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

The Extent and Coverage of Current Knowledge of Connected Health: Systematic Mapping Study

Background: This paper examines the development of the Connected Health research landscape with a view on providing a historical perspective on existing Connected Health research. Connected Health has become a rapidly growing research field as our healthcare system is facing pressured to become more proactive and patient centred. Objective: We aimed to identify the extent and coverage of the current body of knowledge in Connected Health. With this, we want to identify which topics have drawn the attention of Connected health researchers, and if there are gaps or interdisciplinary opportunities for further research. Methods: We used a systematic mapping study that combines scientific contributions from research on medicine, business, computer science and engineering. We analyse the papers with seven classification criteria, publication source, publication year, research types, empirical types, contribution types research topic and the condition studied in the paper. Results: Altogether, our search resulted in 208 papers which were analysed by a multidisciplinary group of researchers. Our results indicate a slow start for Connected Health research but a more recent steady upswing since 2013. The majority of papers proposed healthcare solutions (37%) or evaluated Connected Health approaches (23%). Case studies (28%) and experiments (26%) were the most popular forms of scientific validation employed. Diabetes, cancer, multiple sclerosis, and heart conditions are among the most prevalent conditions studied. Conclusions: We conclude that Connected Health research seems to be an established field of research, which has been growing strongly during the last five years. There seems to be more focus on technology driven research with a strong contribution from medicine, but business aspects of Connected health are not as much studied

ICT Based Diabetes Management System with Comprehensive Mobile Application: Clinical Usefulness Evaluation

Objective of the presented study is to introduce telemedicine system that integrates all of the most implemented features that appear in different mobile diabetes applications in addition to blood glucose: tracking of insulin or other medications, communication, diet management, physical activity, weight, blood pressure, personal health record, education, social media, and alerts. Presented telemedicine system employs these features for management of Type 1 and Type 2 diabetes patients. Clinical evaluation after 3 months of intervention showed change of HbA1c in both patient groups was not only statistical significant but it had also a clinical dimension. The HbA1c decreased by 0,5% represents significant change for reduction of diabetic complications. Baseline for HbA1c in 29 T1DM patients: 8.1±1.5% versus 7.5±1.2% (P = 0.008). Baseline for HbA1c in 26 T2DM patients: 7.4±1.2% versus 6.8±1.1% (p=0.001). Results show statistically significant tendency to decrease in T2DM patients in weight 93.2±13.6kg compared to 91.9±14.2kg (P=0.08); and BMI 30.7±4.1 kg/m2 compared to 30.2±4.2 kg/m2 (P=0.08)