A Smart Sensing Technologies-Based Intelligent Healthcare System for Diabetes Patients.

An Artificial Intelligence (AI)-enabled human-centered smart healthcare monitoring system can be useful in life saving, specifically for diabetes patients. Diabetes and heart patients need real-time and remote monitoring and recommendation-based medical assistance. Such human-centered smart healthcare systems can not only provide continuous medical assistance to diabetes patients but can also reduce overall medical expenses. In the last decade, machine learning has been successfully implemented to design more accurate and precise medical applications. In this paper, a smart sensing technologies-based architecture is proposed that uses AI and the Internet of Things (IoT) for continuous monitoring and health assistance for diabetes patients. The designed system senses various health parameters, such as blood pressure, blood oxygen, blood glucose (non-invasively), body temperature, and pulse rate, using a wrist band. We also designed a non-invasive blood sugar sensor using a near-infrared (NIR) sensor. The proposed system can predict the patient's health condition, which is evaluated by a set of machine learning algorithms with the support of a fuzzy logic decision-making system. The designed system was validated on a large data set of 50 diabetes patients. The results of the simulation manifest that the random forest classifier gives the highest accuracy in comparison to other machine learning algorithms. The system predicts the patient's condition accurately and sends it to the doctor's portal.

An Intelligent Fire Warning Application Using IoT and an Adaptive Neuro-Fuzzy Inference System.

In the recent past, a few fire warning and alarm systems have been presented based on a combination of a smoke sensor and an alarm device to design a life-safety system. However, such fire alarm systems are sometimes error-prone and can react to non-actual indicators of fire presence classified as false warnings. There is a need for high-quality and intelligent fire alarm systems that use multiple sensor values (such as a signal from a flame detector, humidity, heat, and smoke sensors, etc.) to detect true incidents of fire. An Adaptive neuro-fuzzy Inference System (ANFIS) is used in this paper to calculate the maximum likelihood of the true presence of fire and generate fire alert. The novel idea proposed in this paper is to use ANFIS for the identification of a true fire incident by using change rate of smoke, the change rate of temperature, and humidity in the presence of fire. The model consists of sensors to collect vital data from sensor nodes where Fuzzy logic converts the raw data in a linguistic variable which is trained in ANFIS to get the probability of fire occurrence. The proposed idea also generates alerts with a message sent directly to the user's smartphone. Our system uses small size, cost-effective sensors and ensures that this solution is reproducible. MATLAB-based simulation is used for the experiments and the results show a satisfactory output.

Obstetric and neonatal outcomes of maternal vitamin D supplementation: results of an open-label, randomized controlled trial of antenatal vitamin D supplementation in Pakistani women.

OBJECTIVE: The objective of the study was to determine whether vitamin D (vitD) supplementation during pregnancy affects obstetric and neonatal outcomes. SETTING: The study was conducted at a university hospital in Karachi, Pakistan. METHODS: The study was a single-center, open-label, randomized, controlled trial of routine care (group A, 200 mg ferrous sulfate and 600 mg calcium daily) vs vitD supplementation (group B, 4000 IU vitamin D3 daily), started at 20 weeks and continued till delivery. Maternal serum samples of 25-hydroxyvitamin D (25OHD) were collected at baseline and delivery. Neonatal vitD status was assessed in cord blood or in neonatal serum samples within 48 hours of birth. Obstetric outcomes included gestational hypertension, gestational diabetes, and preterm labor, and neonatal well-being included small for gestational age, birth weight, length, head circumference, and 1- and 5-minute Apgar scores. RESULTS: Of 207 gravidae enrolled, 193 completed the trial. Maternal age, vitD status, and gestational age at enrollment were comparable between the two groups. At delivery, maternal 25OHD was increased in group B (18.3 ± 11 ng/dL vs 8.82 ± 11.84 ng/dL (P = .001) compared with group A (6.9 ± 7.0 ng/dL vs 6.32 ± 3.97 ng/dL, P = .06). The obstetric outcomes were comparable between the two groups (P > .05). Neonatal 25OHD levels were significantly higher in group B compared with group A (19.22 ± 12.19 ng/dL vs 6.27 ± 5.2 ng/dL). There was positive correlation between maternal and neonatal 25OHD levels (r = 0.83, P = .001). One- and 5-minute Apgar scores were significantly higher in group B (7.10 ± 0.66 vs 6.90 ± 0.50, P = .026, and 8.53 ± 0.68 vs 8.33 ± 0.81, P = .051, respectively). Neonatal anthropometric parameters were comparable between the two groups (P > .05). CONCLUSION: Maternal vitD supplementation improved maternal and neonatal vitD status.