mHealth Authentication Approach Based 3D Touchscreen and Microphone Sensors for Real-Time Remote Healthcare Monitoring System: Comprehensive Review, Open Issues and Methodological Aspects

Shuwandy, Moceheb Lazam; Zaidan, B. B.; Zaidan, A. A.; Albahri, A. S.; Albahri, O. S.; Alazab, Mamoun

doi:10.1016/j.cosrev.2020.100300

Cited by 40 publications

(10 citation statements)

References 97 publications

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“…i = {1, 2, • • • , N}, and time-varying ratios were calculated using the gain ratios (GRs), which are the ratios of and their log variables logh (i). Proxy and level of developmental mode I device routing logic multiplication are shown in Equations ( 6) and (7).…”

Section: Proposed Intelligent Mobile Health Monitoring Systemmentioning

confidence: 99%

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Real-time tracking and detection of patient conditions in the intelligent m-Health monitoring system

Kang-Won

2022

Front. Public Health

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In order to help patients monitor their personal health in real time, this paper proposes an intelligent mobile health monitoring system and establishes a corresponding health network to track and process patients' physical activity and other health-related factors in real time. Performance was analyzed. The experimental results show that after comparing the accuracy, delay time, error range, efficiency, and energy utilization of Im-HMS and existing UCD systems, it is found that the accuracy of Im-HMS is mostly between 98 and 100%, while the accuracy of UCD is mostly between 98 and 100%. Most of the systems are between 91 and 97%; in terms of delay comparison, the delay of the Im-HMS system is between 18 and 39 ms, which is far lower than the lowest value of the UCD system of 84 ms, and the Im-HMS is significantly better than the existing UCD system; the error range of Im-HMS is mainly between 0.2 and 1.4, while the error range of UCD system is mainly between −2 and 14; and in terms of efficiency and energy utilization, Im-HMS values are higher than those of UCD system. In general, the Im-HMS system proposed in this study is more accurate than UCD system and has lower delay, smaller error, and higher efficiency, and energy utilization is more efficient than UCD system, which is of great significance for mobile health monitoring in practical applications.

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Section: Proposed Intelligent Mobile Health Monitoring Systemmentioning

confidence: 99%

“…The system detects the physical signs of an individual's body and notifies doctors as early as possible in the event of a serious illness. The computer includes wireless chips for data collection, battery energy recovery, and data management levels ( 7 ).…”

Section: Introduction To Physical Activity Monitoringmentioning

confidence: 99%

Real-time tracking and detection of patient conditions in the intelligent m-Health monitoring system

Kang-Won

2022

Front. Public Health

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“…In WSN, the terminal node is a full-function device, which perfectly combines the sensing technology, embedded technology, and wireless communication technology to realize the collection of human physiological parameters. Temperature, heart rate, blood pressure, respiration, and blood oxygen were collected [21]. An end node is a device that specifically performs data collection transmission and cannot transmit messages from other nodes.…”

Section: Terminal Node Designmentioning

confidence: 99%

Real-Time Collection and Analysis of Sports Index Time Series Based on Multimodal Sensor Monitoring

Huang

Zhao

2022

Journal of Sensors

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With the further development of microelectronics technology and sensors, sensors can be widely embedded in mobile phone devices and portable devices. The use of acceleration sensors for human motion monitoring has broad application prospects. Monitoring the daily exercise of the human body is of great significance for formulating scientific exercise and fitness plans and improving physical health. This paper uses the measurement data of multiple types of sensors to propose an index recognition method based on the fusion of multiple types of sensor information. We take the measurement value of a single type of sensor as input and output the index value of the moving part without a strain sensor. The pattern recognition method is used to establish a pattern library, a recognition library, and a measurement library. This article considers noise interference or malfunction of sensor measurements. Aiming at uncertain factors such as the error of the finite element model, a pattern matching method considering the uncertainty is proposed. This article takes aerobics as an example to simulate and analyze the dynamic response of aerobics under wind load. In addition, by simulating the recognition results under different levels of noise interference, the robustness and anti-interference of the pattern matching method are verified.

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“…This type of hospital is managed and operated via Tier 3 [43]. Access to medical care has drastically changed due to the emergence of telemedicine [44][45][46][47][48]. The industry of telemedicine currently shows potential because of the Internet of Things (IoT), which provides enhancements to enable physicians to keep track of their patients at any given time and geographical location [12,[49][50][51][52]).…”

Section: Introductionmentioning

confidence: 99%

Development of IoT-based mhealth framework for various cases of heart disease patients

et al. 2021

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A newly distributed fault-tolerant mHealth framework-based Internet of things (IoT) is proposed in this study to resolve the essential problems of healthcare service provision during the occurrence of frequent failures in a telemedicine architecture. Two models are presented to support the telehealth development of chronic heart disease (CHD) in a distant environment. In model-1, a new local multisensor fusion triage algorithm known as three-level localisation triage (3LLT) is proposed. In 3LLT, a group of heterogeneous sources is applied to triage patients as a clinical process, and the emergency levels inside/ outside the home of a patient with CHD are determined. Failures related to sensor fusion can also be detected. In model-2, a centralised IoT connection towards distributed smart hospitals is employed by mHealth based on two attributes: (1) healthcare service packages and (2) time of arrival of a patient at a hospital. Three decision matrices have been used to overcome several issues on hospital selection based on multi-criteria decision-making by using an analytic hierarchy process. Two datasets are utilised: (1) a clinical CHD dataset, which includes 572 patients for testing model-1, and (2) a nonclinical dataset, which includes hospital healthcare service packages for testing model-2. Consequently, patients with CHD can be triaged into different emergency levels (risk, urgent and sick) with mHealth, and a final decision is made by selecting an appropriate hospital. Results are obtained through the clinical triage of patients, and different scenarios are provided for hospital selection. The verification of statistical results indicates that the proposed mHealth framework is systematically valid. The contribution of the mHealth framework is presented to provide an improved triage process, afford timely services and treatment for CVD patients and minimise the chances of error. These health sectors and policymakers can also recognise the evaluation benefits of smart hospitals by using the presented framework and move forward to fully automated mHealth applications.

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mHealth Authentication Approach Based 3D Touchscreen and Microphone Sensors for Real-Time Remote Healthcare Monitoring System: Comprehensive Review, Open Issues and Methodological Aspects

Cited by 40 publications

References 97 publications

Real-time tracking and detection of patient conditions in the intelligent m-Health monitoring system

Real-time tracking and detection of patient conditions in the intelligent m-Health monitoring system

Real-Time Collection and Analysis of Sports Index Time Series Based on Multimodal Sensor Monitoring

Development of IoT-based mhealth framework for various cases of heart disease patients

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