The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other ‘things’ ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers’ heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries.

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An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments

Background: The emergence of smartphones and tablets featuring vastly advancing functionalities (eg, sensors, computing power, interactivity) has transformed the way mHealth interventions support chronic disease management for older adults. Baby boomers have begun to widely adopt smart devices and have expressed their desire to incorporate technologies into their chronic care. Although smart devices are actively used in research, little is known about the extent, characteristics, and range of smart device-based interventions. Objective: We conducted a scoping review to (1) understand the nature, extent, and range of smart device-based research activities, (2) identify the limitations of the current research and knowledge gap, and (3) recommend future research directions. Methods: We used the Arksey and O’Malley framework to conduct a scoping review. We identified relevant studies from MEDLINE, Embase, CINAHL, and Web of Science databases using search terms related to mobile health, chronic disease, and older adults. Selected studies used smart devices, sampled older adults, and were published in 2010 or after. The exclusion criteria were sole reliance on text messaging (short message service, SMS) or interactive voice response, validation of an electronic version of a questionnaire, postoperative monitoring, and evaluation of usability. We reviewed references. We charted quantitative data and analyzed qualitative studies using thematic synthesis. To collate and summarize the data, we used the chronic care model. Results: A total of 51 articles met the eligibility criteria. Research activity increased steeply in 2014 (17/51, 33%) and preexperimental design predominated (16/50, 32%). Diabetes (16/46, 35%) and heart failure management (9/46, 20%) were most frequently studied. We identified diversity and heterogeneity in the collection of biometrics and patient-reported outcome measures within and between chronic diseases. Across studies, we found 8 self-management supporting strategies and 4 distinct communication channels for supporting the decision-making process. In particular, self-monitoring (38/40, 95%), automated feedback (15/40, 38%), and patient education (13/40, 38%) were commonly used as self-management support strategies. Of the 23 studies that implemented decision support strategies, clinical decision making was delegated to patients in 10 studies (43%). The impact on patient outcomes was consistent with studies that used cellular phones. Patients with heart failure and asthma reported improved quality of life. Qualitative analysis yielded 2 themes of facilitating technology adoption for older adults and 3 themes of barriers. Conclusions: Limitations of current research included a lack of gerontological focus, dominance of preexperimental design, narrow research scope, inadequate support for participants, and insufficient evidence for clinical outcome. Recommendations for future research include generating evidence for smart device-based programs, using patient-generated data for advanced data mining techniques, validating patient decision support systems, and expanding mHealth practice through innovative technologies. [JMIR Mhealth Uhealth 2017;5(5):e69]

Smart Devices for Older Adults Managing Chronic Disease: A Scoping Review

In this paper, a novel algorithm based on a convolutional neural network (CNN) is proposed for myocardial infarction detection via multilead electrocardiogram (ECG). A beat segmentation algorithm utilizing multilead ECG is designed to obtain multilead beats, and fuzzy information granulation is adopted for preprocessing. Then, the beats are input into our multilead-CNN (ML-CNN), a novel model that includes sub two-dimensional (2-D) convolutional layers and lead asymmetric pooling (LAP) layers. As different leads represent various angles of the same heart, LAP can capture multiscale features of different leads, exploiting the individual characteristics of each lead. In addition, sub 2-D convolution can utilize the holistic characters of all the leads. It uses 1-D kernels shared among the different leads to generate local optimal features. These strategies make the ML-CNN suitable for multilead ECG processing. To evaluate our algorithm, actual ECG datasets from the PTB diagnostic database are used. The sensitivity of our algorithm is 95.40%, the specificity is 97.37%, and the accuracy is 96.00% in the experiments. Targeting lightweight mobile healthcare applications, real-time analyses are performed on both MATLAB and ARM Cortex-A9 platforms. The average processing times for each heartbeat are approximately 17.10 and 26.75 ms, respectively, which indicate that this method has good potential for mobile healthcare applications.

Real-Time Multilead Convolutional Neural Network for Myocardial Infarction Detection

Received: 23 September 2019 Accepted: 2 December 2019 Internet of Things (IoT) based smart health monitoring system is a patient monitoring system in which a patient can be monitored 24 hours. In the present world, IoT is changing the infrastructure of technologies. By facilitating effortless interaction among various modules, IoT has enabled us to implement various complex systems such as smart home appliances, smart traffic control systems, smart office systems, smart environment, smart vehicles and smart temperature control systems and so on in very little space. Health monitoring systems are one of the most notable applications of IoT. Many types of designs and patterns have already been implemented to monitor a patient’s health condition through IoT. In this paper, a review of IoT based smart health monitoring systems is presented. The latest innovative technologies developed for IoT based smart health monitoring system with their merits and demerits have been discussed. This review aims to highlight the common design and implementation patterns of intelligent IoT based smart health monitoring devices for patients.

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Developing IoT Based Smart Health Monitoring Systems: A Review

A low-power biosignal acquisition and classification system for body sensor networks is proposed. The proposed system consists of three main parts: 1) a high-pass sigma delta modulator-based biosignal processor (BSP) for signal acquisition and digitization, 2) a low-power, super-regenerative on–off keying transceiver for short-range wireless transmission, and 3) a digital signal processor (DSP) for electrocardiogram (ECG) classification. The BSP and transmitter circuits, which are the body-end circuits, can be operated for over 80 days using two 605 mAH zinc–air batteries as the power supply; the power consumption is 586.5 μW. As for the radio frequency receiver and DSP, which are the receiving-end circuits that can be integrated in smartphones or personal computers, power consumption is less than 1 mW. With a wavelet transform-based digital signal processing circuit and a diagnosis control by cardiologists, the accuracy of beat detection and ECG classification are close to 99.44% and 97.25%, respectively. All chips are fabricated in TSMC 0.18-μm standard CMOS process.

Low-Power Wireless ECG Acquisition and Classification System for Body Sensor Networks

Recently, cardiovascular disease (CVD) has become one of the leading death causes worldwide, and it contributes to 41% of all deaths each year in China. This disease incurs a cost of more than 400 billion US dollars in China on the healthcare expenditures and lost productivity during the past ten years. It has been shown that the CVD can be effectively prevented by an interdisciplinary approach that leverages the technology development in both IT and electrocardiogram (ECG) fields. In this paper, we present WE-CARE , an intelligent telecardiology system using mobile 7-lead ECG devices. Because of its improved mobility result from wearable and mobile ECG devices, the WE-CARE system has a wider variety of applications than existing resting ECG systems that reside in hospitals. Meanwhile, it meets the requirement of dynamic ECG systems for mobile users in terms of the detection accuracy and latency. We carried out clinical trials by deploying the WE-CARE systems at Peking University Hospital. The clinical results clearly showed that our solution achieves a high detection rate of over 95% against common types of anomalies in ECG, while it only incurs a small detection latency around one second, both of which meet the criteria of real-time medical diagnosis. As demonstrated by the clinical results, the WE-CARE system is a useful and efficient mHealth (mobile health) tool for the cardiovascular disease diagnosis and treatment in medical platforms.

WE-CARE: An Intelligent Mobile Telecardiology System to Enable mHealth Applications

With the rapid development of wireless communications and sensor technologies, multi-lead electrocardiogram (ECG) monitoring systems can be implemented for real-time Cardiovascular Disease (CVD) tracking and prevention services by using mobile terminals. To meet this objective, we designed a 7-lead ECG monitoring system enabled by smartphones, which is a combination of user mobility requirement and clinical intelligent function. In the system, an application-layer protocol is conceived and tested for guaranteeing data transmission reliability between smartphones and portable sensors. In addition, the smartphone in the system can be customized as a personal health manager, which can control system function modes and device states, and also perform information management and deeper data analysis. Most significantly, we developed a health risk alarm algorithm to detect ECG signal abnormities, which could help professionals pick out the data with key clinical information. To test our system performance and validity, we carried out simulation tests and system experiments. The results show our system is helpful in CVD prevention services.

Design and tests of a smartphones-based multi-lead ECG monitoring system

ECG monitoring in daily life is an important means of treating heart disease. To make it easier for the medical to monitor the ECG of their patients outside the hospital, we designed and developed an ECG monitoring and alarming system based on Android smart phone. In our system, an ECG device collects the ECG signal and transmits it to an Android phone. The Android phone detects alarms which come from the ECG devices. When alarms occur, Android phone will capture the ECG images and the details about the alarms, and sends them to the cloud Alarm Server (AS). Once received, AS push the messages to doctors’ phone, so the doctors could see the ECG images and alarm details on their mobile phone. In our system, high resolution ECG pictures are transmitted to doctors’ phone in a user-friendly way, which can help doctors keep track of their patient’s condition easily.

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An ECG Monitoring and Alarming System Based On Android Smart Phone

The invention discloses a mobile monitoring method and system based on a mobile network. The method comprises the following steps: 1) setting an information acquisition module and a bluetooth module in portable equipment, and installing a service monitoring module on a mobile terminal; 2) registering equipment information of each portable equipment and user information of each portable equipment to a server; 3) running the mobile terminal of the service monitoring module to search equipment around capable of being in wireless connection at regular time and obtaining the equipment information of the searched equipment and sending the equipment information to the server; 4) the server carrying out verification on each received equipment information and feeding the verification results back to the mobile terminal, and if the verification passes, the mobile terminal establishing communication connection with the corresponding portable equipment and sending the information and the user information collected by the portable equipment to the server; and 5)a user logging in the server and searching the acquisition information of corresponding user. The mobile monitoring method and system based on the mobile network can greatly improve the connection capability and data transmission capability of the wearable equipment.

Mobile monitoring method and system based on mobile network

Cardiovascular disease (CVD), principally heart disease and stroke, has become the worldwide leading killer for people of all racial and ethnic groups. In China, this disease contributes 41% to all deaths each year, and it costs the country more than 400 billion US dollars in last decade, including health expenditures and lost productivity, which continues to grow as the population size increases. Recent research findings indicate that CVD can be effectively prevented to some extent by an interdisciplinary approach that combines ICT (Information and Communication Technology) and healthcare applications. Based on this motivation, we developed WE-CARE, a Wearable Efficient teleCARdiology systEm using mobile 7-lead ECG devices. Our WE-CARE system surpasses existing resting ECG systems that reside in hospitals owing to its improved mobility brought by wearable and mobile ECG devices. Meanwhile, it outperforms those conventional dynamic 1-lead or 3-lead ECG systems for mobile users in terms of the appropriate ECG information required for clinical proposes. We carried out clinical trials by deploying the WE-CARE systems at People Hospital of Peking University. The results showed that our solution achieves a high detection rate over 95% against common types of anomalies in ECG signals, while its risk alert delay is limited around one second, both of which match the criteria of real-time healthcare monitoring very well. As demonstrated by the results, the WE-CARE system is a useful and efficient tool for the cardiovascular disease prevention and daily monitoring.

Hongqiao Gao

Papers

WE-CARE: An Intelligent Mobile Telecardiology System to Enable mHealth Applications

Design and tests of a smartphones-based multi-lead ECG monitoring system

An ECG Monitoring and Alarming System Based On Android Smart Phone

Mobile monitoring method and system based on mobile network

WE-CARE: A wearable efficient telecardiology system using mobile 7-lead ECG devices