A Decision-Aware Ambient Assisted Living System with IoT Embedded Device for In-Home Monitoring of Older Adults.

Older adults' independent life is compromised due to various problems, such as memory impairments and decision-making difficulties. This work initially proposes an integrated conceptual model for assisted living systems capable of providing helping means for older adults with mild memory impairments and their caregivers. The proposed model has four main components: (1) an indoor location and heading measurement unit in the local fog layer, (2) an augmented reality (AR) application to make interactions with the user, (3) an IoT-based fuzzy decision-making system to handle the direct and environmental interactions with the user, and (4) a user interface for caregivers to monitor the situation in real time and send reminders once required. Then, a preliminary proof-of-concept implementation is performed to evaluate the suggested mode's feasibility. Functional experiments are carried out based on various factual scenarios, which validate the effectiveness of the proposed approach. The accuracy and response time of the proposed proof-of-concept system are further examined. The results suggest that implementing such a system is feasible and has the potential to promote assisted living. The suggested system has the potential to promote scalable and customizable assisted living systems to reduce the challenges of independent living for older adults.

Decoding covert visual attention based on phase transfer entropy.

Covert attention to spatial and color features in the visual field is a relatively new control signal for brain-computer interfaces (BCI). To guide the processing resources to the related visual scene aspects, covert attention should be decoded from human brain. Here, a novel expert system is designed to decode covert visual attention based on the EEG signal provided from 15 subjects during a new task based on a change in lumination to two blue and orange color on the right and the left side of the screen, which is evaluated in two cases of binary and multi-class systems. For the first time, Phase transfer entropy (PTE) has been used in these systems, and after selecting the optimal decoding feature, the frequency band (8-13 Hz) Alpha and Beta1 (13-20 Hz) have the best performance compared to other frequency bands. Two-class classification accuracies of the designed system in two frequency bands (Alpha and Beta1) are 91.87% and 89.53%, respectively. Also, the accuracies are 65.11% and 63.38% for multi-class classification in specified frequency bands. In these frequency bands, the parietal and frontal lobes showed the most significant difference in comparison to the other lobes. Also, the obtained results declared that the expert system's performance in the Alpha band by the extracted features from the Posterior region is better than all frequency bands in other different brain regions. The performance of the proposed expert system by PTE is significantly better than the previous phase synchronization based features. Results have shown that the PTE feature performs better than the common methods for decoding covert visual attention.

Computer Aided Diagnosis System for multiple sclerosis disease based on phase to amplitude coupling in covert visual attention.

BACKGROUND AND OBJECTIVE: Computer Aided Diagnosis (CAD) techniques have widely been used in research to detect the neurological abnormalities and improve the consistency of diagnosis and treatment in medicine. In this study, a new CAD system based on EEG signals was developed. The motivation for the development of the CAD system was to diagnose multiple sclerosis (MS) disease during covert visual attention tasks. It is worth noting that research of this kind on the efficacy of attention tasks is limited in scope for MS patients; therefore, it is vital to develop a feature of EEG to characterize the patient's state with high sensitivity and specificity. METHODS: We evaluated the use of phase-amplitude coupling (PAC) of EEG signals to diagnose MS. It is assumed that the role of PAC for information encoding during visual attention in MS is greatly unknown; therefore, we made an attempt to investigate it via CAD systems. The EEG signals were recorded from healthy and MS patients while performing new visual attention tasks. Machine learning algorithms were also used to identify the EEG signals as to whether the disease existed or not. The challenge regarding the dimensionality of the extracted features was addressed through selecting the relevant and efficient features using T-test and Bhattacharyya distance criteria, and the validity of the system was assessed through leave-one-subject-out cross-validation method. RESULTS: Our findings indicated that online sequential extreme learning machine (OS-ELM) classifier with T-test feature selection method yielded peak accuracy, sensitivity and specificity in both color and direction tasks. These values were 91%, 83% and 96% for color task, and 90%, 82% and 96% for the direction task. CONCLUSIONS: Based on the results, it can be concluded that this procedure can be used for the automatic diagnosis of early MS, and can also facilitate the treatment assessment in patients.

Exposure to Electromagnetic Fields of High Voltage Overhead Power Lines and Female Infertility.

BACKGROUND: Living in the vicinity of high voltage power lines has brought about a range of health woes, but the effect of residential exposure to electromagnetic fields from the power lines on female fertility has not been explored yet. OBJECTIVE: To test the hypothesis if residential proximity to high voltage power lines could be associated with the increased risk of female infertility. METHODS: In a case-control study, 462 women with confirmed diagnosis of unexplained infertility or behavioral and environmental factors were assessed between February 2014 and December 2016. Control group comprised of 471 persons with no history of infertility selected using randomized-digit dialing from the numbers registered in a birth registry between 2014 and 2016. The nearest linear distance from high voltage power lines to the participants' residence of cases and controls was measured using a Geographical Information System (GIS) and Google Earth aerial evaluation for high voltage power lines (240-400 kV). RESULTS: 112 (14.1%) houses were within 500 meters from a high voltage power line. Women living within 500 meters of the lines (OR 4.14, 95% CI 2.61 to 6.57) and 500-1000 meters of the line (OR 1.61, 95% CI 1.05 to 2.47) carried a significantly higher risk of infertility than those women living more than 1000 meters away from the power lines. After adjusting for confounding factors, women living within 500 meters of the lines carried a higher risk (aOR 4.44, 95% CI 2.77 to 7.11) of infertility compared with women living more than 1000 meters of the lines. CONCLUSION: The current safety guidelines for electromagnetic fields exposure seems to be not adequate for protecting people from the hazardous effects of the field.

Preterm birth among women living within 600 meters of high voltage overhead Power Lines: a case-control study.

AIM: The issue of preterm birth due to exposure to magnetic fields from power lines is unclear. Exposure to electromagnetic field in uterus has been hypothesized as possible preterm birth. The aim of the present study was to determine whether living closer to high voltage power lines increased the risk of preterm labor. METHODS: In a nested case-control study, 135 cases of singleton live spontaneous preterm birth in Rohani hospital, Babol, Iran, during the period between 2013 and 2014 were studied. The 150 control subjects were singleton term live birth in the same year of birth and city of residence using randomized-digit dialing. The shortest distance to any of the high voltage power lines to the maternal residence during pregnancy was measured using ArcGIS software for every case and control. To test the association between the preterm births and the residential proximity to power lines, stepwise multiple logistic regression was used. RESULTS: There were 28 households, 20 cases (14.8%) and 8 controls (5.3%) situated within 600 meters of high voltage power lines. The adjusted OR for spontaneous preterm birth and birth defect in women who were living in less than 600 meters from high voltage power lines was higher compared to those living at farther distance (OR = 3.28, CI: 1.37 to 7.85) and (OR = 5.05, CI: 1.52 to 16.78), respectively. CONCLUSIONS: Therefore, installing overhead power lines and stations within 600 meters or making overhead underground would be useful in the prevention of both preterm birth and birth defect.