Patient Monitoring Applications Research Articles

CRUSE® -An innovative mobile application for patient monitoring and management in chronic spontaneous urticaria.

Chronic spontaneous urticaria (CSU) is unpredictable and can severely impair patients' quality of life. Patients with CSU need a convenient, user-friendly platform to complete patient-reported outcome measures (PROMs) on their mobile devices. CRUSE® , the Chronic Urticaria Self Evaluation app, aims to address this unmet need. CRUSE® was developed by an international steering committee of urticaria specialists. Priorities for the app based on recent findings in CSU were defined to allow patients to track and record their symptoms and medication use over time and send photographs. The CRUSE® app collects patient data such as age, sex, disease onset, triggers, medication, and CSU characteristics that can be sent securely to physicians, providing real-time insights. Additionally, CRUSE® contains PROMs to assess disease activity and control, which are individualised to patient profiles and clinical manifestations. CRUSE® was launched in Germany in March 2022 and is now available for free in 17 countries. It is adapted to the local language and displays a country-specific list of available urticaria medications. English and Ukrainian versions are available worldwide. From July 2022 to June 2023, 25,710 observations were documented by 2540 users; 72.7% were females, with a mean age of 39.6years. At baseline, 93.7% and 51.3% of users had wheals and angioedema, respectively. Second-generation antihistamines were used in 74.0% of days. The initial data from CRUSE® show the wide use and utility of effectively tracking patients' disease activity and control, paving the way for personalised CSU management.

Distance Energy-Efficient Soft Computing Model for Data Forwarding in Healthcare Sensor Network

Data forwarding is a crucial process in computer networking and telecommunications. It involves the transmission of data packets from a source to a destination within a network. The forwarding process is fundamental for enabling communication between devices on different segments of a network and plays a vital role in maintaining the overall performance and reliability of the network infrastructure. This paper presents the development and evaluation of the Threshold Congestion Data Forwarding Soft Computing (TCDF-SC) algorithm, designed for healthcare sensor networks. The algorithm combines threshold-based congestion management with soft computing techniques to enhance data forwarding efficiency in dynamic healthcare environments. Through extensive simulations, TCDF-SC demonstrates scalability, achieving high total packets forwarded and maintaining a reliable packet delivery ratio as the network scales. The algorithm minimizes average transmission delay and optimizes energy consumption, ensuring timely and energy-efficient data transmission. Comparative analysis against existing protocols, LEACH and BCP, highlights TCDF-SC’s superior performance across key metrics, positioning it as a promising solution for healthcare sensor networks. This research contributes to the advancement of data transmission optimization in healthcare, addressing critical requirements of reliability, energy efficiency, and adaptability in dynamic healthcare settings. Further validation and real-world experimentation will enhance the algorithm’s applicability in diverse healthcare scenarios, fostering advancements in patient monitoring and healthcare applications.

Revolutionizing Healthcare through Health Monitoring Applications with Wearable Biomedical Devices

The Internet of Things (IoT) has revolutionized the connectivity and communication of tangible objects, and it serves as a versatile and cost-effective solution in the healthcare sector, particularly in regions with limited healthcare infrastructure. This research explores the application of sensors such as LM35, AD8232, and MAX30100 for the detection of vital health indicators, including body temperature, pulse rate, electrocardiogram (ECG), and oxygen saturation levels, with data transmission through IoT cloud, offering real-time parameter access via an Android application for non-invasive remote patient monitoring. The study aims to expand healthcare services to various settings, such as hospitals, commercial areas, educational institutions, workplaces, and residential neighborhoods. After the COVID-19 pandemic, IoT-enabled continuous monitoring of critical health metrics such as temperature and pulse rate has become increasingly crucial for early illness detection and efficient communication with healthcare providers. Our low-cost wearable device, which includes ECG monitoring, aims to bridge the accessibility gap for people with limited financial resources, with the primary goal of providing efficient healthcare solutions to underserved rural areas while also contributing valuable data to future medical research. Our proposed system is a low-cost, high-efficiency solution that outperforms existing systems in healthcare data collection and patient monitoring. It improves access to vital health data and shows economic benefits, indicating a significant advancement in healthcare technology.

Skin-color-independent robust assessment of capillary refill time.

Capillary Refill Time (CRT) assesses peripheral perfusion in resource-limited settings. However, the repeatability and reproducibility of CRT measurements are limited for individuals with darker skin. This paper presents quantitative CRT measurements demonstrating good performance and repeatability across all Fitzpatrick skin phototypes. The study involved 22 volunteers and utilized controlled compression at 7 kPa, an RGB video camera, and cocircular polarized white LED light. CRT was determined by calculating the time constant of an exponential regression applied to the mean pixel intensity of the green (G) channel. An adaptive algorithm identifies the optimal regression region for noise reduction, and flags inappropriate readings. The results indicate that 80% of the CRT readings fell within a 20% range of the expected CRT value. The repetition standard deviation was 17%. These findings suggest the potential for developing reliable and reproducible quantitative CRT methods for robust measurements in patient triage, monitoring, and telehealth applications.

Guest Editorial Innovations in Wearable, Implantable, Mobile, & Remote Healthcare With IoT & Sensor Informatics and Patient Monitoring

The papers presented in this special issue focus on technological innovations in wearable, implantable, mobile, and remote healthcare that include Internet of Things (IoT), sensor informatics, and patient monitoring applications. These new technologies are used to track the key signs of people’s health to improve their lifestyle and health disorders. Innovations in IoT devices play a vital role in assisting patients in managing their health conditions. Thanks to the advent of modern communication technologies and Internet of Things (IoT) paradigms that have made the implementation of biomedical devices nearly universal. Now with the evolving industrial revolution, patients and healthcare providers are expecting something more. Practically speaking, healthcare wearables have experienced tremendous growth in the past few years, and it is expected to grow even more shortly, making it an ideal space for the biomedical informatics research community to solve complex healthcare problems and more informed decision making to improve human health.

AAL and Internet of Medical Things for Monitoring Type-2 Diabetic Patients.

Due to an aging population, assisted-care options are required so that senior citizens may maintain their independence at home for a longer time and rely less on caretakers. Ambient Assisted Living (AAL) encourages the creation of solutions that can help to optimize the environment for senior citizens with assistance while greatly reducing their challenges. A framework based on the Internet of Medical Things (IoMT) is used in the current study for the implementation of AAL technology to help patients with Type-2 diabetes. A glucose oxide sensor is used to monitor diabetic elderly people continuously. Spectrogram images are created from the recorded data from the sensor to assess and detect aberrant glucose levels. DenseNet-169 examines and analyzes the spectrogram pictures, and messages are sent to caregivers when aberrant glucose levels are detected. The current work describes both the spectrogram image analysis and the signal-to-spectrogram generating method. The study presents a future perspective model for a mobile application for real-time patient monitoring. Benchmark metrics evaluate the application's performances, including sensitivity, specificity, accuracy, and F1-score. Several cross--validations are used to evaluate the model's performance. The findings demonstrate that the proposed model can correctly identify patients with abnormal blood glucose levels.

Improved Patient Monitoring with a Novel Multisensory Smartwatch Application.

The design of medical alarms has been heavily criticized in the past decade. Auditory medical alarms have poor learnability, discernibility, and relevance, leading to poor patient outcomes, and alarm fatigue, and overall poor informatic system design. We developed a novel trimodal patient monitoring smartwatch application for patient monitoring. Participants completed two phases: (1) control and (2) our novel trimodal system while identifying alarms (heart rate, oxygenation, and blood pressure) and completing a cognitively demanding task. Alarms were auditory icons presented as either solo or co-alarms. Participant performance was assessed by accuracy and response time (RT) of alarm identification. Using the novel system, accuracy was significantly improved overall (p < 0.01) and in co-alarm situations (p < 0.01), but not for solo alarms (p = 0.484). RT was also significantly faster (p < 0.01) while using the novel system for all alarm types. Participants reported decreased mental workload using the novel system. This feasibility study shows that our novel alarm system performs better than current standards. Improvements in accuracy, RT and perceived mental workload indicate the potential of this system to have a positive impact on medical informatic systems and clinical monitoring, for both the patient and the clinician.

Harnessing Digital Health to Objectively Assess Functional Performance in Veterans with Chronic Obstructive Pulmonary Disease

Introduction: An early detection of impaired functional performance is critical to enhance symptom management for patients with chronic obstructive pulmonary disease (COPD). However, conventional functional measures based on walking assessments are often impractical for small clinics where the available space to administrate gait-based test is limited. This study examined the feasibility and effectiveness of an upper-extremity frailty meter (FM) in identifying digital measures of functional performance and assessing frailty in COPD patients. Methods: Forty-eight patients with COPD (age = 68.8 ± 8.5 years, body mass index [BMI] = 28.7 ± 5.8 kg/m²) and 49 controls (age = 70.0 ± 3.0 years, BMI = 28.7 ± 6.1 kg/m²) were recruited. All participants performed a 20-s repetitive elbow flexion-extension test using a wrist-worn FM sensor. Functional performance was quantified by FM metrics, including speed (slowness), range of motion (rigidity), power (weakness), flexion and extension time (slowness), as well as speed and power reduction (exhaustion). Conventional functional measures, including timed-up-and-go test, gait and balance tests, and 5 repetition sit-to-stand test, were also performed. Results: Compared to controls, COPD patients exhibited deteriorated performances in all conventional functional assessments (d = 0.64–1.26, p < 0.010) and all FM metrics (d = 0.45–1.54, p < 0.050). FM metrics had significant agreements with conventional assessment tools (|r| = 0.35–0.55, p ≤ 0.001). FM metrics efficiently identified COPD patients with pre-frailty and frailty (d = 0.82–2.12, p < 0.050). Conclusion: This study proposes the feasibility of using a 20-s repetitive elbow flexion-extension test and wrist-worn sensor-derived frailty metrics as an alternative and practical solution to evaluate functional performance in COPD patients. Its simplicity and low risk for test administration may also facilitate its application for remote patient monitoring. Furthermore, in settings where the administration of walking test is impractical, for example, when ventilator support is needed or space is limited, FM may be used as an alternative solution. Future studies are encouraged to use the FM to quantitatively monitor the progressive decline in functional performance and quantify outcomes of rehabilitation interventions.

Automatic eye localization for hospitalized infants and children using convolutional neural networks

BackgroundReliable localization and tracking of the eye region in the pediatric hospital environment is a significant challenge for clinical decision support and patient monitoring applications. Existing work in eye localization achieves high performance on adult datasets but performs poorly in the busy pediatric hospital environment, where face appearance varies because of age, position and the presence of medical equipment. MethodsWe developed two new datasets: a training dataset using public image data from internet searches, and a test dataset using 59 recordings of patients in a pediatric intensive care unit. We trained two eye localization models, using the Faster R-CNN algorithm to fine-tune a pre-trained ResNet base network, and evaluated them using the images from the pediatric ICU. ResultsThe convolutional neural network trained with a combination of adult and child data achieved an 79.7% eye localization rate, significantly higher than the model trained on adult data alone. With additional pre-processing to equalize image contrast, the localization rate rises to 84%. ConclusionThe results demonstrate the potential of convolutional neural networks for eye localization and tracking in a pediatric ICU setting, even when training data is limited. We obtained significant performance gains by adding task-specific images to the training dataset, highlighting the need for custom models and datasets for specialized applications like pediatric patient monitoring. The moderate size of our added training dataset shows that it is feasible to develop an internal training dataset for clinical computer vision applications, and apply it with transfer learning to fine-tune existing pre-trained models.

Emotion Recognition Based on DEAP Database using EEG Time-Frequency Features and Machine Learning Methods

In recent years, research of the human emotional state is becoming importance, especially in its application for patient monitoring and in the treatment management system of that patient. In this paper, an EEG based emotion recognition system is developed that consists of a feature extraction subsystem and a classifier subsystem. As better performance of the feature extraction subsystem may produce higher recognition accuracy, nine features derived from the time and frequency domain from the EEG signal is used and analyzed. We have utilized support vector machine and Random Forest methods for classifying the emotional state of the subject, and compare its results with other machine learning methods. Using two-fold data validation model, the experiment result shows that the highest recognition accuracy is produced by using Random Forest method, i.e., 62.58%.

Remote Physical Frailty Monitoring-The Application of Deep Learning-Based Image Processing in Tele-Health.

Remote screening physical frailty (PF) may assist in triaging patients with chronic obstructive pulmonary disease (COPD) who are in clinical priorities to visit a clinical center for preventive care. Conventional PF assessment tools have however limited feasibility for remote patient monitoring applications. To improve the safety of PF assessment, we previously developed and validated a quick and safe PF screening tool called Frailty Meter (FM). FM works by quantifying weakness, slowness, rigidity, and exhaustion during a 20-second repetitive elbow flexion/extension task using a wrist-worn sensor and generates a frailty index (FI) ranging from zero to one; higher values indicate progressively greater severity of frailty. However, the use of wrist-sensor limits its applications in telemedicine and remote patient monitoring. In this study, we developed a sensor-less FM based on deep learning-based image processing, which can be easily integrated into mobile health and enables remote assessment of physical frailty. The sensor-less FM extracts kinematic features of the forearm motion from the video of 20-second elbow flexion and extension recorded by a tablet camera, and then calculates frailty phenotypes and FI. To test the validity of sensor-less FM, 11 COPD patients admitted to a Telehealth pulmonary rehabilitation clinic and 10 healthy young volunteers (controls) were recruited. All participants completed the test indicating high feasibility. Strong correlations (0.72 < r < 0.99) were observed between the sensor-based FM and sensor-less FM to extract all frailty phenotypes and FI. After adjusting with age and body mass index(BMI), sensor-less FM enables distinguishing COPD group from controls (p<0.050) with the largest effect sizes observed for weakness (Cohen’s effect size d=2.24), frailty index (d=1.70), and slowness (d=1.70). These pilot findings suggest feasibility and proof of concept validity of this sensor-less FM toward remote assessment of PF in COPD patients.

Pharmacy-based hypertension care employing mHealth in Lagos, Nigeria \u2013 a mixed methods feasibility study

BackgroundAccess to quality hypertension care is often poor in sub-Saharan Africa. Some community pharmacies offer hypertension monitoring services, with and without involvement of medical doctors. To directly connect pharmacy staff and cardiologists a care model including a mobile application (mHealth) for remote patient monitoring was implemented and pilot tested in Lagos, Nigeria. Pharmacists provided blood pressure measurements and counselling. Cardiologists enrolled patients in the pilot program and remotely monitored them, for which patients paid a monthly fee. We evaluated the feasibility of this care model at five private community pharmacies. Outcome measures were retention in care, blood pressure change, quality of care, and patients’ and healthcare providers’ satisfaction with the care model.MethodsPatients participated in the care model’s pilot at one of the five pharmacies for approximately 6–8 months from February 2016. We conducted structured patient interviews and blood pressure measurements at pilot entry and exit, and used exports of the mHealth-application, in-depth interviews and focus group discussions with patients, pharmacists and cardiologists.ResultsOf 336 enrolled patients, 236 (72%) were interviewed at pilot entry and exit. According to the mHealth data 71% returned to the pharmacy after enrollment, with 3.3 months (IQR: 2.2–5.4) median duration of activity in the mHealth-application. Patients self-reported more visits than recorded in the mHealth data. Pharmacists mentioned use of paper records, understaffing, the application not being user-friendly, and patients’ unwillingness to pay as reasons for underreporting. Mean systolic blood pressure decreased 9.9 mmHg (SD: 18). Blood pressure on target increased from 24 to 56% and an additional 10% had an improved blood pressure at endline, however this was not associated with duration of mHealth activity. Patients were satisfied because of accessibility, attention, adherence and information provision.ConclusionPatients, pharmacists and cardiologists adopted the care model, albeit with gaps in mHealth data. Most patients were satisfied, and their mean blood pressure significantly reduced. Usage of the mHealth application, pharmacy incentives, and a modified financing model are opportunities for improvement. In addition, costs of implementation and availability of involved healthcare providers need to be investigated before such a care model can be further implemented.

CEA: Clinical Event Annotator mHealth Application for Real-time Patient Monitoring.

This research develops a novel dynamic mobile health (mHealth) application (app), called the Clinical Event Annotator (CEA). The CEA comprises of a native Android tablet app and an administrative web app. The native app is used at the patient bedside to manually annotate clinical events in real-time. Event types include patient monitor alarms, routine care, clinical interventions, and patient movements. The app can be dynamically updated with user-defined customized events. The web app generates reports of the annotation sessions. The CEA app is developed to support a clinical study that explores the use of pressure-sensitive mats (PSM) in the neonatal intensive care unit (NICU) to detect the respiratory rate (RR), heart rate (HR), and movement of critically ill neonatal patients. High-fidelity CEA app annotations are synced with a backend database that enables integration and synchronization with independently acquired patient monitoring data, such as RR, HR, and contact pressure data from the PSM. The gold standard CEA annotations serve the purpose of retrospectively training machine learning algorithms for clinical event detection. Preliminary test results from use of the app in the clinical study are presented. Development of the CEA app is a unique and novel contribution that addresses the well-known problem of manually annotating physiologic data streams to support clinical data mining applications.

Simulation-based training and OR apprenticeship for medical students : Aprospective, randomized, single-blind study of clinical skills

Simulation-based training (SBT) has developed into an established method of medical training. Studies focusing on the education of medical students have used simulation as an evaluation tool for defined skills. A small number of studies provide evidence that SBT improves medical students' skills in the clinical setting. Moreover, they were strictly limited to a few areas, such as the diagnosis of heart murmurs or the correct application of cricoid pressure. Other studies could not prove adequate transferability from the skills gained in SBT to the patient site. Whether SBT has an effect on medical students' skills in anesthesiology in the clinical setting is controversial. To explore this issue, we designed aprospective, randomized, single-blind trial that was integrated into the undergraduate anesthesiology curriculum of our department during the second year of the clinical phase of medical school. This study intended to explore the effect of SBT on medical students within the mandatory undergraduate anesthesiology curriculum of our department in the operating room with respect to basic skills in anesthesiology. After obtaining ethical approval, the participating students of the third clinical semester were randomized into two groups: the SIM-OR group was trained by a225min long SBT in basic skills in anesthesiology before attending the operating room (OR) apprenticeship. The OR-SIM group was trained after the operating room apprenticeship by SBT. During SBT the students were trained in five clinical skills detailed below. Further, two clinical scenarios were simulated using afull-scale simulator. The students had to prepare the patient and perform induction of anesthesia, including bag-mask ventilation after induction in scenario1 and rapid sequence induction in scenario2. Using the five-point Likert scale, five defined skills were evaluated at defined time points during the study period. 1)application of the safety checklist, 2)application of basic patient monitoring, 3)establishment of intravenous access, 4)bag-and-mask ventilation, and 5)adjustment of ventilatory parameters after the patients' airways were secured. Acumulative score of 5points was defined as the best and acumulative score of 25 as the worst rating for adefined time point. The primary endpoint was the cumulative score after day 1 in the operating room apprenticeship and the difference in cumulative scores from days 1 to 4. Our hypothesis was that the SIM-OR group would achieve abetter score after day 1 in the operating room apprenticeship and would gain alarger increase in score from day 1 to day 4 than the OR-SIM group. 73students were allocated to the OR-SIM group and 70students to the SIM-OR group. There was no significant difference between the two groups after day 1 of the operating room apprenticeship and no difference in increase of the cumulative score from day 1 to day 4 (median of cumulative score on day 1: 'SIM-OR' 11.2points vs. 'OR-SIM' 14.6points; p= 0.067; median of difference from day 1 to day 4: 'SIM-OR' -3.7 vs. 'OR-SIM' -6.4; p= 0.110). With the methods applied, this study could not prove that 225min of SBT before the operating room apprenticeship increased the medical students' clinical skills as evaluated in the operating room. Secondary endpoints indicate that medical students have better clinical skills at the end of the entire curriculum when they have been trained through SBT before the operating room apprenticeship. However, the authors believe that simulator training has apositive impact on students' acquisition of procedural and patient safety skills, even if the methods applied in this study may not mirror this aspect sufficiently.

Real-time seizure prediction using RLS filtering and interpolated histogram feature based on hybrid optimization algorithm of Bayesian classifier and Hunting search

Background and objectivesEpileptic seizure prediction using EEG signal analysis is an important application for drug therapy and pediatric patient monitoring. Time series estimation to obtain the future samples of EEG signal has vital role for detecting seizure attack. In this paper, a novel density-based real-time seizure prediction algorithm based on a trained offline seizure detection algorithm is proposed. MethodsIn the offline seizure detection procedure, after signal preprocessing, histogram-based statistical features are extracted from signal probability distribution. By defining a deterministic polynomial model on the normalized histogram, a novel syntactic feature that is named Interpolated Histogram Feature (IHF) is proposed. Moreover, with this feature, Seizure Distribution Model (SDM) as a descriptor of the seizure and non-seizure signals is presented. By using a novel hybrid optimization algorithm based on Bayesian classifier and Hunting Search (HuS) algorithm, the optimal features are selected. To detect the seizure attacks in the online mode, a Multi-Layer Perceptron (MLP) classifier is trained with the optimal features in the offline procedure. For online prediction, the enhanced Recursive Least Square (RLS) filter is applied to estimate sample-by-sample of the EEG signal. Also, a density-based signal tracking scenario is introduced to update and tune the parameters of RLS filtering algorithm. ResultsOur prediction algorithm is evaluated on 104 hours of EEG signals recorded from 23 pediatric patients. Our online signal prediction algorithm provides the accuracy rate of 86.56% and precision rate of 86.53% simultaneously using the trained MLP classifier from the offline mode. The recall rate of seizure prediction is 97.27% and the false prediction rate of 0.00215 per hour is achieved as well. Ultimately, the future samples of EEG signal are estimated, and the time of seizure signal prediction is also converged to 6.64 seconds. ConclusionIn our proposed real-time algorithm, by implementing a density-based signal tracking scenario, the future samples of signal with suitable time is predicted and the seizure is detected based on the optimal features from the IHF and histogram-based statistical features with acceptable performance.

A Priority based Cross Layer Routing Protocol for healthcare applications

Wireless body area networks (WBANs) represent one of the most promising approaches for improving the quality of life, allowing remote patient monitoring and other healthcare applications. Data dissemination and medium access in a WBAN are critical issues that impact the network reliability, the efficiency and the total energy consumed by the network. In this paper, we propose a Priority-based Cross Layer Routing Protocol (PCLRP) along with a Priority Cross Layer Medium Access Channel protocol (PCLMAC) for healthcare applications.PCLRP combined with PCLMAC ensures reliable traffic dissemination and customized channel access for intra- and inter-body communications. Simulation results show that the proposed protocol achieves customized quality of services and outperforms state of the art existing protocols in terms of power consumption, packet delivery ratio and delay.

Feature Selection and Activity Recognition System Using a Single Triaxial Accelerometer

Activity recognition is required in various applications such as medical monitoring and rehabilitation. Previously developed activity recognition systems utilizing triaxial accelerometers have provided mixed results, with subject-to-subject variability. This paper presents an accurate activity recognition system utilizing a body worn wireless accelerometer, to be used in the real-life application of patient monitoring. The algorithm utilizes data from a single, waist-mounted triaxial accelerometer to classify gait events into six daily living activities and transitional events. The accelerometer can be worn at any location around the circumference of the waist, thereby reducing user training. Feature selection is performed using Relief-F and sequential forward floating search (SFFS) from a range of previously published features, as well as new features introduced in this paper. Relevant and robust features that are insensitive to the positioning of accelerometer around the waist are selected. SFFS selected almost half the number of features in comparison to Relief-F and provided higher accuracy than Relief-F. Activity classification is performed using Naïve Bayes and k-nearest neighbor (k-NN) and the results are compared. Activity recognition results on seven subjects with leave-one-person-out error estimates show an overall accuracy of about 98% for both the classifiers. Accuracy for each of the individual activity is also more than 95%.

Optimal design of energy-efficient and cost-effective wireless body area networks

Wireless Body Area Networks (WBANs) represent one of the most promising approaches for improving the quality of life, allowing remote patient monitoring and other healthcare applications. The deployment of a WBAN is a critical issue that impacts both the network lifetime and the total energy consumed by the network. This work investigates the optimal design of wireless body area networks by studying the joint data routing and relay positioning problem, in order to increase the network lifetime. To this end, we propose a mixed integer linear programming model, the Energy-Aware WBAN Design model, which optimizes the number and location of relays to be deployed and the data routing towards the sink, minimizing both the network installation cost and the energy consumed by wireless sensors and relays. We solve the proposed model in both realistic WBAN scenarios and general topologies, and compare the model performance to the most notable approaches proposed in the literature. Numerical results demonstrate that our model (1) provides a good tradeoff between the energy consumption and the number of installed relays, and (2) designs energy-efficient and cost-effective WBANs in a short computation time, thus representing an interesting framework for the dynamic WBAN design problem.

A Modified IEEE 802.15.4 Superframe Structure for Guaranteed Emergency Handling in Wireless Body Area Network

Wireless Body Area Network (WBAN) is a special purpose personal area network with health-care and other value-added applications. Recently, patient monitoring applications using WBAN have received significant attention. Patient monitoring applications are intended to report any sign of emergency in the monitored patient to the corresponding health personal so that necessary actions can be taken before it is too late. Therefore, the underlying protocol suite in WBAN should ensure that the emergency events will be timely reported. However, WBAN based on IEEE 802.15.4 might not be able to do so. Hence, in this paper, we devise the emergency handling capability in IEEE 802.15.4 MAC. We propose a modified superframe structure for supporting immediate emergency reporting and reliable data transmission. A fraction of inactive period is used opportunistically for three new periods in such a way that temporary switching between the new periods and inactive period occurs only for handling emergency events. We also suggest two different random channel access mechanisms for reporting emergencies. Through the numerical results, we show that the proposed scheme achieves improved latency and throughput in the WBAN environment with compare to IEEE 802.15.4.

A framework for enabling patient monitoring via mobile ad hoc network

A critical component of comprehensive patient monitoring is reliability in communication between the patients and the healthcare professionals without any time and location dependencies. Patient monitoring applications largely rely on infrastructure based wireless networks for signal transmission. However, infrastructure based wireless networks till date, suffer from unpredictable network coverage and have thus been attributed to the unpredictable communication reliability of patient monitoring applications. This research investigates an approach based on leveraging mobile ad hoc network to address the challenge of enhancing communication reliability in the context of patient monitoring. Mobile ad hoc network, formed among patient monitoring devices, has the potential of enhancing network coverage and enabling signal transmission from an area which has low or non-existent coverage from infrastructure based networks. In order to utilize mobile ad hoc network in the context of patient monitoring we propose (1) power management protocols that address the challenge of managing the low battery power of patient monitoring devices while maximizing communication reliability and (2) a framework that models the complex decision logic involved in leveraging mobile ad hoc network for diverse patient monitoring scenarios. Analytical evaluation of the proposed approach supports the premise that mobile ad hoc network formed among patient monitoring devices can enhance the reliability of signal transmission thereby improving the quality of patient monitoring applications. Technical and managerial implications of the research findings and the direction of future research are discussed.