Optimization of Medication Delivery Drone with IoT-Guidance Landing System Based on Direction and Intensity of Light.

This paper presents an optimization of the medication delivery drone with the Internet of Things (IoT)-Guidance Landing System based on direction and intensity of light. The IoT-GLS was incorporated into the system to assist the drone's operator or autonomous system to select the best landing angles for landing. The landing selection was based on the direction and intensity of the light. The medication delivery drone system was developed using an Arduino Uno microcontroller board, ESP32 DevKitC V4 board, multiple sensors, and IoT mobile apps to optimize face detection. This system can detect and compare real-time light intensity from all directions. The results showed that the IoT-GLS has improved the distance of detection by 192% in a dark environment and exhibited an improvement in face detection distance up to 147 cm in a room with low light intensity. Furthermore, a significant correlation was found between face recognition's detection distance, light source direction, light intensity, and light color (p < 0.05). The findings of an optimal efficiency of facial recognition for medication delivery was achieved due to the ability of the IoT-GLS to select the best angle of landing based on the light direction and intensity.

Bioimpedance Vector Analysis in Diagnosing Severe and Non-Severe Dengue Patients.

Real-time monitoring and precise diagnosis of the severity of Dengue infection is needed for better decisions in disease management. The aim of this study is to use the Bioimpedance Vector Analysis (BIVA) method to differentiate between healthy subjects and severe and non-severe Dengue-infected patients. Bioimpedance was measured using a 50 KHz single-frequency bioimpedance analyzer. Data from 299 healthy subjects (124 males and 175 females) and 205 serologically confirmed Dengue patients (123 males and 82 females) were analyzed in this study. The obtained results show that the BIVA method was able to assess and classify the body fluid and cell mass condition between the healthy subjects and the Dengue-infected patients. The bioimpedance mean vectors (95% confidence ellipse) for healthy subjects, severe and non-severe Dengue-infected patients were illustrated. The vector is significantly shortened from healthy subjects to Dengue patients; for both genders the p-value is less than 0.0001. The mean vector of severe Dengue patients is significantly shortened compare to non-severe patients with a p-value of 0.0037 and 0.0023 for males and females, respectively. This study confirms that the BIVA method is a valid method in differentiating the healthy, severe and non-severe Dengue-infected subjects. All tests performed had a significance level with a p-value less than 0.05.

A portable automatic endpoint detection system for amplicons of loop mediated isothermal amplification on microfluidic compact disk platform.

In recent years, many improvements have been made in foodborne pathogen detection methods to reduce the impact of food contamination. Several rapid methods have been developed with biosensor devices to improve the way of performing pathogen detection. This paper presents an automated endpoint detection system for amplicons generated by loop mediated isothermal amplification (LAMP) on a microfluidic compact disk platform. The developed detection system utilizes a monochromatic ultraviolet (UV) emitter for excitation of fluorescent labeled LAMP amplicons and a color sensor to detect the emitted florescence from target. Then it processes the sensor output and displays the detection results on liquid crystal display (LCD). The sensitivity test has been performed with detection limit up to 2.5 × 10(-3) ng/µL with different DNA concentrations of Salmonella bacteria. This system allows a rapid and automatic endpoint detection which could lead to the development of a point-of-care diagnosis device for foodborne pathogens detection in a resource-limited environment.

The theory and fundamentals of bioimpedance analysis in clinical status monitoring and diagnosis of diseases.

Bioimpedance analysis is a noninvasive, low cost and a commonly used approach for body composition measurements and assessment of clinical condition. There are a variety of methods applied for interpretation of measured bioimpedance data and a wide range of utilizations of bioimpedance in body composition estimation and evaluation of clinical status. This paper reviews the main concepts of bioimpedance measurement techniques including the frequency based, the allocation based, bioimpedance vector analysis and the real time bioimpedance analysis systems. Commonly used prediction equations for body composition assessment and influence of anthropometric measurements, gender, ethnic groups, postures, measurements protocols and electrode artifacts in estimated values are also discussed. In addition, this paper also contributes to the deliberations of bioimpedance analysis assessment of abnormal loss in lean body mass and unbalanced shift in body fluids and to the summary of diagnostic usage in different kinds of conditions such as cardiac, pulmonary, renal, and neural and infection diseases.

Smartphone-based solutions for fall detection and prevention: challenges and open issues.

This paper presents a state-of-the-art survey of smartphone (SP)-based solutions for fall detection and prevention. Falls are considered as major health hazards for both the elderly and people with neurodegenerative diseases. To mitigate the adverse consequences of falling, a great deal of research has been conducted, mainly focused on two different approaches, namely, fall detection and fall prevention. Required hardware for both fall detection and prevention are also available in SPs. Consequently, researchers' interest in finding SP-based solutions has increased dramatically over recent years. To the best of our knowledge, there has been no published review on SP-based fall detection and prevention. Thus in this paper, we present the taxonomy for SP-based fall detection and prevention solutions and systematic comparisons of existing studies. We have also identified three challenges and three open issues for future research, after reviewing the existing articles. Our time series analysis demonstrates a trend towards the integration of external sensing units with SPs for improvement in usability of the systems.

Recursive independent component analysis (ICA)-decomposition of ictal EEG to select the best ictal component for EEG source imaging.

OBJECTIVE: This study aimed to present a new ictal component selection technique, named as recursive ICA-decomposition for ictal component selection (RIDICS), for potential application in epileptogenic zone localization. METHODS: The proposed technique decomposes ictal EEG recursively, eliminates a few unwanted components in every recursive cycle, and finally selects the most significant ictal component. Back-projected EEG, regenerated from that component, was used for source estimation. Fifty sets of simulated EEGs and 24 seizures in 8 patients were analyzed. Dipole sources of simulated-EEGs were compared with a known dipole location whereas epileptogenic zones of the seizures were compared with their corresponding sites of successful surgery. The RIDICS technique was compared with a conventional technique. RESULTS: The RIDICS technique estimated the dipole sources at an average distance of 12.86 mm from the original dipole location, shorter than the distances obtained using the conventional technique. Epileptogenic zones of the patients, determined by the RIDICS technique, were highly concordant with the sites of surgery with a concordance rate of 83.33%. CONCLUSIONS: Results show that the RIDICS technique can be a promising quantitative technique for ictal component selection. SIGNIFICANCE: Properly selected ictal component gives good approximation of epileptogenic zone, which eventually leads to successful epilepsy surgery.

The Effectiveness of a Wireless Modular Bed Absence Sensor Device for Fall Prevention among Older Inpatients.

BACKGROUND: Falls and fall-related injuries are increasingly serious issues among elderly inpatients due to population aging. The bed-exit alarm has only previously been evaluated in a handful of studies with mixed results. Therefore, we evaluated the effectiveness of a modular bed absence sensor device (M-BAS) in detecting bed exits among older inpatients in a middle income nation in East Asia. METHODS: Patients aged ≥65 years on an acute geriatric ward who were able to mobilize with or without walking aids and physical assistance were recruited to the study. The total number of alarms and the numbers of true and false alarms were recorded by ward nurses. The M-BAS device is placed across the mattress of all consenting participants. Nurses' workload was assessed using the National Aeronautics and Space Administration-Task Load Index (NASA-TLX) score, while nurses' perceptions were surveyed. RESULTS: The sensitivity of the M-BAS was 100% with a positive predictive value of 68% and a nuisance alarm rate of 31%. There was a significant reduction in total NASA-TLX workload score (mean difference = 14.34 ± 13.96 SD, p < 0.001) at the end of the intervention period. 83% of the nurses found the device useful for falls prevention, 97% found it user friendly, and 87% would use it in future. CONCLUSION: The M-BAS was able to accurately detect bed absence episodes among geriatric inpatients and alert nurses accordingly. The use of the device significantly reduced the total workload score, while the acceptability of the device was high among our nurses. A larger, cluster randomized study to measure actual falls outcome associated with the use of the device is now indicated.

Ictal EEG Source Imaging for Presurgical Evaluation of Refractory Focal Epilepsy.

BACKGROUND: Electroencephalography source imaging (ESI) is a promising tool for localizing the cortical sources of both ictal and interictal epileptic activities. Many studies have shown the clinical usefulness of interictal ESI, but very few have investigated the utility of ictal ESI. The aim of this article is to examine the clinical usefulness of ictal ESI for epileptic focus localization in patients with refractory focal epilepsy, especially extratemporal lobe epilepsy. METHODS: Both ictal and interictal ESI were performed by the use of patient-specific realistic forward models and 3 different linear distributed inverse models. Lateralization as well as concordance between ESI-estimated focuses and single-photon emission computed tomography (SPECT) focuses were assessed. RESULTS: All the ESI focuses (both ictal and interictal) were found lateralized to the same hemisphere as ictal SPECT focuses. Lateralization results also were in agreement with the lesion sides as visualized on magnetic resonance imaging. Ictal ESI results, obtained from the best-performing inverse model, were fully concordant with the same cortical lobe as SPECT focuses, whereas the corresponding concordance rate is 87.50% in case of interictal ESI. CONCLUSIONS: Our findings show that ictal ESI gives fully lateralized and highly concordant results with ictal SPECT and may provide a cost-effective substitute for ictal SPECT.

Liquid density effect on burst frequency in centrifugal microfluidic platforms.

Centrifugal microfluidic platforms are widely used in various advanced processes such as biomedical diagnostics, chemical analysis and drug screening. This paper investigates the effect of liquid density on the burst frequency of the centrifugal microfluidic platform. This effect is experimentally investigated and compared to theoretical values. It is found that increasing the liquid density results in lower burst frequency and it is in agreement with theoretical calculations. Moreover, in this study we proposed the use of the microfluidic CD platform as an inexpensive and simple sensor for liquid density measurements. The proposed liquid sensor requires much less liquid volume (in the range of microliters) compared to conventional density meters. This study presents fundamental work which allows for future advance studies with the aim of designing and fabricating centrifugal microfluidic platforms for more complex tasks such as blood analysis.

Signal quality measures on pulse oximetry and blood pressure signals acquired from self-measurement in a home environment.

Recently, decision support system (DSSs) have become more widely accepted as a support tool for use with telehealth systems, helping clinicians to summarize and digest what would otherwise be an unmanageable volume of data. One of the pillars of a home telehealth system is the performance of unsupervised physiological self-measurement by patients in their own homes. Such measurements are prone to error and noise artifact, often due to poor measurement technique and ignorance of the measurement and transduction principles at work. These errors can degrade the quality of the recorded signals and ultimately degrade the performance of the DSS system, which is aiding the clinician in their management of the patient. Developed algorithms for automated quality assessment for pulse oximetry and blood pressure (BP) signals were tested retrospectively with data acquired from a trial that recorded signals in a home environment. The trial involved four aged subjects who performed pulse oximetry and BP measurements by themselves at their home for ten days, three times per day. This trial was set up to mimic the unsupervised physiological self-measurement as in a telehealth system. A manually annotated "gold standard" (GS) was used as the reference against which the developed algorithms were evaluated after analyzing the recordings. The assessment of pulse oximetry signals shows 95% of good sections and 67% of noisy sections were correctly detected by the developed algorithm, and a Cohen's Kappa coefficient (κ) of 0.58 was obtained in 120 pooled signals. The BP measurement evaluation demonstrates that 75% of the actual noisy sections were correctly classified in 120 pooled signals, with 97% and 91% of the signals correctly identified as worthy of attempting systolic and/or diastolic pressure estimation, respectively, with a mean error and standard deviation of 2.53±4.20 mmHg and 1.46±5.29 mmHg when compared to a manually annotated GS. These results demonstrate the feasibility, and highlight the potential benefit, of incorporating automated signal quality assessment algorithms for pulse oximetry and BP recording within a DSS for telehealth patient management.

Predicting the risk of exacerbation in patients with chronic obstructive pulmonary disease using home telehealth measurement data.

BACKGROUND: The use of telehealth technologies to remotely monitor patients suffering chronic diseases may enable preemptive treatment of worsening health conditions before a significant deterioration in the subject's health status occurs, requiring hospital admission. OBJECTIVE: The objective of this study was to develop and validate a classification algorithm for the early identification of patients, with a background of chronic obstructive pulmonary disease (COPD), who appear to be at high risk of an imminent exacerbation event. The algorithm attempts to predict the patient's condition one day in advance, based on a comparison of their current physiological measurements against the distribution of their measurements over the previous month. METHOD: The proposed algorithm, which uses a classification and regression tree (CART), has been validated using telehealth measurement data recorded from patients with moderate/severe COPD living at home. The data were collected from February 2007 to January 2008, using a telehealth home monitoring unit. RESULTS: The CART algorithm can classify home telehealth measurement data into either a 'low risk' or 'high risk' category with 71.8% accuracy, 80.4% specificity and 61.1% sensitivity. The algorithm was able to detect a 'high risk' condition one day prior to patients actually being observed as having a worsening in their COPD condition, as defined by symptom and medication records. CONCLUSION: The CART analyses have shown that features extracted from three types of physiological measurements; forced expiratory volume in 1s (FEV1), arterial oxygen saturation (SPO2) and weight have the most predictive power in stratifying the patients condition. This CART algorithm for early detection could trigger the initiation of timely treatment, thereby potentially reducing exacerbation severity and recovery time and improving the patient's health. This study highlights the potential usefulness of automated analysis of home telehealth data in the early detection of exacerbation events among COPD patients.

Prediction of chronic obstructive pulmonary disease exacerbation using physiological time series patterns.

Chronic obstructive pulmonary disease (COPD) is responsible for significant morbidity and mortality worldwide. Recent clinical research has indicated a strong association between physiological homeostasis and the onset of COPD exacerbation. Thus the analysis of these variables may yield a means of predicting a COPD exacerbation in the near future. However, the accuracy of existing prediction methods based on statistical analysis of periodic snapshots of physiological variables is still far from satisfactory, due to lack of integration of long-term and interactive effects of the physiological variables. Therefore, developing a relatively accurate method for predicting COPD exacerbation is an outstanding challenge. In this paper, a regression-based machine learning technique was developed, using trend pattern variables extracted from COPD patients' longitudinal physiological records, to classify subjects into "low-risk" and "high-risk" categories, indicating their risk of suffering a COPD exacerbation event. Experimental results from cross validation assessment of the classifier model show an average accuracy of 79.27% using this method.

A guideline-based decision support system for generating referral recommendations from routinely recorded home telehealth measurement data.

The objectives of this paper are to present a guideline-based decision support system (GBDSS) design for supporting patient telehealth management of chronic disease and to test its performance in correctly making referral recommendations using routinely recorded measurement data from home telehealth recordings. The GBDSS has been developed to manage lung disease patients in a home telehealth environment. The system operates by checking the availability of home telehealth measurement data on a daily basis, interprets these data using a rule-based decision tree classification, and ultimately generates referral recommendations based on these measured data. The system has demonstrated discriminative power when applied in the analysis of retrospective telehealth data, as a surrogate for realtime referral generation. To this end a telehealth dataset comprising 16 chronic obstructive pulmonary disease (COPD) patients monitored over a 12 month period was used. It was shown that GBDSS referral recommendations could help reduce the number of cases that required a carer's urgent attention by 72.1%, with 81.9% accuracy, 80.8% specificity and 90.4% sensitivity.