Construction of Community Medical Communication Service and Rehabilitation Model for Elderly Patients under the Internet of Things

The objective of this study was to discuss the health management of elderly patients in the community and the management of community rehabilitation under the support of the new Internet of Things (IoT). The IoT technology was adopted to monitor the wearable devices through mobile medical physiological data. The heart rate, blood pressure, respiratory rate, and other physiological indicators of the elderly were collected in real time. The support vector machine (SVM) algorithm was selected as the core algorithm for the elderly physiological index disease risk assessment, the fuzzy comprehensive evaluation method was adopted as the core method of the elderly disease risk quantitative assessment model to process the physiological indicators, and finally, a reasonable physiological index processing model and quantitative indicators of disease risk were obtained. The data on vascular disease were selected from the MIMIC database. In addition, the advantages and disadvantages of the SVM algorithm and the Backpropagation Neural Network (BPNN) algorithm were compared and analysed. The final verification results showed that the fusion accuracy of the SVM processing MIMIC database and the University of California Irvine (UCI) dataset was 0.8327 and 0.8045, respectively, while the fusion accuracy of the BPNN algorithm in processing the same data was 0.7792 and 0.7288, respectively. It was obvious that the fusion accuracy of the SVM algorithm was higher than that of the BPNN algorithm, and the accuracy difference of the SVM algorithm was lower than that of the BPNN algorithm in different groups of data. In the verification of the elderly disease risk quantitative assessment model, the results were consistent with the selected data, which verified the effectiveness of the design model in this study. Therefore, it can be used as a quantitative assessment model of general elderly physiological indicators of disease risk and can be applied to the community medical communication management system. It proved that the model of medical communication and rehabilitation services for elderly patients in the community constructed in this study can definitely help the development of community service for the elderly.

Smart Health-Enhanced Early Mobilisation in Intensive Care Units

Critically ill patients that stay in Intensive Care Units (ICU) for long periods suffer from Post-Intensive Care Syndrome or ICU Acquired Weakness, whose effects can decrease patients’ quality of life for years. To prevent such issues and aiming at shortening intensive care treatments, Early Mobilisation (EM) has been proposed as an encouraging technique: the literature includes numerous examples of the benefits of EM on the prevention of post-operative complications and adverse events. However, the appropriate application of EM programmes entails the use of scarce resources, both human and technical. Information and Communication Technologies can play a key role in reducing cost and improving the practice of EM. Although there is rich literature on EM practice and its potential benefits, there are some barriers that must be overcome, and technology, i.e., the use of sensors, robotics or information systems, can contribute to that end. This article reviews the literature and analyses on the use of technology in the area of EM, and moreover, it proposes a smart health-enhanced scenario.

Intelligent automated drug administration and therapy: future of healthcare

In the twenty-first century, the collaboration of control engineering and the healthcare sector has matured to some extent; however, the future will have promising opportunities, vast applications, and some challenges. Due to advancements in processing speed, the closed-loop administration of drugs has gained popularity for critically ill patients in intensive care units and routine life such as personalized drug delivery or implantable therapeutic devices. For developing a closed-loop drug delivery system, the control system works with a group of technologies like sensors, micromachining, wireless technologies, and pharmaceuticals. Recently, the integration of artificial intelligence techniques such as fuzzy logic, neural network, and reinforcement learning with the closed-loop drug delivery systems has brought their applications closer to fully intelligent automatic healthcare systems. This review's main objectives are to discuss the current developments, possibilities, and future visions in closed-loop drug delivery systems, for providing treatment to patients suffering from chronic diseases. It summarizes the present insight of closed-loop drug delivery/therapy for diabetes, gastrointestinal tract disease, cancer, anesthesia administration, cardiac ailments, and neurological disorders, from a perspective to show the research in the area of control theory.

Controlled infusion of intravenous cardiac drugs using global optimization

OBJECTIVES:

The objective of the study is to develop an automatic drug infusion control system during cardiovascular surgery.

MATERIALS AND METHODS:

Based on the clinical drug dosage analysis, the modeling of cardiovascular system with baroreceptor model is mathematically modeled using compartmental approach, considering the relationship between the volume and flow rate of blood during each heartbeat. This model is then combined with drug modeling of noradrenaline and nitroglycerine by deriving the volume and drug mass concentration equations, based on pharmacokinetics and pharmacodynamics of the drugs. The closed-loop patient models are derived from the open-loop data obtained from the physiology-drug model with covariate as age. The proportional-integral controller is designed based on optimal values obtained from bacterial foraging-oriented particle swarm optimization algorithm. The controllers are implemented individually for each control variable such as aortic pressure and cardiac output (CO), irrespective of varying weights based on the relative gain array analysis which depicts the maximum influence of cardiac drugs on control variables.

RESULTS:

The physiology-drug model output responses are simulated using MATLAB. The controlled responses of aortic pressure and CO with infusion rate of cardiac drugs are obtained. The robustness of the controller is checked by introducing variations in cardiovascular model parameters. The efficiency of the controller during normal and abnormal conditions is compared using time domain analysis.

CONCLUSIONS:

The controller design was efficient and can be further improved by designing switching-based controllers.

Commercial Devices-Based System Designed to Improve the Treatment Adherence of Hypertensive Patients

This paper presents an intelligent system designed to increase the treatment adherence of hypertensive patients. The architecture was developed to allow communication among patients, physicians, and families to determine each patient's rate assertion of medication intake time and their self-monitoring of blood pressure. Concerning the medication schedule, the system is designed to follow a predefined prescription, adapting itself to undesired events, such as mistakenly taking medication or forgetting to take medication on time. When covering the blood pressure measurement, it incorporates best medical practices, registering the actual values in recommended frequency and form, trying to avoid the known "white-coat effect." We assume that taking medicine precisely and measuring blood pressure correctly may lead to good adherence to the treatment. The system uses commercial consumer electronic devices and can be replicated in any home equipped with a standard personal computer and Internet access. The resulting architecture has four layers. The first is responsible for adding electronic devices that typically exist in today's homes to the system. The second is a preprocessing layer that filters the data generated from the patient's behavior. The third is a reasoning layer that decides how to act based on the patient's activities observed. Finally, the fourth layer creates messages that should drive the reactions of all involved actors. The reasoning layer takes into consideration the patient's schedule and medication-taking activity data and uses implicit algorithms based on the J48, RepTree, and RandomTree decision tree models to infer the adherence. The algorithms were first adjusted using one academic machine learning and data mining tool. The system communicates with users through smartphones (anytime and anywhere) and smart TVs (in the patient's home) by using the 3G/4G and WiFi infrastructure. It interacts automatically through social networks with doctors and relatives when changes or mistakes in medication intake and blood pressure mean values are detected. By associating the blood pressure data with the history of medication intake, our system can indicate the treatment adherence and help patients to achieve better treatment results. Comparisons with similar research were made, highlighting our findings.

Closed-loop hemodynamic management

As the operating room and intensive care settings become increasingly complex, the required vigilance practitioners must dedicate to a wide array of clinical systems has increased concordantly. The resulting shortage of available attention to these various clinical tasks creates a vacuum for the introduction of systems that can administer well-established goal-directed therapies without significant provider feedback. Recently, there has been an explosion of academic exploration into creating such automated systems, with a strong specific focus on hemodynamic control. Within this field, the largest focus has been on goal-directed fluid therapy as systems automating vasopressor administration have only recently become viable options. Our goal in this review article is to summarize the validity of the relevant goal-directed hemodynamic systems and explore the expanding role of automation within these systems.

Distinctive Steady-State Heart Rate and Blood Pressure Responses to Passive Robotic Leg Exercise during Head-Up Tilt: A Pilot Study in Neurological Patients

Introduction: Robot-assisted tilt table therapy was proposed for early rehabilitation and mobilization of patients after diseases such as stroke. A robot-assisted tilt table with integrated passive robotic leg exercise (PE) mechanism has the potential to prevent orthostatic hypotension usually provoked by verticalization. In a previous study with rather young healthy subjects [average age: 25.1 ± 2.6 years (standard deviation)], we found that PE effect on the cardiovascular system depends on the verticalization angle of the robot-assisted tilt table. In the current study, we investigated in an older population of neurological patients (a) whether they show the same PE effects as younger healthy population on the cardiovascular system at different tilt angles, (b) whether changing the PE frequency (i.e., stepping speed) influences the PE effect on the cardiovascular system, (c) whether PE could prevent orthostatic hypotension, and finally, (d) whether PE effect is consistent from day to day.

Methods: Heart rate (HR), and systolic and diastolic blood pressures (sBP, dBP) in response to PE at two different tilt angles (α = 20°, 60°) with three different PE frequencies (i.e., 0, 24, and 48 steps per minute) of 10 neurological patients [average age: 68.4 ± 13.5 years (standard deviation)] were measured on 2 consecutive days. Linear mixed models were used to develop statistical models and analyze the repeated measurements.

Results: The models show that: PE significantly increased sBP and dBP but had no significant effect on HR. (a) Similar to healthy subjects the effect of PE on sBP was dependent on the tilt angle with higher tilt angles resulting in a higher increase. Head-up tilting alone significantly increased HR and dBP but resulted in a non-significant drop in sBP. PE, in general, had a more additive effect on increasing BP. (b) The effect of PE was not influenced by its speed. (c) Neither during head-up tilt alone nor in combination with PE did participants experience orthostatic hypotension. (d) The measurement day was not a statistically significant factor regarding the effects of verticalization and PE on the cardiovascular response.

Conclusion: We provide evidence that PE can increase steady-state values of sBP and dBP in neurological patients during head-up tilt. Similar to healthy subjects the effect on sBP depends on the verticalization angle of the robot-assisted tilt table. PE might have the potential to prevent orthostatic hypotension, but as the amount of drop in BP in response to head-up tilting was not leading to orthostatic hypotension in our patients, we could neither conclude nor reject such a preventive compensatory effect. Furthermore, we found that changing the PE speed does not influence the steady-state cardiovascular response.

Modeling the effect of tilting, passive leg exercise, and functional electrical stimulation on the human cardiovascular system

Long periods of bed rest negatively affect the human body organs, notably the cardiovascular system. To avert these negative effects and promote functional recovery in patients dealing with prolonged bed rest, the goal is to mobilize them as early as possible while controlling and stabilizing their cardiovascular system. A robotic tilt table allows early mobilization by modulating body inclination, automated passive leg exercise, and the intensity of functional electrical stimulation applied to leg muscles (inputs). These inputs are used to control the cardiovascular variables heart rate (HR), and systolic and diastolic blood pressures (sBP, dBP) (outputs). To enhance the design of the closed-loop cardiovascular biofeedback controller, we investigated a subject-specific multi-input multi-output (MIMO) black-box model describing the relationship between the inputs and outputs. For identification of the linear part of the system, two popular linear model structures-the autoregressive model with exogenous input and the output error model-are examined and compared. The estimation algorithm is tested in simulation and then used in four study protocols with ten healthy participants to estimate transfer functions of HR, sBP and dBP to the inputs. The results show that only the HR transfer functions to inclination input can explain the variance in the data to a reasonable extent (on average 69.8%). As in the other input types, the responses are nonlinear; the models are either not reliable or explain only a negligible amount of the observed variance. Analysis of both, the nonlinearities and the occasionally occurring zero-crossings, is necessary before designing an appropriate MIMO controller for mobilization of bedridden patients.

Distinctive Steady-State Heart Rate and Blood Pressure Responses to Passive Robotic Leg Exercise and Functional Electrical Stimulation during Head-Up Tilt

Introduction: Tilt tables enable early mobilization of patients by providing verticalization. But there is a high risk of orthostatic hypotension provoked by verticalization, especially after neurological diseases such as spinal cord injury. Robot-assisted tilt tables might be an alternative as they add passive robotic leg exercise (PE) that can be enhanced with functional electrical stimulation (FES) to the verticalization, thus reducing the risk of orthostatic hypotension. We hypothesized that the influence of PE on the cardiovascular system during verticalization (i.e., head-up tilt) depends on the verticalization angle, and FES strengthens the PE influence. To test our hypotheses, we investigated the PE effects on the cardiovascular parameters heart rate (HR), and systolic and diastolic blood pressures (sBP, dBP) at different angles of verticalization in a healthy population.

Methods: Ten healthy subjects on a robot-assisted tilt table underwent four different study protocols while HR, sBP, and dBP were measured: (1) head-up tilt to 60° and 71° without PE; (2) PE at 20°, 40°, and 60° of head-up tilt; (3) PE while constant FES intensity was applied to the leg muscles, at 20°, 40°, and 60° of head-up tilt; (4) PE with variation of the applied FES intensity at 0°, 20°, 40°, and 60° of head-up tilt. Linear mixed models were used to model changes in HR, sBP, and dBP responses.

Results: The models show that: (1) head-up tilt alone resulted in statistically significant increases in HR and dBP, but no change in sBP. (2) PE during head-up tilt resulted in statistically significant changes in HR, sBP, and dBP, but not at each angle and not always in the same direction (i.e., increase or decrease of cardiovascular parameters). Neither adding (3) FES at constant intensity to PE nor (4) variation of FES intensity during PE had any statistically significant effects on the cardiovascular parameters.

Conclusion: The effect of PE on the cardiovascular system during head-up tilt is strongly dependent on the verticalization angle. Therefore, we conclude that orthostatic hypotension cannot be prevented by PE alone, but that the preventive effect depends on the verticalization angle of the robot-assisted tilt table. FES (independent of intensity) is not an important contributing factor to the PE effect.