Technology and Health Care - Volume Pre-press, issue Pre-press
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Technology and Health Care is intended to serve as a forum for the presentation of original articles and technical notes, observing rigorous scientific standards. Furthermore, upon invitation, reviews, tutorials, discussion papers and minisymposia are featured.
The following types of contributions and areas are considered:
1. Original articles:
Technology development in medicine: New concepts, procedures and devices associated with the use of technology in medical research and clinical practice are presented to a readership with a widespread background in engineering and/or medicine.
Significance of medical technology and informatics for healthcare: The appropriateness, efficacy and usefulness deriving from the application of engineering methods, devices and informatics in medicine and with respect to public health are discussed.
2. Technical notes:
Short communications on novel technical developments with relevance for clinical medicine.
3. Reviews and tutorials (upon invitation only):
Tutorial and educational articles for persons with a primarily medical background on principles of engineering with particular significance for biomedical applications and vice versa are presented.
4. Minisymposia (upon invitation only):
Under the leadership of a Special Editor, controversial issues relating to healthcare are highlighted and discussed by various authors.
Abstract: BACKGROUND: Soccer is one of the world’s most successful sports with several players. Quality player’s activity management is a tough job for administrators to consider in the Internet of Things (IoT) platform. Candidates need to predict the position, intensity, and path of the shot to look back on their results and determine the stronger against low shot and blocker capacities. OBJECTIVE: In this paper, the IoT-assisted wearable device for activity prediction (IoT-WAP) model has been proposed for predicting the activity of soccer players. METHOD: The accelerometer built wearable devices formulates the impacts of…multiple target attempts from the prevailing foot activity model that reflect a soccer player’s characteristics. The deep learning technique is developed to predict players’ various actions for identifying multiple targets from the differentiated input data compared to conventional strategies. The Artificial Neural Network determines a football athlete’s total abilities based on football activities like transfer, kick, run, sprint, and dribbling. RESULTS: The experimental results show that the suggested system has been validated from football datasets and enhances the accuracy ratio of 97.63%, a sensitivity ratio of 96.32%, and a specificity ratio of 93.33% to predict soccer players’ various activities.
Abstract: BACKGROUND: Internet of Things (IoT) technology provides a tremendous and structured solution to tackle service deliverance aspects of healthcare in terms of mobile health and remote patient tracking. In medicine observation applications, IoT and cloud computing serves as an assistant in the health sector and plays an incredibly significant role. Health professionals and technicians have built an excellent platform for people with various illnesses, leveraging principles of wearable technology, wireless channels, and other remote devices for low-cost healthcare monitoring. OBJECTIVE: This paper proposed the Fog-IoT-assisted multisensor intelligent monitoring model (FIoT-MIMM) for analyzing the patient’s physical health…condition. METHOD: The proposed system uses a multisensor device for collecting biometric and medical observing data. The main point is to continually generate emergency alerts on mobile phones from the fog system to users. For the precautionary steps and suggestions for patients’ health, a fog layer’s temporal information is used. RESULTS: Experimental findings show that the proposed FIoT-MIMM model has less response time and high accuracy in determining a patient’s condition than other existing methods. Furthermore, decision making based on real-time healthcare information further improves the utility of the suggested model.
Keywords: IoT, cloud computing, fog computing, multi-sensor system. physical health condition, health monitoring system
Abstract: BACKGROUND: Physical health monitoring may take several forms, from individual quality changes to complex health checks carried out by health staff. Present health issues are detected with monitoring, and potential health problems are expected. Wearable sensors provide users with ease in everyday tracking, although many issues must be addressed in such sensor systems. The devices take a long time to obtain the requisite detection and diagnostic expertise and produce false alarms. OBJECTIVE: In this paper, the Internet of Things-assisted Health Condition Monitoring system (IoT-HCMS) has been proposed to track and analyze the patient physical health condition.…METHOD: The proposed IoT-HCMS utilizes the intelligent monitoring model to follow the patient physical health day by day activities and instantaneously generate the health records. The system will indeed support patients in tracking psychological signs to minimize risks to their well-being. RESULTS: The experimental results show that the IoT-HCMS improves accuracy in patient health monitoring and has less response time.
Abstract: BACKGROUND: Recently, wearable technologies have gained attention in diverse applications of the medical platform to guarantee the health and safety of the sportsperson with the assistance of the Internet of things (IoT) device. The IoT device’s topology varies due to the shift in users’ orientation and accessibility, making it impossible to assign resources, and routing strategies have been considered the prominent factor in the current medical research. Further, for sportspersons with sudden cardiac arrests, hospital survival rates are low in which wearable IoT devices play a significant role. OBJECTIVE: In this paper, the energy efficient optimized…heuristic framework (EEOHF) has been proposed and implemented on a wearable device of the sportsperson’s health monitoring system. METHOD: The monitoring system has been designed with cloud assistance to locate the nearest health centers during an emergency. The wearable sensor technologies have been used with an optimized energy-efficient algorithm that helps athletes monitor their health during physical workouts. The monitoring system has fitness tracking devices, in which health information is gathered, and workout logs are tracked using EEOHF. The proposed method is applied to evaluate and track the sportsperson’s fitness based on case study analysis. RESULTS: The simulation results have been analyzed, and the proposed EEOHF achieves a high accuracy ratio of 97.8%, a performance ratio of 95.3%, and less energy consumption of 9.4%, delay of 13.1%, and an average runtime of 98.2% when compared to other existing methods.
Abstract: BACKGROUND: Nowadays, smart healthcare minimizes medical facilities costs, ease staff burden, achieve unified control of materials and records, and enhance patients’ medical experience. Smart healthcare treatments have critical barriers to improving patient outcomes, reducing the regulatory burden, and promoting the transition from volume to benefit. OBJECTIVE: In this paper, the Internet of Things-assisted Intelligent Monitoring Model (IoT-IMM) has been proposed to improve patient health and maintain health records. METHOD: The advanced IoT sensors can monitor patient health and insert into the patients’ bodies. Information collected can be analyzed, aggregated, and mined to predict…diseases at an early stage. For that, an enhanced deep learning network using Bayes theorem (EDLN-BT) benefits to obtain and verify various patient health data in a specific aspect, making it easy to supervise the patient’s activities. RESULTS: The IoT-IMM-based EDLN-BT results show the smart health care monitoring has undergone substantial growth, improving patient satisfaction for the quality of the healthcare services offered in hospitals and many other healthcare facilities. It helps predict health diseases with increased accuracy, prediction rate with minimal residual error delay, and energy consumption.
Abstract: BACKGROUND: In recent years the Internet of Things (IoT) has become a popular technological culture in the physical education system. Though several technologies have grown in the physical education system domain, IoT plays a significant role due to its optimized health information processing framework for students during workouts. OBJECTIVE: In this paper, an advanced dynamic information processing system (ADIPS) has been proposed with IoT assistance to explore the traditional design architecture for physical activity tracking. METHOD: To track and evaluate human physical activity in day-to-day living, a new paradigm has been integrated with…wearable IoT devices for effective information processing during physical workouts. Continuous observation and review of the condition and operations of various students by ADIPS helps to evaluate the sensed information to analyze the health condition of the students. RESULTS: The result of ADIPS has been implemented based on the performance factor correlation with the traditional system.
Abstract: BACKGROUND: The Internet of Things (IoT) has recently become a prevalent technological culture in the sports training system. Although numerous technologies have grown in the sports training system domain, IoT plays a substantial role in its optimized health data processing framework for athletes during workouts. OBJECTIVE: In this paper, a Dynamic data processing system (DDPS) has been suggested with IoT assistance to explore the conventional design architecture for sports training tracking. Method: To track and estimate sportspersons physical activity in day-to-day living, a new paradigm has been combined with wearable IoT devices for efficient data processing…during physical workouts. Uninterrupted observation and review of different sportspersons condition and operations by DDPS helps to assess the sensed data to analyze the sportspersons health condition. Additionally, Deep Neural Network (DNN) has been presented to extract important sports activity features. RESULTS: The numerical results show that the suggested DDPS method enhances the accuracy of 94.3%, an efficiency ratio of 98.2, less delay of 24.6%, error range 28.8%, and energy utilization of 31.2% compared to other existing methods.
Abstract: BACKGROUND: The modern Internet of Things (IoT) makes small devices that can sense, process, interact, connect devices, and other sensors ready to understand the environment. IoT technologies and intelligent health apps have multiplied. The main challenges in the sports environment are playing without injuries and healthily. OBJECTIVE: In this paper the Internet of Things-based Smart Wearable System (IoT-SWS) is introduced for monitoring sports person activity to improve sports person health and performance in a healthy way. METHOD: Wearable systems are commonly used to capture individual sports details on a real-time basis. Collecting data…from wearable devices and IoT technologies can help organizations learn how to optimize in-game strategies, identify opponents’ vulnerabilities, and make smarter draft choices and trading decisions for a sportsperson. RESULTS: The experimental result shows that IoT-SWS achieve the highest accuracy of 98.22% and efficient in predicting the sports person’s health to improve sports person performance reliably.
Keywords: IoT, wearable device, Internet of things-based smart wearable system (IOT-SWS)
Abstract: BACKGROUND: IL-18 is known as an interferon-inducing factor that belongs to the IL-1 family, and is synthesized as an inactive precursor protein. OBJECTIVE: The present study aims to investigate the expression of IL-18, IL-18R, R and IL-18 binding protein (BP) mRNA in various types of human pituitary tumors, such as adrenocorticotropic hormone (ACTH), growth hormone (GH), prolactin (PRL), thyroid stimulating hormone (TSH)-producing adenomas and non-function adenomas. METHODS: Pituitary adenoma tissues were obtained during the surgery of 41 patients: nine patients had ACTH-producing pituitary adenomas, nine patients had GH-producing pituitary adenomas, five patients had TSH-producing pituitary…adenomas, seven patients had PRL-producing pituitary adenomas, and 11 patients had non-functioning adenomas. The mRNA expression levels of IL-18, IL-18BP, IL-18R and IL-18R were quantified using real-time quantitative PCR. RESULTS: The mRNA expression of IL-18 was significantly higher in ACTH-, GH- and PRL-producing adenomas, when compared to non-function tumors. Similarly, a significantly higher mRNA expression of IL-18BP and IL-18R was observed in ACTH-, GH- and PRL-producing adenomas, when compared with non-functional adenomas. In contrast, no upregulation of IL-18R mRNA was observed in any of the pituitary adenomas. CONCLUSIONS: The mRNA levels of IL-18, IL-18BP and IL-18R are significantly elevated in clinical pituitary tumors, such as ACTH-, GH- and PRL-producing adenomas, when compared to non-functional adenomas. These present results suggest the possibility that IL-18 may be involved in the pathogenesis of pituitary adenoma.
Keywords: Interleukin-18, Human pituitary adenoma, Interleukin-18 receptor, gene expression, hormone