Intelligent Health Research Articles

Revolutionizing public health through digital health technology

ABSTRACT The aging population and increasing chronic diseases strain public health systems. Advancements in digital health promise to tackle these challenges and enhance public health outcomes. Digital health integrates digital health technology (DHT) across healthcare, including smart consumer devices. This article examines the application of DHT in public health and its significant impact on revolutionizing the field. Historically, DHT has not only enhanced the efficiency of disease prevention, diagnosis, and treatment but also facilitated the equitable distribution of global health resources. Looking ahead, DHT holds vast potential in areas such as personalized medicine, telemedicine, and intelligent health management. However, it also encounters challenges such as ethics, privacy, and data security. To further advance DHT, concerted efforts are essential, including policy support, investment in research and development, involvement of medical institutions, and improvement of public digital health literacy.

Intelligent health management technology use and loneliness among older adults with disabilities: the mediating role of social networks

Intelligent health management technology use and loneliness among older adults with disabilities: the mediating role of social networks

Comparative Analysis of Regression Methods for Estimation of Remaining Useful Life of Lithium Ion Battery

Lithium batteries play a critical role in modern technological applications, including electric vehicles and portable electronic devices. Ensuring accurate estimation of their remaining useful life is essential to improve system efficiency and reliability. This study focuses on predicting the remaining useful life of lithium batteries using advanced regression methods. Data were collected from lithium battery charge-discharge cycles, encompassing key operational parameters such as voltage, current, and temperature. The analysis employed several regression models, including linear regression, lasso regression, and Ridge regression, to identify relationships between these parameters and battery life. The models were evaluated based on estimation accuracy, with Root Mean Square Error (RMSE) as the primary performance metric. The findings demonstrate that regression methods can effectively capture non-linear relationships between input variables and the remaining useful life, with lasso and Ridge regression showing superior performance in reducing prediction errors. These results underscore the potential of regression-based approaches in providing robust and reliable estimations of battery life. The conclusions highlight the importance of these models for developing predictive battery management systems, which can optimize battery performance and extend their operational lifespan across various applications. This research establishes a solid foundation for future studies on intelligent battery health monitoring and management.

Research on health monitoring of concrete structure based on G-S-G

An improved concrete structure health monitoring method based on G-S-G is proposed, which fully combines an optimized Gray-Level Co-occurrence Matrix (GLCM) with an improved Self-Organizing Map (SOM) neural network to achieve accurate and real-time concrete structure health monitoring. First of all, in order to obtain a dynamic image of the crack damage region of interest (ROI) with clear contrast and obvious target, the image acquisition system and image optimization method are used to process the damaged image. Moreover, in order to realize the accurate location of crack damage, crack damage identification research based on GLCM-SOM effectively eliminates the interference of honeycomb and pothole damage on crack damage. In order to obtain the indicators for monitoring the health status of the structure, the damage characteristics and probability distribution characteristics of the concrete structure in the gray level co-occurrence matrix are combined to extract the probability range index (PRI). On the basis of extracting the crack damage index, in order to verify the reliability of the sensitive feature index, starting from the two dimensions of damage texture feature and data expansion, through reverse research of the damage model, the damage index of accurately locating the crack damage was selected. It follows that the final sensitive indicators: entropy (ENT) and PRI can be used for structural health monitoring because of their strong damage characterization ability and sensitivity to damage characteristics. This research shows that is helpful to realize the high precision intelligent concrete structure health monitoring of modern concrete structure crack damage.

Exploring the role of cardiac activity in forecasting cognitive fatigue with machine learning

Fatigue poses significant risks to safety, productivity, and overall well-being. Traditional statistical methods have been employed for inferring fatigue-related patterns; however, there is a need for interpretable machine learning approaches that integrate time series prediction capabilities to develop intelligent health systems for fatigue management. This study explored forecasting perception and performance scores associated with fatigue using cardiac activity and heart rate variability (HRV), employing both generalized and personalized models with a 10-minute forecast interval during a cognitively fatiguing task. Participants underwent a two-hour working memory task while providing subjective fatigue responses every 10 min, serving as perception labels, and their performance within each 10-minute interval was quantified as their performance labels. The results revealed that performance-labeled models generated lower mean absolute errors compared to perception-labeled models, while the Gradient Boosting Regression algorithm achieved the lowest mean absolute error in forecasting performance scores due to fatigue for both generalized and personalized models. Sample entropy, ratio of Standard Deviation-Poincaré, the proportion of peak-to-peak intervals over 50 ms, coefficient of variation of peak-to-peak intervals, and low frequency were the most important features for predicting performance. These findings offer the potential to forecast performance decline resulting from fatigue in working memory tasks, facilitating the implementation of fatigue mitigation interventions to reduce injury risks and performance impairments. The integration of interpretable machine learning methods with time series forecasting provides valuable insights for developing intelligent systems that proactively manage fatigue and optimize performance across various domains.

Real-time Prediction Model for Heart Disease Risk during Medical Consultations and Health Monitoring

In the realm of cardiovascular health, early detection and proactive management of heart disease are critical for improving patient outcomes. This paper introduces a novel real-time prediction model designed to assess heart disease risk during medical consultations and continuous health monitoring. Leveraging advanced machine learning techniques and a diverse dataset comprising patient demographics, medical history, and biometric measurements, our model provides immediate, actionable insights into an individual’s cardiovascular health. The model integrates seamlessly with electronic health record (EHR) systems and wearable health devices, offering real-time risk assessments that aid healthcare professionals in making informed decisions and tailoring personalized treatment plans. Through extensive validation and testing, our model demonstrates high accuracy and reliability, with potential to significantly enhance early intervention strategies and patient engagement in heart disease prevention. This research underscores the transformative potential of real-time predictive analytics in clinical practice and highlights pathways for future development and integration of intelligent health monitoring solutions.

Simulation of microenvironmental partial pressure inside spacesuits during near-Earth orbit extravehicular activities and intelligent health state assessment

Simulation of microenvironmental partial pressure inside spacesuits during near-Earth orbit extravehicular activities and intelligent health state assessment

Optimizing Cardiovascular Health Monitoring with IoT-Enabled Sensors and AI: A Focus on Obesity-Induced Cardiovascular Risks in Young Adults

With shifts in lifestyle and dietary patterns, obesity has become an increasing health issue among younger demographics, particularly affecting young adults. This trend is strongly associated with a heightened risk of developing chronic diseases, especially cardiovascular conditions. However, conventional health monitoring systems are often limited to basic parameters such as heart rate, pulse pressure (PP), and systolic blood pressure (SBP), which may not provide a comprehensive assessment of cardiac health. This study introduces an intelligent heart health monitoring system that leverages the Internet of Things (IoT) and advanced sensor technologies. By incorporating IoT-based sensors, this system aims to improve the early detection and continuous monitoring of cardiac function in young obese women. The research employed a TERUMO ES-P2000 to measure blood pressure and a PhysioFlow device to assess noninvasive cardiac hemodynamic parameters. Through precise sensor data collection, the study identified key indicators for monitoring cardiovascular health. Machine learning models and big data analysis were utilized to predict cardiac index (CI) values based on the sensor-derived inputs. The findings indicated that young obese women showed significant deviations in blood pressure (SBP and PP) and cardiac hemodynamic metrics (SVI, EDV, and ESV) at an early stage. The implementation of signal processing techniques and IoT sensors enhanced the CI prediction accuracy from 33% (using basic parameters like heart rate, PP, and SBP) to 66%. Moreover, the integration of extra sensor-based parameters, such as Stroke Volume Index (SVI) and Cardiac Output (CO), along with the use of color space transformations, successfully improved the prediction accuracy of the original data by 36.68%, increasing from 53.33% to 90.01%. This represents a significant improvement of 30.01% compared to the existing technology’s accuracy of 60%. These results underscore the importance of utilizing sensor-derived parameters as critical early indicators of cardiac function in young obese women. This research advances smart healthcare through early cardiovascular risk assessment using AI and noninvasive sensors.

Experimental Study on the Effects of Controllable Parameters on the Healthy Operation of SF-2A Material Water-Lubricated Stern Bearing in Multi-Point Ultra-Long Shaft Systems of Ships

Effective control of the health operating condition of multi-support, ultra-long shaft system water-lubricated stern bearings is crucial for supporting the intelligent maintenance and health management of ships. This study investigates the failure modes of water-lubricated stern bearings and focuses on the critical failure modes of abnormal wear and high-temperature meltdown to analyze the mechanisms and influencing factors of these failures. It discusses the conditions for healthy operation of water-lubricated stern bearings, as well as methods for controlling lubrication and temperature rise. Based on this, controllable parameters for the healthy operation of water-lubricated stern bearings were selected, an experimental rig was constructed, and experiments were conducted using SF-2A material water-lubricated bearings. The experimental results indicate that by controlling parameters such as shaft rotational speed, inlet lubrication water temperature, clear-water lubrication, sediment-laden-water lubrication, bearing specific pressure, and the surface morphology of the bearing liner, the velocity characteristics, lubrication characteristics, and temperature rise characteristics of the bearings can be effectively altered. The sensitivity of the lubrication and temperature rise characteristics of SF-2A material water-lubricated stern bearings to controllable parameters varies under different environmental conditions. The study finds that precise control of these parameters can improve the operating condition and reliability of water-lubricated bearings.

Explainable Artificial Intelligence for Medical Applications: A Review

The continuous development of artificial intelligence (AI) theory has propelled this field to unprecedented heights, owing to the relentless efforts of scholars and researchers. In the medical realm, AI takes a pivotal role, leveraging robust machine learning (ML) algorithms. AI technology in medical imaging aids physicians in X-ray, computed tomography (CT) scans, and magnetic resonance imaging (MRI) diagnoses, conducts pattern recognition and disease prediction based on acoustic data, delivers prognoses on disease types and developmental trends for patients, and employs intelligent health management wearable devices with human-computer interaction technology to name but a few. While these well-established applications have significantly assisted in medical field diagnoses, clinical decision-making, and management, collaboration between the medical and AI sectors faces an urgent challenge: How to substantiate the reliability of decision-making? The underlying issue stems from the conflict between the demand for accountability and result transparency in medical scenarios and the black-box model traits of AI. This article reviews recent research grounded in explainable artificial intelligence (XAI), with an emphasis on medical practices within the visual, audio, and multimodal perspectives. We endeavour to categorise and synthesise these practices, aiming to provide support and guidance for future researchers and healthcare professionals.

Revolutionizing Healthcare with Intelligent Remote Health Monitoring

Revolutionizing healthcare through intelligent remote health monitoring involves leveraging advanced technologies such as artificial intelligence, wearable devices, and IoT to provide continuous and real-time monitoring of patient's health conditions. This approach enables proactive healthcare management by collecting and analyzing vital health data remotely, facilitating Prompt identification of possible wellness issues, and reducing the need for frequent hospital visits. It promotes personalized healthcare, improves patient outcomes, and enhances the efficiency of healthcare systems by allowing healthcare providers to make data-driven decisions. This transformation in healthcare delivery ensures better access to care, particularly for individuals in remote or underserved areas, while also addressing the growing demand for healthcare services globally. Revolutionizing healthcare through intelligent remote health monitoring involves leveraging Cutting-edge innovations like synthetic intelligence, wearable devices, and the Internet of Things (IoT) to provide continuous and real-time monitoring of patient's health conditions. This approach enables proactive healthcare management by collecting and analyzing vital health data remotely, facilitating Prompt identification of emerging health issues, and reducing the need for frequent hospital visits. The integrationof machine learning algorithms allows for predictive analytics, which can anticipate health deteriorations and recommend timely interventions. It promotes personalized healthcare by tailoring treatments to individual needs, improves patient outcomes through constant monitoring and timely responses, and enhances healthcare systems by allowing healthcare providers to make data-driven decision.

Intelligent health management strategies, adaptation, and validation of the SAT-SF questionnaire into Spanish in cancer patients

ABSTRACT Introduction A sudden cancer diagnosis creates a psycho-emotional and social crisis, making it crucial to manage emotional coping strategies and assess comprehensive health care, addressing both behavioral and practical aspects of patients’ daily lives. Although there are instruments that measure strategies for emotional coping, emotional adjustment or post-traumatic growth, we still do not have a questionnaire in Spanish that comprehensively evaluates health self-management. Aim This study aims to translate into Spanish and determine the validity and reliability of the Spanish version of the Smart Management Strategy for Health Assessment Tool (SAT-SF) evaluation questionnaire in Peruvian patients with cancer. Methodology Cross-sectional study. Translation and back-translation of the SAT-SF were carried out. Next, 271 Peruvian adult patients of both sexes diagnosed with different types of cancer participated in the validation process. The factor structure was studied through Confirmatory Factor Analysis, the scale’s reliability through Cronbach’s alpha and omega coefficients, and the factorial invariance of the scale according to sex. For concurrent validity, the association with the Medical Outcomes Study (MOS) social support scale was analyzed. Result The original model of three related dimensions presents acceptable fit indices to the data (χ 2 = 1202.23; df = 402; p < .001; RMSEA = .086 [90% CI .080–.091]; SRMR = .065; CFI = .940; TLI = .930). The scale’s factor structure has shown evidence of being strictly invariant for the groups of men and women. The reliability indices according to dimensions were Personal competencies (α = .91; ω = .92), Preparation strategies (α = .87; ω = .86) and Implementation strategies (α = .89; ω = .91). Additionally, it was evidenced that the dimensions of the SAT-SF are significantly related to the dimensions of the Social Support scale. Conclusion The SAT-SF questionnaire was adapted to Spanish, demonstrating adequate construct validity, internal consistency, reliability, and concurrent validity.

A hybrid prognostic framework: Stochastic degradation process with adaptive trajectory learning to transfer historical health knowledge

Remaining useful life (RUL) prediction is crucial to supporting intelligent maintenance and health management of safety–critical products. Although advanced data-driven approaches such as deep neural networks are effective in processing high-dimensional non-linear health features, their application to field RUL prediction confronts with two challenges: (a) adaptivity of the lifetime parameter learning process is often restricted, and (b) prediction of multi-source uncertainties is almost analytically intractable. This paper addresses such challenges by devising a tractable, global adaptive model-data-interaction prognostic framework, where a non-linear stochastic degradation model governed by self-adaptive trajectory pattern is constructed to transfer historical health knowledge. In particular, a joint parameter learning framework is established under the structure of a multi-branch Bayesian network, such that to simultaneously learn: (a) degradation model parameters, and (b) network hyper-parameters. Additionally, the key control parameters of the degradation process are updated adaptively leveraging multi-dimensional sequential Bayesian learning. An efficient interpolation algorithm is further proposed to alleviate computation burden of RUL distributions. Case studies conducted on both turbofan engines degradation data and field train bearing vibration data demonstrate the superior model performance compared to existing methodologies.

Self-Powered, Flexible, Wireless and Intelligent Human Health Management System Based on Natural Recyclable Materials.

Combining wearable sensors with modern technologies such as internet of things and big data to monitor or intervene in obesity-induced chronic diseases, such as obstructive sleep apnea, type II diabetes, cardiovascular diseases, and Alzheimer's disease, is of great significance to the self-health management of human beings. This study designed a loofah-conducting graphite four friction layer enhanced triboelectric nanogenerator (LG-TENG) and developed a health management system for human motion recognition and early warning of sleep breathing abnormalities. By uniformly spraying and depositing conductive graphite on the surface of the loofah and the elastic film cross-interlocking bending structure design, the signal strength of the LG-TENG has been improved by 390%. The stable output signal is still maintained after 1500 s of continuous operation at a frequency of 2 Hz. LG-TENG can realize accurate motion analysis by muscle contraction state. Combining different deep learning models resulted in 98.1% accuracy in recognizing seven categories of displacement speeds for an individual and 96.46% accuracy in recognizing seven categories of displacement speeds for three individuals. In addition, the sleep breathing monitoring early warning system was developed by integrating Bluetooth wireless transmission and upper computer analysis technology. This system aims to analyze and provide real-time warnings for sleep-breathing abnormalities. This research promotes an innovation of TENG technology based on the advantages of natural materials, recyclability and low cost. It offers new ideas for self-health management and scientific exercise for obese people, showing a broad application prospect.

Overview of object detection based on deep learning

Abstract. In recent years, computer vision technology has developed rapidly, as one of its important research directions, object detection has received widespread attention due to its high accuracy. Meanwhile, object detection has many application fields, such as intelligent transportation, medical and health, security systems, etc. Traditional object detection methods have limitations when applied to complex real-world scenarios. To improve the shortcomings of conventional methods, deep learning based object detection algorithms have significantly improved the efficiency of object detection and become a research hotspot in object detection. This article summarizes the algorithms into two-stage and one-stage object detection algorithms based on the technical processes and structural differences in handling object detection tasks. Firstly, several common two-stage and one-stage object detection algorithms and their applications in real-world scenarios are introduced. Then their data sets and algorithm performance are analyzed and compared. Finally, according to the results of the comparison, the existing problems of the two-stage object detection algorithm and the one-stage object detection method are discussed, and their future development directions are pointed out.

Gear-fault monitoring and digital twin demonstration of aircraft engine based on piezoelectric vibration sensor for engine health management

Aircraft engine vibration signals carry crucial operational information, necessitating accurate monitoring and analysis to ensure flight safety. However, the detection accuracy of vibration sensors is significantly compromised in high-temperature environments, posing substantial challenges for traditional battery-powered models. Here, we present a self-powered piezoelectric vibration sensor based on a polyvinylidene fluoride-polyimide nanofiber membrane, which can effectively mitigate the performance degradation in high-temperature environments. The piezoelectric vibration sensor is capable of distinguishing the vibration signals generated by healthy gears, pitting gears, and broken gears, achieving a recognition accuracy of 96.3 % by using machine-learning algorithms. Finally, we develop a digital twin system to identify failure modes in aircraft engine gears, promising intelligent engine health management.

Femtosecond laser-inscribed in-fiber Mach-Zehnder interferometer for ultra-sensitive small-angle torsion measurement

We propose and demonstrate a novel in-fiber Mach-Zehnder interferometer (MZI), using a femtosecond (FS) laser to inscribe two parallel straight waveguides along both sides of the core-cladding boundary of an optical fiber. Both simulations and experiments demonstrated that constructing a second straight waveguide (WG2) on the opposite side of the fiber core significantly enhances the mode coupling efficiency in fiber, resulting in ultra-sensitive small twist-angle torsion measurement by intensity interrogation. By finely adjusting the waveguide spacing between the WG2 and the fiber core during the FS fabrication process, a large tuning range of ∼ 200 nm for the designable interference wavelength with maximal fringe contrast has been achieved. Through comparative experiments, the proposed in-fiber MZI performs ultra-sensitivity with 17417.92 dB/(rad/mm) over small twist angles from −10° to 0° for the sample “WG1-4/WG2-4” and 19480.57 dB/(rad/mm) over small twist angles from 0° to 10° for the sample “WG1-4/WG2-6”. Besides, the device is insensitive to temperature and strain. The exhibited advantages of high sensitivity, high reproducibility, and low crosstalks promote the proposed in-fiber MZI to be applied in future industrial engineering monitoring and intelligent health monitoring.

Assessment of Personalized Exercise Prescriptions Issued by ChatGPT 4.0 and Intelligent Health Promotion Systems for Patients with Hypertension Comorbidities Based on the Transtheoretical Model: A Comparative Analysis.

Exercise is a vital adjunct therapy for patients with hypertension comorbidities. However, medical personnel and patients face significant obstacles in implementing exercise prescription recommendations. AI has been developed as a beneficial tool in the healthcare field. The performance of intelligent tools such as ChatGPT 4.0 and Intelligent Health Promotion Systems (IHPS) in issuing exercise prescriptions for patients with hypertension comorbidities remains to be verified. After collecting patient information through IHPS hardware and questionnaire systems, the data were input into the software terminals of ChatGPT 4.0 and IHPS according to the five stages of the Transtheoretical Model, resulting in exercise prescriptions. Subsequently, experts from various fields scored the accuracy, comprehensiveness, and applicability of each prescription, along with providing professional recommendations based on their expertise. By comparing the performance of both systems, their capability to serve this specific group was evaluated. In most cases, ChatGPT scored significantly higher than IHPS in terms of accuracy, comprehensiveness, and applicability. However, when patients exhibited certain functional movement disorders, GPT's exercise prescriptions involved higher health risks, whereas the more conservative approach of IHPS was advantageous. The path of generating exercise prescriptions using artificial intelligence, whether via ChatGPT or IHPS, cannot achieve a completely satisfactory state.But can serve as a supplementary tool for professionals issuing exercise prescriptions to patients with hypertension comorbidities, especially in alleviating the financial burden of consulting costs. Future research could further explore the performance of AI in issuing exercise prescriptions, harmonize it with physiological indicators and phased feedback, and develop an interactive user experience.

Intelligent Fault Diagnosis of Planetary Gearbox Across Conditions Based on Subdomain Distribution Adversarial Adaptation.

Sensory data are the basis for the intelligent health state awareness of planetary gearboxes, which are the critical components of electromechanical systems. Despite the advantages of intelligent diagnostic techniques for detecting intricate fault patterns and improving diagnostic speed, challenges still persist, which include the limited availability of fault data, the lack of labeling information and the discrepancies in features across different signals. Targeting this issue, a subdomain distribution adversarial adaptation diagnosis method (SDAA) is proposed for faults diagnosis of planetary gearboxes across different conditions. Firstly, nonstationary vibration signals are converted into a two-dimensional time-frequency representation to extract intrinsic information and avoid frequency overlapping. Secondly, an adversarial training mechanism is designed to evaluate subclass feature distribution differences between the source and target domain. A conditional distribution adaptation is employed to account for correlations among data from different subclasses. Finally, the proposed method is validated through experiments on planetary gearboxes, and the results demonstrate that SDAA can effectively diagnose faults under crossing conditions with an accuracy of 96.7% in diagnosing gear faults and 95.2% in diagnosing planet bearing faults. It outperforms other methods in both accuracy and model robustness. This confirms that this approach can refine domain-invariant information for transfer learning with less information loss from the sub-class level of fault data instead of the overall class level.

Exploring the Impact of an Interactive Electronic Pegboard on Manual Dexterity and Cognitive Skills of Patients With Stroke: Preliminary Analysis.

As individuals age, the incidence and mortality rates of cerebrovascular accidents significantly rise, leading to fine motor impairments and cognitive deficits that impact daily life. In modern occupational therapy, assessing manual dexterity and cognitive functions typically involves observation of patients interacting with physical objects. However, this pen-and-paper method is not only time-consuming, relying heavily on therapist involvement, but also often inaccurate. Digital assessment methods, therefore, have the potential to increase the accuracy of diagnosis, as well as decrease the workload of health care professionals. This study examined the feasibility of an interactive electronic pegboard for the assessment and rehabilitation of patients with stroke. We explored the pegboard's clinical applicability by examining the relationship among stages, timing, and difficulty settings, as well as their alignment with patient capabilities. In total, 10 participants used a prototype of the pegboard for functional and task assessments; questionnaire interviews were conducted simultaneously to collect user feedback. Patients with stroke consistently required more time to complete tasks than expected, significantly deviating from the initial time frames. Additionally, the participants exhibited a slight reduction in performance levels in both manual dexterity and cognitive abilities. Insights from questionnaire responses revealed that the majority of participants found the prototype interface easy and enjoyable to use, with good functionality. This preliminary investigation supports the efficacy of interactive electronic pegboards for the rehabilitation of the hand functions of patients with stroke, as well as training their attentional and cognitive abilities. This digital technology could potentially alleviate the burden of health care workers, positioning it as a valuable and intelligent precision health care tool.