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Realization and Technology Acceptance Test of a Wearable Cardiac Health Monitoring and Early Warning System with Multi-Channel MCGs and ECG.

ABSTRACT: In this work, a wearable smart clothing system for cardiac health monitoring with a multi-channel mechanocardiogram (MCG) has been developed to predict the myo-cardiac left ventricular ejection fraction (LVEF) function and to provide early risk warnings to the subjects. In this paper, the realization of the core of this system, i.e., the Cardiac Health Assessment and Monitoring Platform (CHAMP), with respect to its hardware, firmware, and wireless design features, is presented. The feature values from the CHAMP system have been correlated with myo-cardiac functions obtained from actual heart failure (HF) patients. The usability of this MCG-based cardiac health monitoring smart clothing system has also been evaluated with technology acceptance model (TAM) analysis and the results indicate that the subject shows a positive attitude toward using this wearable MCG-based cardiac health monitoring and early warning system.

SUBMITTER: Lin WY

PROVIDER: S-EPMC6210769 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Realization and Technology Acceptance Test of a Wearable Cardiac Health Monitoring and Early Warning System with Multi-Channel MCGs and ECG.

Lin Wen-Yen WY Ke Hong-Lin HL Chou Wen-Cheng WC Chang Po-Cheng PC Tsai Tsai-Hsuan TH Lee Ming-Yih MY

Sensors (Basel, Switzerland) 20181019 10

In this work, a wearable smart clothing system for cardiac health monitoring with a multi-channel mechanocardiogram (MCG) has been developed to predict the myo-cardiac left ventricular ejection fraction (LVEF) function and to provide early risk warnings to the subjects. In this paper, the realization of the core of this system, i.e., the Cardiac Health Assessment and Monitoring Platform (CHAMP), with respect to its hardware, firmware, and wireless design features, is presented. The feature value ...[more]

PMID: 30347695

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Project description:INTRODUCTION:Unobtrusive metrics that can auto-assess performance during clinical procedures are of value. Three approaches to deriving wearable technology-based metrics are explored: (1) eye tracking, (2) psychophysiological measurements [e.g. electrodermal activity (EDA)] and (3) arm and hand movement via accelerometry. We also measure attentional capacity by tasking the operator with an additional task to track an unrelated object during the procedure. METHODS:Two aspects of performance are measured: (1) using eye gaze and psychophysiology metrics and (2) measuring attentional capacity via an additional unrelated task (to monitor a visual stimulus/playing cards). The aim was to identify metrics that can be used to automatically discriminate between levels of performance or at least between novices and experts. The study was conducted using two groups: (1) novice operators and (2) expert operators. Both groups made two attempts at a coronary angiography procedure using a full-physics virtual reality simulator. Participants wore eye tracking glasses and an E4 wearable wristband. Areas of interest were defined to track visual attention on display screens, including: (1) X-ray, (2) vital signs, (3) instruments and (4) the stimulus screen (for measuring attentional capacity). RESULTS:Experts provided greater dwell time (63% vs 42%, p?=?0.03) and fixations (50% vs 34%, p?=?0.04) on display screens. They also provided greater dwell time (11% vs 5%, p?=?0.006) and fixations (9% vs 4%, p?=?0.007) when selecting instruments. The experts' performance for tracking the unrelated object during the visual stimulus task negatively correlated with total errors (r?=?-?0.95, p?=?0.0009). Experts also had a higher standard deviation of EDA (2.52 µS vs 0.89 µS, p?=?0.04). CONCLUSIONS:Eye tracking metrics may help discriminate between a novice and expert operator, by showing that experts maintain greater visual attention on the display screens. In addition, the visual stimulus study shows that an unrelated task can measure attentional capacity. Trial registration This work is registered through clinicaltrials.gov, a service of the U.S. National Health Institute, and is identified by the trial reference: NCT02928796.

Project description:IntroductionDigital biomarkers may act as a tool for early detection of changes in cognition. It is important to understand public perception of technologies focused on monitoring cognition to better guide the design of these tools and inform patients appropriately about the associated risks and benefits. Health care systems may also play a role in the clinical, legal, and financial implications of such technologies.ObjectiveTo evaluate public opinion on the use of passive technology for monitoring cognition.MethodsThis was a one-time, Internet-based survey conducted in English and Spanish.ResultsWithin the English survey distributed in the USA (n = 173), 58.1% of respondents would be highly likely to agree to passive monitoring of cognition via a smartphone application. Thirty-eight percent of those with a higher degree of experience with technology were likely to agree to monitoring versus 20% of those with less experience with technology (p = 0.003). Sixty-two percent of non-health-care professionals were likely to agree to monitoring versus 45% of health-care workers (p = 0.012). There were significant concerns regarding privacy (p < 0.01). We compared the surveys answered in Spanish in Costa Rica via logistic regression (n = 43, total n = 216), adjusting for age, education level, health-care profession, owning a smartphone, experience with technology, and perception of cognitive decline. Costa Rican/Spanish-speaking respondents were 7 times more likely to select a high probability of agreeing to such a technology (p < 0.01). English-speaking respondents from the USA were 5 times more likely to be concerned about the impact on health insurance (p = 0.001) and life insurance (p = 0.01).ConclusionsUnderstanding public perception and ethical implications should guide the design of digital biomarkers for cognition. Privacy and the health-care system in which the participants take part are 2 major factors to be considered. It is the responsibility of researchers to convey the ethical and legal implications of cognition monitoring.

Project description:BackgroundDiabetes mellitus is a metabolic disorder that affects hundreds of millions of people worldwide and causes several million deaths every year. Such a dramatic scenario puts some pressure on administrations, care services, and the scientific community to seek novel solutions that may help control and deal effectively with this condition and its consequences.ObjectiveThis study aims to review the literature on the use of modern mobile and wearable technology for monitoring parameters that condition the development or evolution of diabetes mellitus.MethodsA systematic review of articles published between January 2010 and July 2020 was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Manuscripts were identified through searching the databases Web of Science, Scopus, and PubMed as well as through hand searching. Manuscripts were included if they involved the measurement of diabetes-related parameters such as blood glucose level, performed physical activity, or feet condition via wearable or mobile devices. The quality of the included studies was assessed using the Newcastle-Ottawa Scale.ResultsThe search yielded 1981 articles. A total of 26 publications met the eligibility criteria and were included in the review. Studies predominantly used wearable devices to monitor diabetes-related parameters. The accelerometer was by far the most used sensor, followed by the glucose monitor and heart rate monitor. Most studies applied some type of processing to the collected data, mainly consisting of statistical analysis or machine learning for activity recognition, finding associations among health outcomes, and diagnosing conditions related to diabetes. Few studies have focused on type 2 diabetes, even when this is the most prevalent type and the only preventable one. None of the studies focused on common diabetes complications. Clinical trials were fairly limited or nonexistent in most of the studies, with a common lack of detail about cohorts and case selection, comparability, and outcomes. Explicit endorsement by ethics committees or review boards was missing in most studies. Privacy or security issues were seldom addressed, and even if they were addressed, they were addressed at a rather insufficient level.ConclusionsThe use of mobile and wearable devices for the monitoring of diabetes-related parameters shows early promise. Its development can benefit patients with diabetes, health care professionals, and researchers. However, this field is still in its early stages. Future work must pay special attention to privacy and security issues, the use of new emerging sensor technologies, the combination of mobile and clinical data, and the development of validated clinical trials.

Dataset Information

Realization and Technology Acceptance Test of a Wearable Cardiac Health Monitoring and Early Warning System with Multi-Channel MCGs and ECG.

Publications

Realization and Technology Acceptance Test of a Wearable Cardiac Health Monitoring and Early Warning System with Multi-Channel MCGs and ECG.

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