Intelligent Health Monitoring Research Articles

An Intelligent Health Monitoring System for Buildings Based on IoT Technology

This paper presents the design and development of an intelligent health monitoring system for buildings, utilizing IoT (Internet of Things) technology. In addressing a prevailing issue in current building stress monitoring methods, which often overlooks point cloud data preprocessing, we propose a method that focuses on monitoring vertical stress points in concrete structures and aligning the collected data with finite element analysis models. Subsequently, harnessing the potential of IoT technology, we design a Zigbee-based scheme for collecting environmental data within the building. This scheme incorporates a diverse array of sensors and encompasses the construction of a comprehensive software and hardware system alongside a monitoring platform. The resulting system is adept at real-time monitoring and data collection across various facets of the building, ensuring alignment with actual conditions. It demonstrates significant practical value, offering user-friendly operation, robust stability, and excellent performance in the realm of building monitoring.

Evaluation of the Benefits of an Intelligent Health Monitoring System in the Care of Geriatric Patients

Abstract Collaborating with intelligent health monitoring in the clinical care of elderly patients can enhance understanding of changes in their body functions, thereby improving their quality of life and preventing complications. This paper focuses on the user characteristics and needs of geriatric patients, builds an intelligent health monitoring system based on C/S architecture, and develops a health data collection process for geriatric patients using smart wearables. We use the VMD algorithm to reduce the noise in the physiological signal data of elderly patients, then input it into a time-sequence convolutional network to extract the corresponding ECG signal features. We then combine this data with the LSTM model to classify the ECG signals, enabling us to diagnose the health of elderly patients. We established a continuity of care program based on the intelligent health monitoring system and designed a comparison experiment to evaluate the impact of the application. The VMD algorithm can recognize the insignificant signal peaks between 1.5s and 5.5s, and the health diagnosis model has the highest classification accuracy of 99.24% for ECG beats. In the continuity of care model, the elderly patients’ physiological function score was 68.42±4.76, and their serum ALB level was 35.79±6.72 g/L, which was 17.42% higher than the control group’s level after the intervention. It also helped the elderly patients’ mental health. The intelligent health monitoring system can dynamically acquire the physiological characteristics of elderly patients in real time and generate visualization results to help medical staff customize personalized care plans.

Retracted: An Intelligent Health Monitoring Model Based on Fuzzy Deep Neural Network.

[This retracts the article DOI: 10.1155/2022/4757620.].

Flexible Accelerated-Wound-Healing Antibacterial Hydrogel-Nanofiber Scaffold for Intelligent Wearable Health Monitoring.

Flexible epidermal sensors hold significant potential in personalized healthcare and multifunctional electronic skins. Nonetheless, achieving both robust sensing performance and efficient antibacterial protection, especially in medical paradigms involving electrophysiological signals for wound healing and intelligent health monitoring, remains a substantial challenge. Herein, we introduce a novel flexible accelerated-wound-healing biomaterial based on a hydrogel-nanofiber scaffold (HNFS) via electrostatic spinning and gel cross-linking. We effectively engineer a multifunctional tissue nanoengineered skin scaffold for wound treatment and health monitoring. Key features of HNFS include high tensile strength (24.06 MPa) and elasticity (214.67%), flexibility, biodegradability, and antibacterial properties, enabling assembly into versatile sensors for monitoring human motion and electrophysiological signals. Moreover, in vitro and in vivo experiments demonstrate that HNFS significantly enhances cell proliferation and skin wound healing, provide a comprehensive therapeutic strategy for smart sensing and tissue repair, and guide the development of high-performance "wound healing-health monitoring" bioelectronic skin scaffolds. Therefore, this study provides insights into crafting flexible and repairable skin sensors, holding potential for multifunctional health diagnostics and intelligent medical applications in intelligent wearable health monitoring and next-generation artificial skin fields.

Heart failure patients monitoring using IoT-based remote monitoring system

Intelligent health monitoring systems are becoming more important and popular as technology advances. Nowadays, online services are replacing physical infrastructure in several domains including medical services as well. The COVID-19 pandemic has also changed the way medical services are delivered. Intelligent appliances, smart homes, and smart medical systems are some of the emerging concepts. The Internet of Things (IoT) has changed the way communication occurs alongside data collection sources aided by smart sensors. It also has deployed artificial intelligence (AI) methods for better decision-making provided by efficient data collection, storage, retrieval, and data management. This research employs health monitoring systems for heart patients using IoT and AI-based solutions. Activities of heart patients are monitored and reported using the IoT system. For heart disease prediction, an ensemble model ET-CNN is presented which provides an accuracy score of 0.9524. The investigative data related to this system is very encouraging in real-time reporting and classifying heart patients with great accuracy.

Bioinspired Ultra‐Robust Ionogels Constructed with Soft‐Rigid Confinement Space for Multimodal Monitoring Electronics

AbstractIonogels are compelling materials for flexible hybrid electronics owing to their attractive physical properties and infinite adjustability of chemical structures. However, ionogels must be sufficiently strong to ensure durability, stability, and a wide range of strains in various applications to make electronic systems mechanically compliant. Inspired by the hierarchical structure of multiphase substances in the skin, it is fabricated several transparent (>90%) and ultra‐robust (tensile strength >17 MPa, toughness >40 MJ m−3, and elongation ≈300%) ionogels via in situ polymerization of polymers with the different binding abilities to the ionic liquid by forming soft and rigid confinement space. This strategy can also be applied to other ionic liquids and polymers. Furthermore, the designed ionogel sensors can be used to develop a wearable intelligent health monitoring system capable of monitoring health‐related physiological signals, such as temperature, body tremors, wrist pulse, breathing, and gestures, for predicting and responding to emergencies, which will pave the way for wearable security monitoring technology.

A stretchable all-nanofiber iontronic pressure sensor

Flexible pressure sensors with high stretchability, sensitivity, and stability are undoubtedly urgently required for potential applications in intelligent soft robots, human-machine interaction, health monitoring, and other fields. However, most current flexible pressure sensors are unable to endure large deformation and are prone to performance degradation or even failure during frequent operation due to their multilayered structures. Here, we propose a stretchable all-nanofiber iontronic pressure sensor that is composed of ionic nanofiber membranes used as dielectric layers and liquid metal used as electrodes. This sensor exhibits a high sensitivity of 1.08 kPa-1 over a wide range of 0-300 kPa, with a fast response-relaxation time of about 18/22 ms and excellent stability. The high sensitivity comes from the electric double layer formed at the ionic film/electrode interface, while high stretchability and stability are enabled by in-situ encapsulated all-nanofiber structures. As a proof of concept, a prototype sensor array is integrated into a soft pneumatic gripper, demonstrating its capability of pressure perception and object recognition during the grasping process. Thus, the scheme provides another excellent strategy to fabricate stretchable pressure sensors with superb performance in terms of high stretchability, sensitivity, and stability.

Development and Implementation of an Intelligent Health Monitoring System using IoT and Advanced Machine Learning Techniques

Healthcare practices have a tremendous amount of potential to change as a result of the convergence of IoT technologies with cutting-edge machine learning. This study offers an IoT-connected sensor-based Intelligent Health Monitoring System for real-time patient health assessment. Our system offers continuous health monitoring and early anomaly identification by integrating temperature, blood pressure, and ECG sensors. The Support Vector Machine (SVM) model proves to be a reliable predictor after thorough analysis, obtaining astounding accuracy rates of 94% for specificity, 95% for the F1 score, 92% for recall, and 94% for total accuracy. These outcomes demonstrate how well our system performs when it comes to providing precise and timely health predictions. Healthcare facilities can easily integrate our Intelligent Health Monitoring System as part of the practical application of our research. Real-time sensor data can be used by doctors to proactively spot health issues and provide prompt interventions, improving the quality of patient care. This study's integration of advanced machine learning and IoT underlines the strategy's disruptive potential for transforming healthcare procedures. This study provides the foundation for a more effective, responsive, and patient-centered healthcare ecosystem by employing the potential of connected devices and predictive analytics.

Intelligent medical IoT health monitoring system based on VR and wearable devices

Abstract In order to improve the shortcomings of the traditional monitoring equipment that is difficult to measure the daily physical parameters of the elderly and improve the accuracy of parameter measurement, this article designs wearable devices through the Internet of Things technology and virtual reality technology. With this device, four daily physical parameters of the elderly, such as exercise heart rate, blood pressure, plantar health, and sleep function, are measured. The feasibility of the measurement method and equipment is verified by experiments. The experimental results showed that the accuracy of the measurement method based on the reflective photoplethysmography signal was high, with the mean and difference values of the subjects’ heart rate basically lying around 0 BPM and in good agreement between the estimated heart rate and the reference value. In the blood pressure measurements, the correlation coefficient between the P r s {P}_{rs} estimate and the reference value was 0.81. The estimation accuracy of the device used in the article was high, with the highest correlation coefficient of 0.96 ± 0.02 for subjects’ heart rate at rest, and its estimation error rate was 0.02 ± 0.01. The P n t h {P}_{{n}th} value for subject B8 exceeded the threshold of 0.5 before subject B21, and subject B8 had more severe symptoms, which was consistent with the actual situation. The wearable device was able to identify the subject’s eye features and provide appropriate videos to help subjects with poor sleep quality to fall asleep. The article provides a method and device that facilitates healthcare professionals to make real-time enquiries and receive user health advice.

An Ultra-Low Power Reconfigurable Biomedical AI Processor With Adaptive Learning for Versatile Wearable Intelligent Health Monitoring.

Wearable intelligent health monitoring devices with on-device biomedical AI processor can be used to detect the abnormity in users' biomedical signals (e.g., ECG arrythmia classification, EEG-based seizure detection). This requires ultra-low power and reconfigurable biomedical AI processor to support battery-supplied wearable devices and versatile intelligent health monitoring applications while achieving high classification accuracy. However, existing designs have issues in meeting one or more of the above requirements. In this work, a reconfigurable biomedical AI processor (named BioAIP) is proposed, mainly featuring: 1) a reconfigurable biomedical AI processing architecture to support versatile biomedical AI processing. 2) an event-driven biomedical AI processing architecture with approximate data compression to reduce the power consumption. 3) an AI-based adaptive-learning architecture to address patient-to-patient variation and improve the classification accuracy. The design has been implemented and fabricated using a 65nm CMOS process technology. It has been demonstrated with three typical biomedical AI applications, including ECG arrythmia classification, EEG-based seizure detection and EMG-based hand gesture recognition. Compared with the state-of-the-art designs optimized for single biomedical AI tasks, the BioAIP achieves the lowest energy per classification among the designs with similar accuracy, while supporting various biomedical AI tasks.

An adaptive and space-energy efficiency vibration absorber system using a self-sensing and tunable magnetorheological elastomer

Vibration monitoring and buffering is an important technical task in many industrial fields, such as bridges, machine tools and other specific applications. Recently, a magneto-rheological elastomer (MRE) has been utilized in an intelligent vibration absorber system with tunable stiffness, which can reversibly regulate its resonant frequency in real time to effectively prevent the resonance of the mechanical system. However, this MRE must be integrated with a vibrational sensor and equipped with an external magnetic field (e.g., electromagnetic coils or permanent magnets). Thus, the overall system is usually bulky and energy-intensive, which severely limits its broad application potential. In this work, we present an MRE-based tunable vibration absorber (MRE-TVA) with an elaborate 3-layered architecture integrated with a self-sensing triboelectric nanogenerator (TENG) sensor. The inner two-layer MRE structure exhibits variable stiffness, while the outer layer of the negative Poisson's ratio structure can confine the deformation of the inner MRE layers and form a contact-separation TENG sensor with the MRE, where the sensor can recognize external vibration information in a timely manner. In particular, the TENG sensor enables sensitive identification of the abnormal state of resonant excitation and other mechanical disturbances through dual logic judgment control based on vibration frequency and amplitude. For energy-space efficiency, a bistable permanent magnet controlled by a pulse current is integrated to generate a desirable magnetic field for modulation of the MRE stiffness. Compared with traditional electromagnetic coils, the energy and space utilization rates are increased by 94.25% and 75%, respectively. This combination of self-sensing and vibration damping systems achieves high energy-space efficiency, which will enable the intelligent health monitoring of bridges and other autonomous, smart, and long-term engineering tasks.

Retracted: Application of Wireless Sensor Network Model Based on Big Data Ecosystem in Intelligent Health Monitoring System

Retracted: Application of Wireless Sensor Network Model Based on Big Data Ecosystem in Intelligent Health Monitoring System

Stretchable Sensors: Novel Human Motion Monitoring Wearables.

A human body monitoring system remains a significant focus, and to address the challenges in wearable sensors, a nanotechnology-enhanced strategy is proposed for designing stretchable metal-organic polymer nanocomposites. The nanocomposite comprises reduced graphene oxide (rGO) and in-situ generated silver nanoparticles (AgNPs) within elastic electrospun polystyrene-butadiene-polystyrene (SBS) fibers. The resulting Sandwich Structure Piezoresistive Woven Nanofabric (SSPWN) is a tactile-sensitive wearable sensor with remarkable performance. It exhibits a rapid response time (less than three milliseconds) and high reproducible stability over 5500 cycles. The nanocomposite also demonstrates exceptional thermal stability due to effective connections between rGO and AgNPs, making it suitable for wearable electronic applications. Furthermore, the SSPWN is successfully applied to human motion monitoring, including various areas of the hand and RGB sensing shoes for foot motion monitoring. This nanotechnology-enhanced strategy shows promising potential for intelligent healthcare, health monitoring, gait detection, and analysis, offering exciting prospects for future wearable electronic products.

Retracted: The Intervention of Music Therapy on Behavioral Training of High-Functioning Autistic Children under Intelligent Health Monitoring.

[This retracts the article DOI: 10.1155/2022/5766617.].

Damage Monitoring of Braided Composites Using CNT Yarn Sensor Based on Artificial Fish Swarm Algorithm.

This study aims to enable intelligent structural health monitoring of internal damage in aerospace structural components, providing a crucial means of assuring safety and reliability in the aerospace field. To address the limitations and assumptions of traditional monitoring methods, carbon nanotube (CNT) yarn sensors are used as key elements. These sensors are woven with carbon fiber yarns using a three-dimensional six-way braiding process and cured with resin composites. To optimize the sensor configuration, an artificial fish swarm algorithm (AFSA) is introduced, simulating the foraging behavior of fish to determine the best position and number of CNT yarn sensors. Experimental simulations are conducted on 3D braided composites of varying sizes, including penetration hole damage, line damage, and folded wire-mounted damage, to analyze the changes in the resistance data of carbon nanosensors within the damaged material. The results demonstrate that the optimized configuration of CNT yarn sensors based on AFSA is suitable for damage monitoring in 3D woven composites. The experimental positioning errors range from 0.224 to 0.510 mm, with all error values being less than 1 mm, thus achieving minimum sensor coverage for a maximum area. This result not only effectively reduces the cost of the monitoring system, but also improves the accuracy and reliability of the monitoring process.

Transforming Clinical Information Systems: Empowering Healthcare through Telemedicine, Data Science, and Artificial Intelligence Applications.

In this synopsis, the editors of the Clinical Information Systems (CIS) section of the IMIA Yearbook of Medical Informatics overview recent research and propose a selection of best papers published in 2022 in the CIS field. The editors follow a systematic approach to gather relevant articles and select the best papers for the section. This year, they updated the query to incorporate the topic of telemedicine and removed search terms related to geographic information systems. The revised query resulted in a larger number of identified papers, necessitating the appointment of a third section editor to handle the increased workload. The editors narrowed the initial pool of articles to 15 candidate papers through a multi-stage selection process. At least seven independent reviews were collected for each candidate paper, and a selection meeting with the IMIA Yearbook editorial board led to the final selection of the best papers for the CIS section. The query was carried out in mid-January 2023 and retrieved a deduplicated result set of 5,206 articles from 1,500 journals. This year, 15 papers were nominated as candidates, and four were finally selected as the best papers in the CIS section.Including telemedicine in the query resulted in a substantial increase in the number of papers found. The analysis highlights the growing convergence between clinical information systems and telemedicine, with mobile health (mHealth) technologies and data science applications gaining prominence. The selected candidate papers emphasize the practical impact of research efforts, focusing on patient-centric outcomes and benefits, including intelligent mobile health monitoring systems and AI-assisted decision-making in healthcare. Looking ahead, the field of CIS is expected to continue evolving, driven by advances in telemedicine, mHealth technologies, data science, and AI integration, leading to more efficient, patient-oriented, and intelligent healthcare systems and overall improvement of global healthcare outcomes.

Design of P-FLANN Model for Intelligent Water Fountain Sound Pleasantness Monitoring Using Bio-inspired Computing and Human Speech Perception

Cognitive-inspired Computational Computing systems play a crucial role in designing intelligent health monitoring systems which help both patients and hospitals. It also helps in early and consistent decision-making for various health issues including human psychological health. Water fountains built in parks and public spaces are used as decorative instruments which not only give appealing visuals but also it provides a relaxing environment to the visitors. These natural sounds have a direct effect on the psychological health of visitors. Very few research works are reported on developing the relationship between water sounds and their corresponding psychological impact. This assessment needs trained manpower and a lot of experimental time which is costly and may not be always available. In this paper to access the human health-friendly water fountain sounds from the pleasantness, a Perceptually Weighted functional link artificial neural network (P-FLANN) model is developed. To reduce the computational complexity of training and for faster convergence, swam intelligence-based optimization algorithm is used for updating the weights. It is observed from the comparative simulation results that the proposed P-FLANN model can effectively perform prediction tasks which is not only cost-effective but also 95% accurate and can play a crucial role in designing human health-friendly water fountains in smart cities.

Non-contact high precision pulse-rate monitoring system for moving subjects in different motion states.

Remote photoplethysmography (rPPG) enables contact-free monitoring of the pulse rate by using a color camera. The fundamental limitation is that motion artifacts and changes in ambient light conditions greatly affect the accuracy of pulse-rate monitoring. We propose use of a high-speed camera and a motion suppression algorithm with high computational efficiency. This system incorporates a number of major improvements including reproduction of pulse wave details, high-precision pulse-rate monitoring of moving subjects, and excellent scene scalability. A series of quantization methods were used to evaluate the effect of different frame rates and different algorithms in pulse-rate monitoring of moving subjects. The experimental results show that use of 180-fps video and a Plane-Orthogonal-to-Skin (POS) algorithm can produce high-precision pulse-rate monitoring results with mean absolute error can be less than 5 bpm and the relative accuracy reaching 94.5%. Thus, it has significant potential to improve personal health care and intelligent health monitoring.

Survey on Sensors and Smart Devices for IoT Enabled Intelligent Healthcare System.

The Internet of Things (IoT) in the healthcare system is rapidly changing from the conventional hospital and concentrated specialist behavior to a distributed, patient-centric approach. With the advancement of new techniques, a patient needs sophisticated healthcare requirements. IoT-enabled intelligent health monitoring system with sensors and devices is a patient analysis technique to monitor the patient 24h a day. IoT is swapping the architecture and has improved the application of different complex systems. Healthcare devices are one of the most remarkable applications of the IoT. Many patient monitoring techniques are available in the IoT platform. This review presents an IoT-enabled intelligent health monitoring system by analyzing the papers reported between 2016 and 2023. This survey also discusses the concept of big data in IoT networks and the IoT computing technology known as edge computing. This review concentrated on sensors and smart devices used in intelligent IoT based health monitoring systems with merits and demerits. This survey gives a brief study based on sensors and smart devices used in IoT smart healthcare systems.

An intelligent impact load identification and localization method based on autonomic feature extraction and anomaly detection

The main problems of impact load identification are the complexity of dynamic data and the establishment of theoretical model. This paper proposed an intelligent impact load identification and localization method based on autonomic feature extraction and anomaly detection. The method firstly extracts features automatically from dynamic response through principal component analysis and trains ensemble learning model to identify and locate impact loads. Then the method sifts abnormal impact samples through mathematical statistics and studies the causes of the impact anomaly to improve the accuracy of identification. An anomaly detection model is also trained. The effectiveness and efficiency of the method are verified by identifying the force peak and location of impact loads acting on a thin-walled cylinder structure. The final error of the proposed method is less than 6.62% and the accuracy of anomaly detection is more than 91%. The result proves that the automatic feature extraction and anomaly detection make the proposed method more intelligent and superior to the existing methods. The method can be applied to various engineering structures and exhibits potential for intelligent structural design and structural health monitoring.