Connected Health Applications Research Articles

Automated Fabrication of Smart Strain Sensing Threads.

With favorable properties of stretchability, stitchability, and potential to be woven into a fabric, thread-based sensors have gained considerable interest for wearable devices for smart and connected health applications. To facilitate wearable applications, an easy and reliable way to fabricate these thread-based sensors with good performance and consistency is the key while manufacturing these smart threads. In this paper, we propose an automated thread-coating system that can fabricate thread-based strain sensors with controlled parameters. The platform uses integrated sensors for controlled manufacturing of the threads in a highly compact structure that consists of an innovative tension sensor and a closed-loop thermal management system. Using this new system, a sample thread with a gauge factor of 1.47 and tension sensitivity of 32.64 KΩ/N is prepared. Compared with hand-coated thread, the machine-fabricated thread shows much better sensitivity and consistency. The prepared strain sensor is made into a respiration sensor patch and a limb motion patch to demonstrate its application.

Cortisol detection methods for stress monitoring in connected health

Everyday responsibilities and lifestyle issues are the main cause of physical and psychological stress, which deteriorates the individual's health. Prolonged exposure to stress triggers the adrenocorticotrophic hormonal (ACTH) system and causes the release of cortisol hormones from the adrenal cortex. Many other biomarkers are affected by stress, but cortisol is considered the most vital and potentially clinically useful biomarker for stress estimation and monitoring. Accurate and timely detection of increased cortisol levels might improve the diagnosis, treatment, and prevention of stress-related diseases such as anxiety disorders, metabolic dysregulation, and cardiovascular diseases. Unfortunately, most of the cortisol assessments are currently performed only in laboratories and there is no point-of-care solution for ambulatory/real-time cortisol assessment. This review aims to provide an overview of the most promising techniques, currently used for cortisol detection and the challenges associated with them. The review also provides a feasibility report about measuring cortisol levels in different bio-fluids (for example, urine), a correlation of perceived stress with cortisol levels, and methods/devices used in the laboratory as well as in the ambulatory environment for cortisol detection. The overall conclusion suggests that significant research efforts and investments are required for the development of an accurate, rapid, and repeatable cortisol measuring device that can be used for connected health applications.

A secure, private, and explainable IoHT framework to support sustainable health monitoring in a smart city

Internet of Health Things (IoHT) have allowed connected health paradigm ubiquitous. 5 G supported healthcare vertical allows IoHT to offer connected health monitoring with quality of service and ultra-low latency. Deep learning has shown potential in processing massive amount of IoHT data that are generated daily, automate connected healthcare workflows, and help in decision making processes. However, three important challenges need to be addressed to attain long term healthcare-related sustainability – data security, data privacy, and social acceptance of deep learning process. In this paper, we propose a framework that will allow healthcare sustainability through the following contributions 1) ensure privacy of training dataset, 2) support the aggregation of the global model gradients through a private Blockchain-brokered entity, 3) support trustworthiness and provenance of the federated clients by blockchain and off-chain, 4) share the dataset, train the model and share trained model among the federated clients in an encrypted fashion, and 5) add explainability and reasoning of deep learning process to make the model acceptable by the society. We will present the detailed design of our proposed sustainable system, the implementation details and test results. The test results show promising prospect of achieving sustainability of IoHT-enabled connected health applications.

Remote Monitoring of Patients with COVID-19 after Hospital Discharge with Connected Health Platform: Outcomes and Quality of Life

Background: Telemonitoring with applications and connected devices facilitates a more accessible and efficient attention. Its implementation has been accelerated thanks to the pandemic by COVID-19, where they have allowed the continuity of care. Objective: To evaluate the efficacy of a remote monitoring platform for the outpatient follow-up after hospital discharge by COVID-19. Methods: Prospective observational study of patients discharged from the hospital with COVID-19 infection between March 23 and May 25, 2020, who were followed for one month with the Connected Health telemonitoring platform. The mobile phone application connected to a pulse oximeter, allowed to measure vital signs and answer health questionnaires (EQ5D3L and CAT) daily, and alert the medical team that could be contacted by video consultation. Results: 95 patients (64% male) with a mean age of 54 (SD 26-81) years were included. The application allowed the detection of alerts for pain (80% of patients) and a decrease in oxygen saturation (12%). No patient required hospital readmission or presented complications. The application allowed strict monitoring of symptoms and quality of life. The main symptom was severe pain (59% of patients) followed by anxiety or depression (25%). The average state of health was 65 (SD 20-100). COVID-19 caused a low impact on the quality of life of 62% of the patients, although 8% reported a significant limitation, due to shortness of breath and leaving the house. Conclusion: telemonitoring allows a safe remote monitoring of patients after hospital discharge by COVID-19. The Connected Health application has allowed the measurement of oxygen saturation, symptoms and quality of life, and the detection and management of alerts by the medical team through video consultation.

Efficacy of Remote Monitoring of Overweight Patients with Connected Health Platform

Background: Mobile technology has emerged as a potentially useful platform to facilitate weight management and tackle the current obesity epidemic. Objective: To evaluate the efficacy of a remote monitoring platform for overweight patients. Methods: Multicenter prospective observational study of overweight patients seen in an endocrinology clinic of 5 healthcare centers, between August 2017 and August 2020, who were followed with the Connected Health telemonitoring platform. The mobile phone application connected to a scale and activity wristband, allowed to measure weight, activity, answer health questionnaire and its management by the medical team that could be contacted by video consultation. Results: 79 patients (57% female) with a mean age of 46,8 (SD 23-70) years were included with a mean follow-up of 12.9 months (SD 4-36). The application allowed to measure weight, activity and quality of life and no patient presented complications. The mean weight of all patients at the beginning of the intervention was 84.1 kg and at the end of the follow-up it was 82.4 kg, with a mean reduction of 1.7 kg (p<0.05). The average state of health of all patients at baseline was 62 (SD 34-100) and at the end of the follow-up was 66 (SD 38-100) (p<0.05). Conclusion: Telemonitoring allows a safe remote monitoring of overweight patients. The Connected Health application allowed the measurement of weight, activity and quality of life, and was associated with significant changes in body weight.

An Electroanalytical Paper-Based Wound Dressing Using ZIF-67/C3 N4 Nanocomposite Towards the Monitoring of Staphylococcus Aureus in Diabetic Foot Ulcer

Slow or non-healing wounds in chronic diabetic foot diseases are often associated with co-morbid circumstances, inevitably leading to lower limb amputations. The wound bed in chronic foot ulcers are prone to bacterial infections, with Staphylococcus aureus ( S. aureus ) acting as the major culprit in resisting the normal wound healing process. This study reports the development of a flexible screen-printed wound dressing based on a composite of Zeolitic Imidazolate Framework (ZIF 67) and carbon nitride (C3 N4) conjugated with S. aureus probe DNA for the detection of its corresponding target DNA. The nano-wound dressing was assessed using Electrochemical Impedance Spectroscopy (EIS) for the quantification of S. aureus target DNA within 1 fM to $10~\mu \text{M}$ . A direct proportionality between the charge transfer resistance ( $\text{R}_{\text {ct}}$ ) and S. aureus target DNA concentration. Circuit simulations detailing the quantifications of interfacial parameters, involved in the recognition event, were performed by curve fitting of Nyquist spectra in order to explain the variation of $\text{R}_{\text {ct}}$ with target DNA concentrations. The Limit of Detection (LoD), sensitivity and response time of the developed wound dressing-based sensor were found to be 0.46 fM, 0.25 $\text{k}\Omega $ /fM/mm2 and ~ 10 s respectively at 21.04 kHz, while being selective only towards S. aureus target DNA. The sensor was further validated in human serum samples and was shown to possess a shelf life of 35 days, thereby exhibiting tremendous potential to be implemented in connected health applications.

Electroanalytical Sensor for Diabetic Foot Ulcer Monitoring with Integrated Electronics for Connected Health Application

AbstractL‐tyrosine is an amino acid, the concentration of which is found to be highly elevated in patients suffering from diabetic foot ulcer (DFU). The latter proves to be fatal when it turns out chronic and may lead to amputation. The conventional clinical diagnostic methods are costly and time consuming, in which case, the condition of patient(s) may deteriorate long before proper treatment commences. Herein, we report the development of smart band‐aid for real time monitoring of L‐tyrosine by employing enzymatic bio‐sensor using α‐MnO2/tyrosinase. The smart band‐aid was further integrated with portable electronics capable of wireless data transmission to a personal digital assistant, and its tyrosine sensing performance was evaluated. Anodic current was found to vary linearly with the concentrations of L‐tyrosine in the range of 5 nM–500 μM. The developed sensor displayed a limit of detection and sensitivity of 0.71 nM and 0.67 μA/nM/mm2 respectively, with a stability of 25 days. The developed sensor was validated using a commercial impedance analyzer. The impedance response was found to be consistent with the cyclic voltammogram obtained and demonstrated to be a linear function of tyrosine concentration. The developed sensing platform combines early diagnosis with connected health technologies, thus, fitting well into modern healthcare needs.

Efficiency of Connected Health Telemonitoring Platform for Patients with High Blood Pressure

Background: Chronic diseases are expected to double their current incidence in people over 65, before by 2030. Cardiovascular diseases, such as ischemic heart disease and heart failure, are the most common cause of morbidity and mortality in developed countries and high blood pressure is the most prevalent risk factor. Telemedicine collects data, shares information and provides clinical care, education, public health and remote administrative services. Telehealth and specially connected health platforms, have the potential to improve the health care system, reduce costs and increase patient satisfaction. Methods: During 2018, 111 patients with high blood pressure (81 (73%) men and 30 (27%) women with an average age 58,4 years) were selected and followed for 12 months. All patients included in the telemonitoring program, received the Connected Health application on their smartphone and a bluetooth device to measure blood pressure. The application reinforces education about the disease, treatment compliance, blood pressure monitoring and in case of decompensation, generates alarms that trigger a response by the medical team with video consultation. Results: To analyse the difference in blood pressure (systolic and diastolic), we calculate the average of the first (when patients added to the program) and last 30 measurements after the 12-month follow-up. We observed a reduction of 5,7 mmHg (4%) in systolic and 4 mmHg (5%) in diastolic blood pressure (p <0.05). The use of the connected health platform avoided 3,2% of costs (Blood Pressure Holter and outpatient visits) in one year. The patient satisfaction analysed with the Net Promoting Score of the services implicated increased 2.2 points during the study period. Conclusion: A telemonitoring platform for patients with high blood pressure, based on health education, treatment compliance, blood pressure monitoring and early detection of decompensation, improves blood pressure figures, reduce costs and increase patient satisfaction.

Digital health solutions implemented by Sanitas hospitals to maintain continuity of care during COVID-19 pandemic

Introduction: Digital health facilitates patient-centered, accessible, safe, and more efficient care, through technologies such as telemedicine, big data, bots, artificial intelligence, and other technologies. Undoubtedly, its implementation has been accelerated thanks to the COVID-19 pandemic, where they have demonstrated their effectiveness, by maintaining continuity of care and facilitating early interventions thanks to the analysis of data and the deployment of bots, telemonitoring and virtual care platforms. Objective and methods: Prospective observational study to describe the digital health solutions implemented by Sanitas hospitals, a health insurance company with around 2 million costumers, 5 teaching hospitals and many outpatient health care facilities throughout Spain, to maintain continuity of care during COVID-19 pandemic. We outline the results of using the Sanitas telemedicine platform (video consultations and Connected Health application) and chatbot. Results: During the first 2 months of the COVID-19 outbreak, we have experienced an exponential increase in the number of video consultations, coming from an average of 300 a day before the COVID-19 crisis to around 5000 a day, going from 27.058 virtual visits made during 2019, to 114.598 in the first 5 months of 2020. The Connected Health mobile phone application allowed to remote monitoring 95 patients after hospital discharge for COVID-19 infection, measuring vital signs with a connected pulse oximeter, answer health questionnaires daily, and alert the medical team who received alerts for pain from 80% of patients and a decrease in oxygen saturation in 12% of cases. Bots has also helped to fight the COVID-19 crisis, making information available by providing the best answer to patients whenever they want it 24/7. Our bot SanIA has experienced 16.858 consultations about COVID-19 during the first 2 months of the outbreak. Conclusions: Digital health, throughout video consultations, telemonitoring platform and bots, has helped to maintain continuity of care during the COVID-19 crisis. The COVID-19 pandemic has brought a sudden change in the adoption of digital health strategies, which will undoubtedly continue in the long term, and has served us, both health staff and the population, to be better prepared for this next digital age.

RadSense: Enabling one hand and no hands interaction for sterile manipulation of medical images using Doppler radar

Abstract In this paper, we show how surgeons can interact with medical images using finger and hand gestures in two situations: one hand-free and no hands-free interaction. We explain how interaction with only one hand or a couple of fingers is beneficial and can help surgeons have continuous interaction, without the need to release their tools and leave the operating table, saving valuable patient time. To this end, we present RadSense, an end-to-end and unobtrusive system that uses Doppler radar-sensing to recognize hand and finger gestures when either one or both hands are busy. Our system permits the following important capabilities: (1) touch-less input for sterile interaction with connected health applications, (2) hand and finger gesture recognition when either one or both hands are busy holding tools, extending multitasking capabilities for health professionals, and (3) mobile and networked, allowing for custom wearable and non-wearable configurations. We evaluated our system in a simulated operating room to manipulate preoperative images using four gestures: circle, double tap, swipe, and finger click. We collected data from five subjects and trained a K-Nearest-Neighbor multi-class classifier using 15-fold cross validation, achieving a 94.5% precision for gesture classification. We conclude that our system performs with high accuracy and is useful in cases where only one hand or a few fingers are free to interact when the hands are busy.

System-on-Chip Solution for Patients Biometric: A Compressive Sensing-Based Approach

The ever-increasing demand for biometric solutions for the Internet-of-Things (IoT)-based connected health applications is mainly driven by the need to tackle fraud issues, along with the imperative to improve patient privacy, safety, and personalized medical assistance. However, the advantages offered by the IoT platforms come with the burden of big data and its associated challenges in terms of computing complexity, bandwidth availability, and power consumption. This paper proposes a solution to tackle both privacy issues and big data transmission by incorporating the theory of compressive sensing and a simple, yet, efficient identification mechanism using the electrocardiogram (ECG) signal as a biometric trait. Moreover, the paper presents the hardware implementation of the proposed solution on a system-on-chip (SoC) platform with an optimized architecture to further reduce the hardware resource usage. First, we investigate the feasibility of compressing the ECG data while maintaining a high identification quality. The obtained results show a 98.88% identification rate using only a compression ratio of 30%. Furthermore, the proposed system has been implemented on a Zynq SoC using heterogeneous software/hardware solution, which is able to accelerate the software implementation by a factor of 7.73 with a power consumption of 2.318 W.

Sustainability requirements for connected health applications

AbstractConnected health applications are efficient tools to improve health care services. The aim of this paper is to define a reusable requirements repository (catalog) for sustainable connected health applications based on the main related software engineering standards, e‐health technology standards, and literature. The catalog contains requirements which cover the individual, social, environmental, and technical dimensions of sustainability. The requirements presented in this paper can be very useful to connected health application stakeholders and can be adapted to specific m‐health applications. Moreover, the requirements catalog can be used to generate a checklist of requirements to evaluate and audit connected health applications. An application example is presented to show that the evaluation of connected health applications using the generated checklist is realistic.

Prevailing Opinions on Connected Health in Austria: Results from an Online Survey.

New technological developments affect almost every sector of our daily lives, including the healthcare sector. We evaluated how connected health applications, subsumed as eHealth and telemedicine, are perceived in relation to socio-demographic characteristics. The current cross-sectional, online survey collected self-reported data from a non-probability convenience sample of 562 Austrian adults (58.9% females). The concept of eHealth and telemedicine was poorly established among the study population. While most participants already used mobile devices, they expressed a quite low desirability of using various telemedicine applications in the future. Study participants perceived that the most important overall benefits for implementing connected health technology were better quality of healthcare, location-independent access to healthcare services, and better quality of life. The respective three top-ranked overall barriers were data security, lack of acceptance by doctors, and lack of technical prerequisites. With regard to aging societies, healthcare providers, and users alike could take advantage of inexpensive, consumer-oriented connected health solutions that address individual needs of specific target groups. The present survey identified issues relevant for successful implementation of ICT-based healthcare solutions, providing a compilation of several areas requiring further in-depth research.

The Use of Ensemble Empirical Mode Decomposition With Canonical Correlation Analysis as a Novel Artifact Removal Technique

Biosignal measurement and processing is increasingly being deployed in ambulatory situations particularly in connected health applications. Such an environment dramatically increases the likelihood of artifacts which can occlude features of interest and reduce the quality of information available in the signal. If multichannel recordings are available for a given signal source, then there are currently a considerable range of methods which can suppress or in some cases remove the distorting effect of such artifacts. There are, however, considerably fewer techniques available if only a single-channel measurement is available and yet single-channel measurements are important where minimal instrumentation complexity is required. This paper describes a novel artifact removal technique for use in such a context. The technique known as ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) is capable of operating on single-channel measurements. The EEMD technique is first used to decompose the single-channel signal into a multidimensional signal. The CCA technique is then employed to isolate the artifact components from the underlying signal using second-order statistics. The new technique is tested against the currently available wavelet denoising and EEMD-ICA techniques using both electroencephalography and functional near-infrared spectroscopy data and is shown to produce significantly improved results.