The concentration in the new era of healthcare is the medical Internet of Things (IoT) according to preventive and prediction ( $p^{2}~Health$ ). In large scale and general perspective, the effective parameters from behavioral, ambient, and physiological domains as the most influencing fields of interest in healthcare must be monitored. In personalized healthcare monitoring, wearables are playing an important role in terms of data measurement and collection. We aim at creating a configurable and adaptable platform for comprehensive parameters monitoring, according to the convenient mode of wearability. Hence, we develop an innovative wrist-worn prototype for ambient monitoring and a flexible IoT gateway. The prototype measures the most critical parameters from an ambient domain. In this platform, via IoT gateway as an intermediate hub between the wearables and the IoT server, bidirectional communication between the end user and medics is established in real time. In addition, the physician as the real-time observer of patients are given the possibility to set up the required parameters for measurement through the IoT gateway and activate/deactivate the sensors on the wearables. Therefore, depending on the target investigation, status of patients, requirements, and demands, medics can determine the setup parameters for measurement. Thus, the application of this platform is not limited to specific groups, but widely may be applied whether in daily routine or medical research investigation. Under the flexible IoT gateway, the new wearables can readily be integrated and synchronized into the platform. The users are not restricted to select only one specific product but there are alternatives as well. To support the solution, experimental results are provided.

A Flexible and Pervasive IoT-Based Healthcare Platform for Physiological and Environmental Parameters Monitoring

Dementia, a chronic and progressive cognitive declination of brain function caused by disease or impairment, is becoming more prevalent due to the aging population. A major challenge in dementia is achieving accurate and timely diagnosis. In recent years, neuroimaging with computer-aided algorithms have made remarkable advances in addressing this challenge. The success of these approaches is mostly attributed to the application of machine learning techniques for neuroimaging. In this review paper, we present a comprehensive survey of automated diagnostic approaches for dementia using medical image analysis and machine learning algorithms published in the recent years. Based on the rigorous review of the existing works, we have found that, while most of the studies focused on Alzheimer's disease, recent research has demonstrated reasonable performance in the identification of other types of dementia remains a major challenge. Multimodal imaging analysis deep learning approaches have shown promising results in the diagnosis of these other types of dementia. The main contributions of this review paper are as follows. 1) Based on the detailed analysis of the existing literature, this paper discusses neuroimaging procedures for dementia diagnosis. 2) It systematically explains the most recent machine learning techniques and, in particular, deep learning approaches for early detection of dementia.

https://ieeexplore.ieee.org/ielaam/4664312/8643115/8572804-aam.pdf

Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects

Wireless Sensor Networks for Home Health Care.

Nowadays, a large amount of services are deployed on the edge of the network from the cloud since processing data at the edge can reduce response time and lower bandwidth cost for applications such as healthcare in smart homes. Resource management is very important in the edge computing since it is able to increase the system efficiency and improve the quality of service. A common approach for resource management in edge computing is to assign tasks to the remote cloud or edge devices just according to several factors such as energy, bandwidth consumption, and latency. However, the approach is insufficiently efficient and falls short in meeting the requirements of handling health emergency when being applied in smart homes for healthcare. Possible health emergency needs immediate attention and different health tasks have different priorities to be processed. In this paper, we propose a task scheduling approach called HealthEdge that sets different processing priorities for different tasks based on the collected data on human health status and determines whether a task should run in a local device or a remote cloud in order to reduce its total processing time as much as possible. Based on a real trace from five patients, we conduct a trace-driven experiment to evaluate the performance of HealthEdge in comparison with other methods. The results show that HealthEdge can optimally assign tasks between the network edge and cloud, which can reduce the task processing time, reduce bandwidth consumption and increase local edge workstation utilization.

Healthedge: Task Scheduling for Edge Computing with Health Emergency and Human Behavior Consideration in Smart Homes

In today's world, 46.8 million people suffer from brain related diseases. Dementia is most prevalent of all. In general scenario, a dementia patient lacks proper guidance in searching out the way to return back at his/her home. Thus, increasing the risk of getting damaged at individual-health level. Therefore, it is important to track their movement in more sophisticated manner as possible. With emergence of wearables, GPS sensors and Internet of Things (IoT), such devices have become available in public domain. Smartphone apps support caregiver to locate the dementia patients in real-time. RF, GSM, 3G, Wi-Fi and 4G technology fill the communication gap between patient and caregiver to bring them closer. In this paper, we incorporated 7 most popular wearables for investigation to seek appropriateness for dementia tracking in recent times in systematic manners. We performed an in-depth review of these wearables as per the cost, technology wise and application wise characteristics. A case novel study i.e. IoT-based Force Sensor Resistance enabled System-FSRIoT, has been proposed and implemented to validate the effectiveness of IoT in the domain of smarter dementia patient tracking in wearable form factor. The results show promising aspect of a whole new notion to leverage efficient assistive physio-medical healthcare to the dementia patients and the affected family members to reduce life risks and achieve a better social life.

A Systematic Review and Implementation of IoT-Based Pervasive Sensor-Enabled Tracking System for Dementia Patients

Advances in sensing, wireless communication, and data analytics have enabled various monitoring systems for smart health applications. However, many challenges remain to deploy such systems in actual homes, such as achieving robustness, unobtrusiveness, fault tolerance, privacy, and minimal user burden. This paper presents how these challenges were overcome in the realization and successful prototype deployment of the Behavioral and Environmental Sensing and Intervention (BESI) system. BESI is designed to sense behavioral activities using wearables and monitor environmental parameters with in-home sensors. With such data, behavioral patterns can then be modeled to determine associations with environmental attributes and, when appropriate, real-time notifications or interventions can be made based on these models. Challenges in building platforms with residential deployment constraints are discussed. BESI is currently deployed for an in-home study on dementia, and the results are presented to illustrate data collection procedures and system performance.

BESI: reliable and heterogeneous sensing and intervention for in-home health applications

/pdf/assessing-besi-mobile-application-usability-for-caregivers-494gkgosp8.pdf

Assessing BESI mobile application usability for caregivers of persons with dementia

Disruptions in airline operations are not uncommon and can interrupt smooth and efficient passenger transportation, especially during extreme weather conditions and hurricanes. Airline operations can be severely affected and/or halted for the duration of these phenomena. In order to develop tools to implement proper recovery actions for different stakeholders during a disruption present in an air transportation system, prior hurricane data analysis is crucial. This work focuses on analyzing a large set of airline data during Hurricane Matthew in 2016 to obtain meaningful insights regarding the affected airports and airlines. Our analysis also predicts the number of affected airline passengers during the hurricane. The results of our study show that Orlando International Airport (MCO) and Southwest Airlines were the most affected airport and airline, respectively. Our findings further reveal that certain airline passengers were affected before and after the day of the hurricane.

Harshitha Meda

Papers

BESI: reliable and heterogeneous sensing and intervention for in-home health applications

Assessing BESI mobile application usability for caregivers of persons with dementia

Analysis of Hurricane Matthew 2016 Data to Estimate Airline Passengers Disruption

Integrating Geospatial Information in the Analysis of Network Disruptions