Open Tool for Collecting Physiological Data: Collection of Emotional Data During Gameplay

2018 ◽  
pp. 123-131
Author(s):  
Victor Moreira ◽  
Rodrigo Carvalho ◽  
Maria Lúcia Okimoto
Keyword(s):  
Data Collection ◽  
Physiological Data

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.

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

scholarly journals Ambulatory monitoring promises equitable personalized healthcare delivery in underrepresented patients

2021 ◽  
Vol 2 (3) ◽  
pp. 494-510
Author(s):  
Kanchan Kulkarni ◽  
Rahul Kumar Sevakula ◽  
Mohamad B Kassab ◽  
John Nichols ◽  
Jesse D. Roberts ◽  
...  
Keyword(s):  
Rural Communities ◽  
Data Collection ◽  
Underserved Populations ◽  
Healthcare Access ◽  
Healthcare Delivery ◽  
Ambulatory Monitoring ◽  
Physiological Data ◽  
Time Data ◽  
Health Technologies ◽  
Essential Components

Abstract The pandemic has brought to everybody’s attention the apparent need of remote monitoring, highlighting hitherto unseen challenges in healthcare. Today, mobile monitoring and real-time data collection, processing and decision-making, can drastically improve the cardiorespiratory–haemodynamic health diagnosis and care, not only in the rural communities, but urban ones with limited healthcare access as well. Disparities in socioeconomic status and geographic variances resulting in regional inequity in access to healthcare delivery, and significant differences in mortality rates between rural and urban communities have been a growing concern. Evolution of wireless devices and smartphones has initiated a new era in medicine. Mobile health technologies have a promising role in equitable delivery of personalized medicine and are becoming essential components in the delivery of healthcare to patients with limited access to in-hospital services. Yet, the utility of portable health monitoring devices has been suboptimal due to the lack of user-friendly and computationally efficient physiological data collection and analysis platforms. We present a comprehensive review of the current cardiac, pulmonary, and haemodynamic telemonitoring technologies. We also propose a novel low-cost smartphone-based system capable of providing complete cardiorespiratory assessment using a single platform for arrhythmia prediction along with detection of underlying ischaemia and sleep apnoea; we believe this system holds significant potential in aiding the diagnosis and treatment of cardiorespiratory diseases, particularly in underserved populations.

A microcomputer system for physiological data collection and analysis

1993 ◽  
Vol 38 (5) ◽  
pp. 400-405 ◽  
Author(s):  
Peter R. L'Estrange ◽  
Alan R. Blowers ◽  
Robert G. Carlyon ◽  
Stig L. Karlsson
Keyword(s):  
Data Collection ◽  
Microcomputer System ◽  
Physiological Data ◽  
Data Collection And Analysis

Selecting, evaluating, and translating psychophysiological measures into clinical settings: From lab to practice

2018 ◽  
Vol 7 (1) ◽  
pp. 232-236 ◽  
Author(s):  
Lauren Kennedy ◽  
Nathan Lau ◽  
Scott Pappada ◽  
Sarah Henrickson Parker
Keyword(s):  
Data Collection ◽  
Physiological State ◽  
Healthcare Setting ◽  
Sensor Technology ◽  
Physiological Data ◽  
Clinical Settings ◽  
Clinical Workflow ◽  
Healthcare Settings ◽  
Passive Data ◽  
Performance Estimates

Physiological data collection methods are unobtrusive, passive, continuous, and objective. The information afforded by sensors collecting physiological data can be transformed to represent operator performance estimates and stress state visualizations in real time. This technology is conducive to healthcare settings, creating the potential to inform healthcare operators of their current performance and physiological statuses. Despite the broad and pervasive utility of sensor technology, its applications in healthcare are underutilized and misunderstood. This is likely due to the combination of a lack of understanding of the full capabilities of sensor technology, a scarcity of demonstrated uses in healthcare, and an uncertainty surrounding translation and implementation into practice. Implementing findings from providers’ physiological data can be met with challenges, especially in the healthcare setting. Clinicians are most frequently concerned with patient care, and may not always recognize the importance of their own physiological state. While transitioning sensor technology from personal monitoring purposes to a data collection tool can be challenging, passive data collection via sensor technology may have significant value for learners and experienced practitioners. The goals of this paper are to: 1. raise awareness of sensor technology and its utility in clinical settings; 2. provide empirical examples of how to use sensor technology to answer basic and applied questions pertaining to clinical workflow; and 3. exemplify scalability and translatability of findings from sensor technology studies in clinical settings.

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