0-5 years old. EEG/ECG data were collected one a week. In every session, children worn the EEG headband for 5 minutes, and the ECG hand pad for 3 minutes. In total, 3,531 calls were sent over 5 weeks. 2,291 participants picked up the calls, and 984 of those answered the consent question. In total, 585 people completed the surveys over the course of the 5 weeks. CONCLUSIONS The present study achieved its objectives in demonstrating the feasibility of generating data through an unprecedented use of a multi-modal approach for tracking child development in Malawi, one of the poorest countries in the world. Above and beyond its multiple dimensions, the dynamics of child development are complex: not only it is the case that no data stream in isolation can accurately characterize it, but also that, even if combined, infrequent data might miss critical inflection points and interactions between different conditions and behaviors. In turn, combining different modes, and at sufficiently high frequency, allows researchers to make progress by considering contact patterns, reported symptoms and behaviors and critical biomarkers all at once. This application showcases that even in developed countries facing multiple constraints, complementary technologies can leverage and accelerate the digitalization of health, bringing benefits to populations that lack new tools to understanding, mainly, of child well-being and development. CLINICALTRIAL

A Portable, Inexpensive, Wireless Vital Signs Monitoring System

2010 ◽  
Vol 44 (4) ◽  
pp. 350-353 ◽  
Author(s):  
David Kaputa ◽  
David Price ◽  
John D. Enderle
Keyword(s):  
Data Collection ◽  
Vital Signs ◽  
Physiological Data ◽  
Storage Device ◽  
Medical Facility ◽  
Monitoring Device ◽  
Non Invasive ◽  
Vital Signs Monitoring ◽  
Electronic Storage ◽  
The University

Abstract The University of Connecticut, Department of Biomedical Engineering has developed a device to be used by patients to collect physiological data outside of a medical facility. This device facilitates modes of data collection that would be expensive, inconvenient, or impossible to obtain by traditional means within the medical facility. Data can be collected on specific days, at specific times, during specific activities, or while traveling. The device uses biosensors to obtain information such as pulse oximetry (SpO2), heart rate, electrocardiogram (ECG), non-invasive blood pressure (NIBP), and weight which are sent via Bluetooth to an interactive monitoring device. The data can then be downloaded to an electronic storage device or transmitted to a company server, physician's office, or hospital. The data collection software is usable on any computer device with Bluetooth capability, thereby removing the need for special hardware for the monitoring device and reducing the total cost of the system. The modular biosensors can be added or removed as needed without changing the monitoring device software. The user is prompted by easy-to-follow instructions written in non-technical language. Additional features, such as screens with large buttons and large text, allow for use by those with limited vision or limited motor skills.

scholarly journals Recent Advances in Wearable Devices for Non-Invasive Sensing

2021 ◽  
Vol 11 (3) ◽  
pp. 1235
Author(s):  
Su Min Yun ◽  
Moohyun Kim ◽  
Yong Won Kwon ◽  
Hyobeom Kim ◽  
Mi Jung Kim ◽  
...  
Keyword(s):  
Health Monitoring ◽  
Data Transmission ◽  
Wearable Sensors ◽  
Wearable Devices ◽  
Wireless Power ◽  
Non Invasive ◽  
Sensing Systems ◽  
Recent Advances ◽  
Wearable Systems ◽  
Precise Diagnosis

The development of wearable sensors is aimed at enabling continuous real-time health monitoring, which leads to timely and precise diagnosis anytime and anywhere. Unlike conventional wearable sensors that are somewhat bulky, rigid, and planar, research for next-generation wearable sensors has been focused on establishing fully-wearable systems. To attain such excellent wearability while providing accurate and reliable measurements, fabrication strategies should include (1) proper choices of materials and structural designs, (2) constructing efficient wireless power and data transmission systems, and (3) developing highly-integrated sensing systems. Herein, we discuss recent advances in wearable devices for non-invasive sensing, with focuses on materials design, nano/microfabrication, sensors, wireless technologies, and the integration of those.

Mapping the urban environment using real-time physiological monitoring

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dimitra Dritsa ◽  
Nimish Biloria
Keyword(s):  
Data Collection ◽  
Conceptual Model ◽  
Urban Environment ◽  
Physiological Responses ◽  
Physiological Data ◽  
Contextual Data ◽  
Content Type ◽  
The Relationship ◽  
The City ◽  
Urban Stress

PurposeThis paper presents a critical review of studies which map the urban environment using continuous physiological data collection. A conceptual model is consequently presented for mitigating urban stress at the city and the user level.Design/methodology/approachThe study reviews relevant publications, examining the tools used for data collection and the methods used for data analysis and data fusion. The relationship between urban features and physiological responses is also examined.FindingsThe review showed that the continuous monitoring of physiological data in the urban environment can be used for location-aware stress detection and urban emotion mapping. The combination of physiological and contextual data helps researchers understand how the urban environment affects the human body. The review indicated a relationship between some urban features (green, land use, traffic, isovist parameters) and physiological responses, though more research is needed to solidify the existence of the identified links. The review also identified many theoretical, methodological and practical issues which hinder further research in this area.Originality/valueWhile there is large potential in this field, there has been no review of studies which map continuously physiological data in the urban environment. This study covers this gap and introduces a novel conceptual model for mitigating urban stress.

BrainStorm: a psychosocial game suite design for non-invasive cross-generational cognitive capabilities data collection

2017 ◽  
Vol 29 (6) ◽  
pp. 1311-1323
Author(s):  
Faizan Ahmad ◽  
Yiqiang Chen ◽  
Lisha Hu ◽  
Shuangquan Wang ◽  
Jindong Wang ◽  
...  
Keyword(s):  
Data Collection ◽  
Non Invasive

scholarly journals A Physiological-Monitoring Electronic Platform for Cattle Grazing Systems

2020 ◽  
Vol 11 (3) ◽  
pp. 1-12
Author(s):  
Ricardo R. Santos ◽  
Fabiana V. Alves ◽  
Patrik O. Bressan ◽  
Ricardo E. Aguiar ◽  
Wellington O. Santos ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Skin Temperature ◽  
Mobile Device ◽  
Electronic Device ◽  
Cattle Grazing ◽  
Physiological Data ◽  
Physiological Monitoring ◽  
Physiological Variables ◽  
Non Invasive ◽  
Grazing Systems

In this work, we present a non-invasive electronic platform for physiological data acquisition on cattle grazing systems. The platform can be used for dairy and beef cattle to continuously monitor physiological variables such as skin temperature, heartbeats, and respiratory frequency. The set of sensors are coupled into a halter so that they are in touch with the animal's forehead. Users can monitor the data acquired by the electronic device using a mobile device (smartphone or tablet) and it visualizes important physiological parameters in the platform cloud system.

Real-Time Physiological Data Collection and Analysis in Animal Inhalation Models: Predictive and Diagnostic Implications

2019 ◽  
pp. 791-802
Author(s):  
Benjamin Wong ◽  
Bryan. J. McCranor ◽  
Lewandowski Lewowski ◽  
Alfred. M. Sciuto
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Physiological Data ◽  
Data Collection And Analysis
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