An overview of recent health care support systems for eEmergency and mHealth applications

Implementation of ISO/IEEE 11073 PHD SpO2 and ECG Device Specializations over Bluetooth HDP following Health Care Profile for Smart Living

Current m-Health scenarios in the smart living era, as the interpretation of the smart city at each person’s level, present several challenges associated with interoperability between different clinical devices and applications. The Continua Health Alliance establishes design guidelines to standardize application communication to guarantee interoperability among medical devices. In this paper, we describe the implementation of two IEEE agents for oxygen saturation level (SpO2) measurements and electrocardiogram (ECG) data acquisition, respectively, and a smartphone IEEE manager for validation. We developed both IEEE agents over the Bluetooth Health Device Profile following the Continua guidelines and they are part of a telemonitoring system. This system was evaluated in a sample composed of 10 volunteers (mean age 29.8 ± 7.1 y/o; 5 females) under supervision of an expert cardiologist. The evaluation consisted of measuring the SpO2 and ECG signal sitting and at rest, before and after exercising for 15 min. Physiological measurements were assessed and compared against commercial devices, and our expert physician did not find any relevant differences in the ECG signal. Additionally, the system was assessed when acquiring and processing different heart rate data to prove that warnings were generated when the heart rate was under/above the thresholds for bradycardia and tachycardia, respectively.

Remote Pulmonary Function Test Monitoring in Cloud Platform via Smartphone Built-in Microphone

With an aging population that continues to grow, health care technology plays an increasingly active role, especially for chronic disease management. In the health care market, cloud platform technology is becoming popular, as both patients and physicians demand cost efficiency, easy access to information, and security. Especially for asthma and chronic obstructive pulmonary disease (COPD) patients, it is recommended that pulmonary function test (PFT) be performed on a daily basis. However, it is difficult for patients to frequently visit a hospital to perform the PFT. In this study, we present an application and cloud platform for remote PFT monitoring that can be directly measured by smartphone microphone with no external devices. In addition, we adopted the IBM Watson Internet-of-Things (IoT) platform for PFT monitoring, using a smartphone's built-in microphone with a high-resolution time-frequency representation. We successfully demonstrated real-time PFT monitoring using the cloud platform. The PFT parameters of FEV1/FVC (%) could be remotely monitored when a subject performed the PFT test. As a pilot study, we tested 13 healthy subjects, and found that the absolute error mean was 4.12 and the standard deviation was 3.45 on all 13 subjects. With the developed applications on the cloud platform, patients can freely measure the PFT parameters without restriction on time and space, and a physician can monitor the patients' status in real time. We hope that the PFT monitoring platform will work as a means for early detection and treatment of patients with pulmonary diseases, especially those having asthma and COPD.

mHealth and big-data integration: promises for healthcare system in India

BACKGROUND

The use of mobile devices in health (mobile health/mHealth) coupled with related technologies promises to transform global health delivery by creating new delivery models that can be integrated with existing health services. These delivery models could facilitate healthcare delivery into rural areas where there is limited access to high-quality access care. Mobile technologies, Internet of Things and 5G connectivity may hold the key to supporting increased velocity, variety and volume of healthcare data.

OBJECTIVE

The purpose of this study is to identify and analyse challenges related to the current status of India's healthcare system-with a specific focus on mHealth and big-data analytics technologies. To address these challenges, a framework is proposed for integrating the generated mHealth big-data and applying the results in India's healthcare.

METHOD

A critical review was conducted using electronic sources between December 2018 and February 2019, limited to English language articles and reports published from 2010 onwards.

MAIN OUTCOME

This paper describes trending relationships in mHealth with big-data as well as the accessibility of national opportunities when specific barriers and constraints are overcome. The paper concentrates on the healthcare delivery problems faced by rural and low-income communities in India to illustrate more general aspects and identify key issues. A model is proposed that utilises generated data from mHealth devices for big-data analysis that could result in providing insights into the India population health status. The insights could be important for public health planning by the government towards reaching the Universal Health Coverage.

CONCLUSION

Biomedical, behavioural and lifestyle data from individuals may enable customised and improved healthcare services to be delivered. The analysis of data from mHealth devices can reveal new knowledge to effectively and efficiently support national healthcare demands in less developed nations, without fully accessible healthcare systems.

Fog computing and IoT based healthcare support service for dengue fever

Purpose

This paper has proposed a Fog architecture-based framework, which classifies dengue patients into uninfected, infected and severely infected using a data set built in 2010. The aim of this proposed framework is to developed a latency-aware system for classifying users into different categories based on their respective symptoms using Internet of Things (IoT) sensors and audio and video files.

Design/methodology/approach

To achieve the aforesaid aim, a smart framework is proposed, which consist of three components, namely, IoT layer, Fog infrastructure and cloud computing. The latency of the system is reduced by using network devices located in the Fog infrastructure. Data generated by IoT layer will first be processed by Fog layer devices which are in closer proximity of the user. Raw data and data generated will later be stored on cloud infrastructure, from where it will be sent to different entities such as user, hospital, doctor and government healthcare agencies.

Findings

Experimental evaluation proved the hypothesis that using the Fog infrastructure can achieve better response time for latency sensitive applications with the least effect on accuracy of the system.

Originality/value

The proposed Fog-based architecture can be used with IoT to directly link it with the Fog layer.

Heart monitoring systems--a review

To diagnose health status of the heart, heart monitoring systems use heart signals produced during each cardiac cycle. Many types of signals are acquired to analyze heart functionality and hence several heart monitoring systems such as phonocardiography, electrocardiography, photoplethysmography and seismocardiography are used in practice. Recently, focus on the at-home monitoring of the heart is increasing for long term monitoring, which minimizes risks associated with the patients diagnosed with cardiovascular diseases. It leads to increasing research interest in portable systems having features such as signal transmission capability, unobtrusiveness, and low power consumption. In this paper we intend to provide a detailed review of recent advancements of such heart monitoring systems. We introduce the heart monitoring system in five modules: (1) body sensors, (2) signal conditioning, (3) analog to digital converter (ADC) and compression, (4) wireless transmission, and (5) analysis and classification. In each module, we provide a brief introduction about the function of the module, recent developments, and their limitation and challenges.

Statistical analysis of heart rate and heart rate variability monitoring through the use of smart phone cameras

Video recordings of finger tips made using a smartphone camera contain a pulsatile component caused by the cardiac pulse equivalent to that present in a photoplethysmographic signal. By performing peak detection on the pulsatile signal it is possible to extract a continuous heart rate signal. We performed direct comparisons between 5-lead electrocardiogram based heart rate variability measurements and those obtained from an iPhone 4s and Motorola Droid derived pulsatile signal to determine the accuracy of heart rate variability measurements obtained from the smart phones. Monitoring was performed in the supine and tilt positions for independent iPhone 4s (2 min recordings, n=9) and Droid (5 min recordings, n=13) experiments, and the following heart rate and heart rate variability parameters were estimated: heart rate, low frequency power, high frequency power, ratio of low to high frequency power, standard deviation of the RR intervals, and root mean square of successive RR-differences. Results demonstrate that accurate heart rate variability parameters can be obtained from smart phone based measurements.

Analysis of time-series correlation between weighted lifestyle data and health data

The time-series data analysis described here is based on the simple idea that the accumulation of the effects of lifestyle events, such as ingestion and exercise, could affect personal health with some delay. The delay may reflect complex bio-reactions such as those of metabolism in a human body. In the analysis, the accumulation of the effects of lifestyle events is represented by a summation of daily lifestyle data whose time-series correlation to variations of health data is examined (healthcare-data-mining). The concept of weighting is introduced for the summation of daily lifestyle data. As a result, it is suggested that the nature of personal health could be represented by a weighting pattern characterized by a small number of parameters.

Parameters characterizing nature of personal health in the correlation between energy expenditure/supply and body-fat

Correlations between energy expenditure/supply and body-fat percentage were studied using personally stored daily time-series data. The weighting patterns for the summation of daily time-series energy expenditure and supply data giving the maximum correlations with the variation of daily body-fat percentage data were obtained. The weighting patterns can be expressed by two parameters whose combination is considered to characterize the nature of personal health. The combination of the parameters for a subject was found to show a significant bias in the frequency distribution, independent of season and aging, for the term of seven years, and the combination of the parameters of 20 other subjects showed a tendency to divide into two types.

Physiological parameter monitoring from optical recordings with a mobile phone

We show that a mobile phone can serve as an accurate monitor for several physiological variables, based on its ability to record and analyze the varying color signals of a fingertip placed in contact with its optical sensor. We confirm the accuracy of measurements of breathing rate, cardiac R-R intervals, and blood oxygen saturation, by comparisons to standard methods for making such measurements (respiration belts, ECGs, and pulse-oximeters, respectively). Measurement of respiratory rate uses a previously reported algorithm developed for use with a pulse-oximeter, based on amplitude and frequency modulation sequences within the light signal. We note that this technology can also be used with recently developed algorithms for detection of atrial fibrillation or blood loss.