Current Challenges and Barriers to the Wider Adoption of Wearable Sensor Applications and Internet-of-Things in Health and Well-being

The aim of this review is to investigate barriers and challenges of Wearable Sensors (WS) and Internet-of-Things (IoT) solutions in healthcare. This work specifically focuses on falls and Activity of Daily Life (ADLs) for ageing population and independent living for older adults. The majority of the studies focussed on the system aspects of WS and IoT solutions including advanced sensors, wireless data collection, communication platforms and usability. The current studies are focused on a single use-case/health area using non-scalable and ‘silo’ solutions. Moderate to low usability/ userfriendly approach is reported in most of the current studies. Other issues found were, inaccurate sensors, battery/power issues, restricting the users within the monitoring area/space and lack of interoperability. The advancement of wearable technology and possibilities of using advanced technology to support ageing population is a concept that has been investigated by many studies. We believe, WS and IoT monitoring plays a critical role towards support of a world-wide goal of tackling ageing population and efficient independent living. Consequently, in this study we focus on identifying three main challenges regarding data collection and processing, techniques for risk assessment, usability and acceptability of WS and IoT in wider healthcare settings

Laser-Induced Graphene for Heartbeat Monitoring with HeartPy Analysis

The HeartPy Python toolkit for analysis of noisy signals from heart rate measurements is an excellent tool to use in conjunction with novel wearable sensors. Nevertheless, most of the work to date has focused on applying the toolkit to data measured with commercially available sensors. We demonstrate the application of the HeartPy functions to data obtained with a novel graphene-based heartbeat sensor. We produce the sensor by laser-inducing graphene on a flexible polyimide substrate. Both graphene on the polyimide substrate and graphene transferred onto a PDMS substrate show piezoresistive behavior that can be utilized to measure human heartbeat by registering median cubital vein motion during blood pumping. We process electrical resistance data from the graphene sensor using HeartPy and demonstrate extraction of several heartbeat parameters, in agreement with measurements taken with independent reference sensors. We compare the quality of the heartbeat signal from graphene on different substrates, demonstrating that in all cases the device yields results consistent with reference sensors. Our work is a first demonstration of successful application of HeartPy to analysis of data from a sensor in development

Wearable Patch for Mass Casualty Screening with Graphene Sensors

Wearable sensors are reaching maturity, at the same time as technologies for communicating physiological data and those for analyzing massive amounts of data. The combination of the three technologies invites for applications in mass screening of personal health through smart algorithm deployment on data from wearable patches. We propose and present an architecture for a wearable patch to be used in mass casualty emergency situations, or for hospital bedside monitoring. The proposed patch will contain multiple sensors of physiological parameters. We propose to create respiration and heartbeat sensors made of laser induced graphene. We show that graphene on flexible substrates can be utilized in conjunction with the Python heart rate analysis toolkit - HeartPy to reliably acquire physiological data from human subject

Wright State University\u27s Celebration of Research, Scholarship and Creative Activities Book of Abstracts from Friday, April 21, 2017

The student abstract booklet is a compilation of abstracts from students\u27 oral and poster presentations at Wright State University\u27s Annual Celebration of Research, Scholarship and Creative Activities on April 21, 2017.https://corescholar.libraries.wright.edu/urop_celebration/1024/thumbnail.jp

A Critical Review of Consumer Wearables, Mobile Applications, and Equipment for Providing Biofeedback, Monitoring Stress, and Sleep in Physically Active Populations

The commercial market for technologies to monitor and improve personal health and sports performance is ever expanding. A wide range of smart watches, bands, garments, and patches with embedded sensors, small portable devices and mobile applications now exist to record and provide users with feedback on many different physical performance variables. These variables include cardiorespiratory function, movement patterns, sweat analysis, tissue oxygenation, sleep, emotional state, and changes in cognitive function following concussion. In this review, we have summarized the features and evaluated the characteristics of a cross-section of technologies for health and sports performance according to what the technology is claimed to do, whether it has been validated and is reliable, and if it is suitable for general consumer use. Consumers who are choosing new technology should consider whether it (1) produces desirable (or non-desirable) outcomes, (2) has been developed based on real-world need, and (3) has been tested and proven effective in applied studies in different settings. Among the technologies included in this review, more than half have not been validated through independent research. Only 5% of the technologies have been formally validated. Around 10% of technologies have been developed for and used in research. The value of such technologies for consumer use is debatable, however, because they may require extra time to set up and interpret the data they produce. Looking to the future, the rapidly expanding market of health and sports performance technology has much to offer consumers. To create a competitive advantage, companies producing health and performance technologies should consult with consumers to identify real-world need, and invest in research to prove the effectiveness of their products. To get the best value, consumers should carefully select such products, not only based on their personal needs, but also according to the strength of supporting evidence and effectiveness of the products

mHealth Engineering: A Technology Review

In this paper, we review the technological bases of mobile health (mHealth). First, we derive a component-based mHealth architecture prototype from an Institute of Electrical and Electronics Engineers (IEEE)-based multistage research and filter process. Second, we analyze medical databases with regard to these prototypic mhealth system components.. We show the current state of research literature concerning portable devices with standard and additional equipment, data transmission technology, interface, operating systems and software embedment, internal and external memory, and power-supply issues. We also focus on synergy effects by combining different mHealth technologies (e.g., BT-LE combined with RFID link technology). Finally, we also make suggestions for future improvements in mHealth technology (e.g., data-protection issues, energy supply, data processing and storage)

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Gyrocardiography: A New Non-invasive Monitoring Method for the Assessment of Cardiac Mechanics and the Estimation of Hemodynamic Variables

Gyrocardiography (GCG) is a new non-invasive technique for assessing heart motions by using a sensor of angular motion - gyroscope - attached to the skin of the chest. In this study, we conducted simultaneous recordings of electrocardiography (ECG), GCG, and echocardiography in a group of subjects consisting of nine healthy volunteer men. Annotation of underlying fiducial points in GCG is presented and compared to opening and closing points of heart valves measured by a pulse wave Doppler. Comparison between GCG and synchronized tissue Doppler imaging (TDI) data shows that the GCG signal is also capable of providing temporal information on the systolic and early diastolic peak velocities of the myocardium. Furthermore, time intervals from the ECG Q-wave to the maximum of the integrated GCG (angular displacement) signal and maximal myocardial strain curves obtained by 3D speckle tracking are correlated. We see GCG as a promising mechanical cardiac monitoring tool that enables quantification of beat-by-beat dynamics of systolic time intervals (STI) related to hemodynamic variables and myocardial contractility

Automated Detection of Atrial Fibrillation Based on Time-Frequency Analysis of Seismocardiograms

In this paper, a novel method to detect atrial fibrillation from a seismocardiogram (SCG) is presented. The proposed method is based on linear classification of the spectral entropy and a heart rate variability index computed from the SCG. The performance of the developed algorithm is demonstrated on data gathered from 13 patients in clinical setting. After motion artefact removal, in total 119 minutes of AFib data and 126 minutes of sinus rhythm data were considered for automated atrial fibrillation detection. No other arrhythmias were considered in this study. The proposed algorithm requires no direct heartbeat peak detection from the SCG data, which makes it tolerant against interpersonal variations in the SCG morphology, and noise. Furthermore, the proposed method relies solely on SCG and needs no complementary electrocardiography (ECG) to be functional. For the considered data, the detection method performs well even on relatively low quality SCG signals. Using a majority voting scheme which takes 5 randomly selected segments from a signal and classifies these segments using the proposed algorithm, we obtained an average true positive rate of 99.9% and an average true negative rate of 96.4% for detecting atrial fibrillation in leave-one-out cross-validation. The presented work facilitates adoption of MEMS-based heart monitoring devices for arrhythmia detection.</p