A novel health monitoring system for vital signs using IoT.

The current research looked at how to use the Internet of Things (IoT) to create a vital sign health monitoring system. Eight indications are employed to get critical patient information. Therefore, the number of nodes of the IoT embedded in the human body is 8, which have been worked on in different places of the body. Among the 8 nodes, node number 1 is located in the center of the grid (the center of the human body). The number of rounds is 9000 and the nodes are adopted with the initial energy of the nodes of 0.5 J and the radio range of 10 m. MATLAB software was used to simulate the WBAN network, which consists of IoT sensors embedded in the human body. The eight-item health assessment tool takes the following into account: pulse rate, blood pressure (mm Hg), serum cholesterol (mg/dl), temperature (°C), exercise-induced angina, and exercise-induced ST-wave depression, major blood vessels are counted using a medical procedure called endoscopy that involves examining the alveoli, which are small air sacs in the lungs where gas exchange occurs. We compared the number of major vessels at rest with the maximal heart rate during activity. The sensors were responsible for sending this data to the health center (base station). The data collected from the installation of these 8 sensors on 303 patients were collected and evaluated by machine learning method using MLP neural network method. Finally, it can be claimed that the present study has provided an automated method of determining the health of people using the IoT in a way that provides a state of health with an accuracy of over 99% and can be used in medical centers.

Using Physiological Biomarkers to Optimize Management of TBI in Austere Environments.

INTRODUCTION: Multimodal monitoring is the use of data from multiple physiological sensors combined in a way to provide individualized patient management. It is becoming commonplace in the civilian care of traumatic brain-injured patients. We hypothesized we could bring the technology to the battlefield using a noninvasive sensor suite and an artificial intelligence-based patient management guidance system. METHODS: Working with military medical personnel, we gathered requirements for a hand-held system that would adapt to the rapidly evolving field of neurocritical care. To select the optimal sensors, we developed a method to evaluate both the value of the sensor's measurement in managing brain injury and the burden to deploy that sensor in the battlefield. We called this the Value-Burden Analysis which resulted in a score weighted by the Role of Care. The Value was assessed using 7 criteria, 1 of which was the clinical value as assessed by a consensus of clinicians. The Burden was assessed using 16 factors such as size, weight, and ease of use. We evaluated and scored 17 sensors to test the assessment methodology. In addition, we developed a design for the guidance system, built a prototype, and tested the feasibility. RESULTS: The resulting architecture of the system was modular, requiring the development of an interoperable description of each component including sensors, guideline steps, medications, analytics, resources, and the context of care. A Knowledge Base was created to describe the interactions of the modules. A prototype test set-up demonstrated the feasibility of the system in that simulated physiological inputs would mimic the guidance provided by the current Clinical Practice Guidelines for Traumatic Brain Injury in Prolonged Care (CPG ID:63). The Value-Burden analysis yielded a ranking of sensors as well as sensor metadata useful in the Knowledge Base. CONCLUSION: We developed a design and tested the feasibility of a system that would allow the use of physiological biomarkers as a management tool in forward care. A key feature is the modular design that allows the system to adapt to changes in sensors, resources, and context as well as to updates in guidelines as they are developed. Continued work consists of further validation of the concept with simulated scenarios.

Non-Contact Measurement of Cardiopulmonary Activity Using Software Defined Radios.

Vital signs are important indicators to evaluate the health status of patients. Channel state information (CSI) can sense the displacement of the chest wall caused by cardiorespiratory activity in a non-contact manner. Due to the influence of clutter, DC components, and respiratory harmonics, it is difficult to detect reliable heartbeat signals. To address this problem, this paper proposes a robust and novel method for simultaneously extracting breath and heartbeat signals using software defined radios (SDR). Specifically, we model and analyze the signal and propose singular value decomposition (SVD)-based clutter suppression method to enhance the vital sign signals. The DC is estimated and compensated by the circle fitting method. Then, the heartbeat signal and respiratory signal are obtained by the modified variational modal decomposition (VMD). The experimental results demonstrate that the proposed method can accurately separate the respiratory signal and the heartbeat signal from the filtered signal. The Bland-Altman analysis shows that the proposed system is in good agreement with the medical sensors. In addition, the proposed system can accurately measure the heart rate variability (HRV) within 0.5m. In summary, our system can be used as a preferred contactless alternative to traditional contact medical sensors, which can provide advanced patient-centered healthcare solutions.

Telemonitoring in Non-invasive Ventilation.

Telemonitoring in non-invasive ventilation is constantly evolving to enable follow-up of adults and children. Depending on the device and manufacturer, different ventilator variables are displayed on web-based platforms. However, high-granularity measurement is not always available remotely, which precludes breath-by-breath waveforms and precise monitoring of nocturnal gas exchange. Therefore, telemonitoring is mainly useful for monitoring utilization of the device, leaks, and respiratory events. Coordinated relationships between patients, homecare providers, and hospital teams are necessary to transform available data into diagnosis and actions. Telemonitoring is time and cost-consuming. The balance between cost, workload, and clinical benefit should be further evaluated.

Bioimpedance sensor array for monitoring chronic wounds: Validation of method feasibility.

In an ageing society, the incidence of hard-to-heal wounds is rising. Chronic wound healing is a complex process, which requires specialised treatment. Clinical assessment of the wound is essential to establish care approaches but is usually based on visual evaluation and it remains challenging. Therefore, innovative quantitative methods for the assessment of chronic wounds are needed. We conducted a single-centre observational study designed to assess the feasibility of a bioimpedance measurement method conducted with a multielectrode sensor array to monitor the wound healing process in patients with chronic wounds of venous, mixed venous-arterial and diabetic aetiology. In total, 104 measurements of bioimpedance were conducted in 18 ulcers during the study. Across all 7 patients analysed, the bioimpedance of the ulcers was consistently increasing as the wound surface was decreasing. The variables had significant (p < 0.001) and strong negative correlation (r = -0.86). We validated the feasibility of the bioimpedance measurement method for the monitoring of the wound healing process on the lower legs. It may be a promising quantitative method for monitoring the status of the wounds. However, long-term measurements are needed to show the usability of the electrode dressing and bioimpedance measurement in the assessment of chronic wounds.

WSe2/chitosan-based wearable multi-functional platform for monitoring electrophysiological signals, pulse rate, respiratory rate, and body movements.

A cleanroom free optimized fabrication of a low-cost facile tungsten diselenide (WSe2) combined with chitosan-based hydrogel device is reported for multifunctional applications including tactile sensing, pulse rate monitoring, respiratory rate monitoring, human body movements detection, and human electrophysiological signal detection. Chitosan being a natural biodegradable, non-toxic compound serves as a substrate to the semiconducting WSe2 electrode which is synthesized using a single step hydrothermal technique. Elaborate characterization studies are performed to confirm the morphological, structural, and electrical properties of the fabricated chitosan/WSe2 device. Chitosan/WSe2 sensor with copper contacts on each side is put directly on skin to capture human body motions. The resistivity of the sample was calculated as 26 kΩ m-1. The device behaves as an ultrasensitive pressure sensor for tactile and arterial pulse sensing with response time of 0.9 s and sensitivity of around 0.02 kPa-1. It is also capable for strain sensing with a gauge factor of 54 which is significantly higher than similar other reported electrodes. The human body movements sensing can be attributed to the piezoresistive character of WSe2 that originates from its non-centrosymmetric structure. Further, the sensor is employed for monitoring respiratory rate which measures to 13 counts/min for healthy individual and electrophysiological signals like ECG and EOG which can be used later for detecting numerous pathological conditions in humans. Electrophysiological signal sensing is carried out using a bio-signal amplifier (Bio-Amp EXG Pill) connected to Arduino. The skin-friendly, low toxic WSe2/chitosan dry electrodes pave the way for replacing wet electrodes and find numerous applications in personalized healthcare.

Wireless ear EEG to monitor drowsiness.

Neural wearables can enable life-saving drowsiness and health monitoring for pilots and drivers. While existing in-cabin sensors may provide alerts, wearables can enable monitoring across more environments. Current neural wearables are promising but most require wet-electrodes and bulky electronics. This work showcases in-ear, dry-electrode earpieces used to monitor drowsiness with compact hardware. The employed system integrates additive-manufacturing for dry, user-generic earpieces, existing wireless electronics, and offline classification algorithms. Thirty-five hours of electrophysiological data were recorded across nine subjects performing drowsiness-inducing tasks. Three classifier models were trained with user-specific, leave-one-trial-out, and leave-one-user-out splits. The support-vector-machine classifier achieved an accuracy of 93.2% while evaluating users it has seen before and 93.3% when evaluating a never-before-seen user. These results demonstrate wireless, dry, user-generic earpieces used to classify drowsiness with comparable accuracies to existing state-of-the-art, wet electrode in-ear and scalp systems. Further, this work illustrates the feasibility of population-trained classification in future electrophysiological applications.

iEat: automatic wearable dietary monitoring with bio-impedance sensing.

Diet is an inseparable part of good health, from maintaining a healthy lifestyle for the general population to supporting the treatment of patients suffering from specific diseases. Therefore it is of great significance to be able to monitor people's dietary activity in their daily life remotely. While the traditional practices of self-reporting and retrospective analysis are often unreliable and prone to errors; sensor-based remote diet monitoring is therefore an appealing approach. In this work, we explore an atypical use of bio-impedance by leveraging its unique temporal signal patterns, which are caused by the dynamic close-loop circuit variation between a pair of electrodes due to the body-food interactions during dining activities. Specifically, we introduce iEat, a wearable impedance-sensing device for automatic dietary activity monitoring without the need for external instrumented devices such as smart utensils. By deploying a single impedance sensing channel with one electrode on each wrist, iEat can recognize food intake activities (e.g., cutting, putting food in the mouth with or without utensils, drinking, etc.) and food types from a defined category. The principle is that, at idle, iEat measures only the normal body impedance between the wrist-worn electrodes; while the subject is doing the food-intake activities, new paralleled circuits will be formed through the hand, mouth, utensils, and food, leading to consequential impedance variation. To quantitatively evaluate iEat in real-life settings, a food intake experiment was conducted in an everyday table-dining environment, including 40 meals performed by ten volunteers. With a lightweight, user-independent neural network model, iEat could detect four food intake-related activities with a macro F1 score of 86.4% and classify seven types of foods with a macro F1 score of 64.2%.

A Review of Patient Bed Sensors for Monitoring of Vital Signs.

The analysis of biomedical signals is a very challenging task. This review paper is focused on the presentation of various methods where biomedical data, in particular vital signs, could be monitored using sensors mounted to beds. The presented methods to monitor vital signs include those combined with optical fibers, camera systems, pressure sensors, or other sensors, which may provide more efficient patient bed monitoring results. This work also covers the aspects of interference occurrence in the above-mentioned signals and sleep quality monitoring, which play a very important role in the analysis of biomedical signals and the choice of appropriate signal-processing methods. The provided information will help various researchers to understand the importance of vital sign monitoring and will be a thorough and up-to-date summary of these methods. It will also be a foundation for further enhancement of these methods.

Smart Sleep Monitoring: Sparse Sensor-Based Spatiotemporal CNN for Sleep Posture Detection.

Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are crucial to prevent the development of ulcers and bedsores. This study presents a novel sparse sensor-based spatiotemporal convolutional neural network (S3CNN) for detecting sleep posture. This S3CNN holistically incorporates a pair of spatial convolution neural networks to capture cardiorespiratory activity maps and a pair of temporal convolution neural networks to capture the heart rate and respiratory rate. Sleep data were collected in actual sleep conditions from 22 subjects using a sparse sensor array. The S3CNN was then trained to capture the spatial pressure distribution from the cardiorespiratory activity and temporal cardiopulmonary variability from the heart and respiratory data. Its performance was evaluated using three rounds of 10 fold cross-validation on the 8583 data samples collected from the subjects. The results yielded 91.96% recall, 92.65% precision, and 93.02% accuracy, which are comparable to the state-of-the-art methods that use significantly more sensors for marginally enhanced accuracy. Hence, the proposed S3CNN shows promise for sleep posture monitoring using sparse sensors, demonstrating potential for a more cost-effective approach.

Unobtrusive Skin Temperature Estimation on a Smart Bed.

The transition from wakefulness to sleep occurs when the core body temperature decreases. The latter is facilitated by an increase in the cutaneous blood flow, which dissipates internal heat into the micro-environment surrounding the sleeper's body. The rise in cutaneous blood flow near sleep onset causes the distal (hands and feet) and proximal (abdomen) temperatures to increase by about 1 °C and 0.5 °C, respectively. Characterizing the dynamics of skin temperature changes throughout sleep phases and understanding its relationship with sleep quality requires a means to unobtrusively and longitudinally estimate the skin temperature. Leveraging the data from a temperature sensor strip (TSS) with five individual temperature sensors embedded near the surface of a smart bed's mattress, we have developed an algorithm to estimate the distal skin temperature with a minute-long temporal resolution. The data from 18 participants who recorded TSS and ground-truth temperature data from sleep during 14 nights at home and 2 nights in a lab were used to develop an algorithm that uses a two-stage regression model (gradient boosted tree followed by a random forest) to estimate the distal skin temperature. A five-fold cross-validation procedure was applied to train and validate the model such that the data from a participant could only be either in the training or validation set but not in both. The algorithm verification was performed with the in-lab data. The algorithm presented in this research can estimate the distal skin temperature at a minute-level resolution, with accuracy characterized by the mean limits of agreement [-0.79 to +0.79 °C] and mean coefficient of determination R2=0.87. This method may enable the unobtrusive, longitudinal and ecologically valid collection of distal skin temperature values during sleep. Therelatively small sample size motivates the need for further validation efforts.

Continuous Temperature Telemonitoring of Patients with COVID-19 and Other Infectious Diseases Treated in Hospital-at-Home: Viture® System Validation.

Body temperature must be monitored in patients receiving Hospital-at-Home (HaH) care for COVID-19 and other infectious diseases. Continuous temperature telemonitoring (CTT) detects fever and patient deterioration early, facilitating decision-making. We performed a validation clinical study assessing the safety, comfort, and impact on healthcare practice of Viture®, a CTT system, compared with a standard digital axillary thermometer in 208 patients with COVID-19 and other infectious diseases treated in HaH at the Navarra University Hospital (HUN). Overall, 3258 pairs of measurements showed a clinical bias of -0.02 °C with limits of agreement of -0.96/+0.92 °C, a 95% acceptance rate, and a mean absolute deviation of 0.36 (SD 0.30) °C. Viture® detected 3 times more febrile episodes and revealed fever in 50% more patients compared with spot measurements. Febrile episodes were detected 7.23 h (mean) earlier and modified the diagnostic and/or therapeutic approach in 43.2% of patients. Viture® was validated for use in a clinical setting and was more effective in detecting febrile episodes than conventional methods.

Fluid-Solid Interaction Analysis for Developing In-Situ Strain and Flow Sensors for Prosthetic Valve Monitoring.

Transcatheter aortic valve implantation (TAVI) was initially developed for adult patients, but there is a growing interest to expand this procedure to younger individuals with longer life expectancies. However, the gradual degradation of biological valve leaflets in transcatheter heart valves (THV) presents significant challenges for this extension. This study aimed to establish a multiphysics computational framework to analyze structural and flow measurements of TAVI and evaluate the integration of optical fiber and photoplethysmography (PPG) sensors for monitoring valve function. A two-way fluid-solid interaction (FSI) analysis was performed on an idealized aortic vessel before and after the virtual deployment of the SAPIEN 3 Ultra (S3) THV. Subsequently, an analytical analysis was conducted to estimate the PPG signal using computational flow predictions and to analyze the effect of different pressure gradients and distances between PPG sensors. Circumferential strain estimates from the embedded optical fiber in the FSI model were highest in the sinus of Valsalva; however, the optimal fiber positioning was found to be distal to the sino-tubular junction to minimize bending effects. The findings also demonstrated that positioning PPG sensors both upstream and downstream of the bioprosthesis can be used to effectively assess the pressure gradient across the valve. We concluded that computational modeling allows sensor design to quantify vessel wall strain and pressure gradients across valve leaflets, with the ultimate goal of developing low-cost monitoring systems for detecting valve deterioration.

Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and Challenges.

The proliferation of wearable technology enables the generation of vast amounts of sensor data, offering significant opportunities for advancements in health monitoring, activity recognition, and personalized medicine. However, the complexity and volume of these data present substantial challenges in data modeling and analysis, which have been addressed with approaches spanning time series modeling to deep learning techniques. The latest frontier in this domain is the adoption of large language models (LLMs), such as GPT-4 and Llama, for data analysis, modeling, understanding, and human behavior monitoring through the lens of wearable sensor data. This survey explores the current trends and challenges in applying LLMs for sensor-based human activity recognition and behavior modeling. We discuss the nature of wearable sensor data, the capabilities and limitations of LLMs in modeling them, and their integration with traditional machine learning techniques. We also identify key challenges, including data quality, computational requirements, interpretability, and privacy concerns. By examining case studies and successful applications, we highlight the potential of LLMs in enhancing the analysis and interpretation of wearable sensor data. Finally, we propose future directions for research, emphasizing the need for improved preprocessing techniques, more efficient and scalable models, and interdisciplinary collaboration. This survey aims to provide a comprehensive overview of the intersection between wearable sensor data and LLMs, offering insights into the current state and future prospects of this emerging field.

[Methods for parturition monitoring in the mare - an overview]. / Methoden der Geburtsüberwachung bei der Stute ­ eine Übersicht.

Various systems are available for birth monitoring in horses, whereby a distinction must be made between methods for more accurate prediction of the date of birth in order to intensify monitoring of the mare in a timely manner as well as methods for detecting individuals that are in labor. Basically, it should be noted that there are almost no studies that compare different methods on the same population of mares. As the time of birth approaches, physiological parameters of mare and fetus change, but their variability is too high to predict the exact parturition time point prospectively. The best method currently available is the detection of a decrease in the pH value and an increase in the calcium concentration of the udder secretions.Continuous camera monitoring is currently the method of choice for the detection of the start of the parturition process. However, the downside of this method is that the recordings have to be evaluated by a human.Recent developments based on the use of artificial intelligence could provide significant improvement. Before these methods are ready for practical use, the combination of camera monitoring and a sensor that is sewn into the vulva and activated during the opening phase of parturition is the safest method.

Technological Aspects of Device-Based Monitoring and the Impact on Hospital Services.

This systematic review examines the components of technology used in device-based monitoring and its impact on hospital service use. The most impactful remote monitoring interventions were those measuring vital signs and behavior, while non-implantable devices had less impact on hospital service use.

Telemedicine Alert API Engine System for Vital Signs.

Telemedicine is used to assist and support remote medical care for patients. Our objective was to build up a REST Webservices alert engine that receives clinical parameters from patients of vital signs and basic laboratories to monitor patients remotely. We built a REST API using FHIR, so it can interoperate with other applications, send data to be processed, and receive a response. If the API detects a health risk situation, it sends an alert about the medical parameters that are controlled. The results of the processed data, news and alert, can return synchronously or asynchronously, at the same time that the data to be processed is being sent. The alerts generated can be automatically sent to a web service, mail or WhatsApp of the physician. The alert message comes out as normal, low, medium and high risk. The presented approach establishes communication that enables timely health information exchange. We conducted an experiment (with fictitious data) where we sent several queries by Postman. Finally, we evaluated the communication to be successful by manual checking. The use of the API significantly improves the monitoring of chronic patients. Many works show the effectiveness of telemedicine to improve the control of certain chronic diseases. In addition, telemedicine interventions were also found to significantly improve other health outcomes. Our API enables us to transfer data and produce alerts successfully. This gives us hope that a future with ubiquitous healthcare information interoperability is possible using our system.

An integrated and flexible ultrasonic device for continuous bladder volume monitoring.

Bladder volume measurement is critical for early detection and management of lower urinary tract dysfunctions. Current gold standard is invasive, and alternative technologies either require trained personnel or do not offer medical grade information. Here, we report an integrated wearable ultrasonic bladder volume monitoring device for accurate and autonomous continuous monitoring of the bladder volume. The device incorporates flexible and air-backed ultrasonic transducers and miniaturized control electronics with wireless data transmission capability. We demonstrate the real-life application of the device on healthy volunteers with various bladder shapes and sizes with high accuracy. Apart from the lower urinary tract dysfunctions, the proposed technology could also be adapted for various wearable ultrasonic applications.

Reliable Contactless Monitoring of Heart Rate, Breathing Rate, and Breathing Disturbance During Sleep in Aging: Digital Health Technology Evaluation Study.

BACKGROUND: Longitudinal monitoring of vital signs provides a method for identifying changes to general health in an individual, particularly in older adults. The nocturnal sleep period provides a convenient opportunity to assess vital signs. Contactless technologies that can be embedded into the bedroom environment are unintrusive and burdenless and have the potential to enable seamless monitoring of vital signs. To realize this potential, these technologies need to be evaluated against gold standard measures and in relevant populations. OBJECTIVE: We aimed to evaluate the accuracy of heart rate and breathing rate measurements of 3 contactless technologies (2 undermattress trackers, Withings Sleep Analyzer [WSA] and Emfit QS [Emfit]; and a bedside radar, Somnofy) in a sleep laboratory environment and assess their potential to capture vital signs in a real-world setting. METHODS: Data were collected from 35 community-dwelling older adults aged between 65 and 83 (mean 70.8, SD 4.9) years (men: n=21, 60%) during a 1-night clinical polysomnography (PSG) test in a sleep laboratory, preceded by 7 to 14 days of data collection at home. Several of the participants (20/35, 57%) had health conditions, including type 2 diabetes, hypertension, obesity, and arthritis, and 49% (17) had moderate to severe sleep apnea, while 29% (n=10) had periodic leg movement disorder. The undermattress trackers provided estimates of both heart rate and breathing rate, while the bedside radar provided only the breathing rate. The accuracy of the heart rate and breathing rate estimated by the devices was compared with PSG electrocardiogram-derived heart rate (beats per minute) and respiratory inductance plethysmography thorax-derived breathing rate (cycles per minute), respectively. We also evaluated breathing disturbance indexes of snoring and the apnea-hypopnea index, available from the WSA. RESULTS: All 3 contactless technologies provided acceptable accuracy in estimating heart rate (mean absolute error <2.12 beats per minute and mean absolute percentage error <5%) and breathing rate (mean absolute error ≤1.6 cycles per minute and mean absolute percentage error <12%) at 1-minute resolution. All 3 contactless technologies were able to capture changes in heart rate and breathing rate across the sleep period. The WSA snoring and breathing disturbance estimates were also accurate compared with PSG estimates (WSA snore: r2=0.76; P<.001; WSA apnea-hypopnea index: r2=0.59; P<.001). CONCLUSIONS: Contactless technologies offer an unintrusive alternative to conventional wearable technologies for reliable monitoring of heart rate, breathing rate, and sleep apnea in community-dwelling older adults at scale. They enable the assessment of night-to-night variation in these vital signs, which may allow the identification of acute changes in health, and longitudinal monitoring, which may provide insight into health trajectories. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.3390/clockssleep6010010.

Continuous Monitoring by a Wearable Sensor Did Not Enhance Discharge Decision-Making in an Acute Admission Ward: Results of a Randomized Controlled Trial.

Adding continuous monitoring to usual care at an acute admission ward did not have an effect on the proportion of patients safely discharged. Implementation challenges of continuous monitoring may have contributed to the lack of effect observed.