An Adaptive User Interface in Healthcare

AbstractHealthcare is a broad subject with many different challenges, yet it is important and relatable to everyone. The aging Baby Boomer generation is an important healthcare issue today. In Canada, and many other developed nations, the number of citizens reaching the age of retirement and seniority is growing faster than the rate of citizens working and providing health related services. As people age they tend to require more frequent checkups and health services, ultimately putting a bigger resource drain on healthcare infrastructure. New advancements in Computer Science and Engineering are allowing the development of next generation applications with the purpose of providing healthcare services in a cost effective and efficient way. This paper proposes a multi-agent system for tracking and monitoring health data for patients. Furthermore, agents within the system use reinforcement learning techniques to build an adaptive user interface for each human user. The actions and behaviour of users are monitored and used to modify their respective user interface over time. To demonstrate the feasibility of the architecture, two scenarios are provided. We conclude with several possible future directions for this research

Dynamic Healthcare Interface for Patients

AbstractCanadian healthcare is a fundamental part of society. Challenges such as the aging baby boomer generation require the healthcare industry to meet higher demands while using fewer resources. Computer systems designed to record and report physical health properties of an individual personcan be used in part to accomplish this task. In this paper, we present the architecture of a hypothetical multi-agent system designed to provide healthcare information about specific patients through continuous monitoring. The resulting data from the system is accessible by the patient to whom it belongs as well as his or her healthcare professional. Furthermore, the proposed system utilizes an adaptive user interface for the purpose of improving the overall experience for users with poor vision or motor skills. Specifically, we focus on the implementation of several of the key components involved in the adaptive user interface: learning component and the user model. To demonstrate the feasibility of the implementation two scenarios are provided. We conclude with several possible future directions for this research

Comparative Study of Fingerprint and Centroid Localization Protocol Using COOJA

AbstractSensor networks are in a numerous number of applications. However, implementing wireless sensor networks present new challenges compared with theoretical networks. Cooja is the Contiki network simulator. It allows large and small networks of Contiki motes to be simulated; moreover, motes can be emulated at the hardware level. In this paper, we evaluate the accuracy performance of two very well-known localization protocols, namely: fingerprint and centroid protocols using Tmote sky in Cooja. It is worth mentioning that this the first time this study is conducted in Cooja. The results conform to the theory that fingerprint protocol has a better performance than centroid in terms of accuracy when accuracy is quantified

An approximate analytical formula for estimating the weight of factors affecting the spread of COVID-19: a case study of the first wave

COVID-19 pandemic has changed the way we live our lives for the foreseen future. To date, there have been over 113 million reported cases and 2.5 million deaths worldwide. Many studies investigated the factors affecting the number of daily cases such as weather conditions, lockdown duration and other factors. In this study, we propose a COVID-19 analytical formula for factors contributing to the number of the new coronavirus daily cases. We have also calculated values of relative weights of those factors. We focus on the first wave data that are publically available. Seven countries were considered including the UK, Italy, Spain, Canada, South Korea, Germany and France. We considered the following factors: temperature, humidity, government expenditure, lockdown hours and the number of daily tests for COVID-19 performed. The weights were calculated based on the hypothesis that a high correlation between recorded data of a given pair of countries implies a high correlation of the pair’s COVID-19 proposed analytical formula. The factors are calculated using the brute-force technique. Our results showed that in five out of the seven countries; temperature, humidity, and lockdown duration were the most dominant with values of 26%, 32% and 38%, respectively. In other countries, however, humidity, government expenditure and the daily performed tests for COVID-19 were the most effective factors, with relative values of 35%, 26%, and 28%

An Enhanced Distributed Scheme for WSNs

This paper investigates data processing schemes that define the distribution of decision making that affects system accuracy and energy consumption. There exist two typical schemes, namely: centralized and distributed schemes. In a centralized scheme, nodes collect samples and send them to a “fusion center.” This scheme provides optimal decision accuracy; however, it consumes considerable energy. In contrast, distributed schemes allow nodes to make local 1-bit decisions, which are sent to the fusion center to make the final decision. In a hybrid scheme, the network specifies the level of accuracy required for the whole system. This can be achieved by manipulating the scheme to work interchangeably as centralized or distributed. Most of the energy consumed is in the transmission process; therefore, this paper proposes an energy-saving hybrid scheme that focuses on optimizing transmission energy. In this proposed scheme, each node is able to alternate between centralized and decentralized scheme according to its location and path length. To validate the proposed approach, it is simulated and the results are compared with the hybrid scheme

A Web-Based Application of TELOSB Sensor Network

Sensor network can be used in a numerous number of applications. However, implementing wireless sensor networks present new challenges compared with theoretical networks. In addition, implementing a sensor network might provide results different from that derived theoretically. Some routing protocols when implemented might fail to perform. In this paper, we implement three routing protocols, namely: Dynamic MANET on-demand, Collection Tree and Dissemination protocols. To compare the performance of these protocols, they are implemented using a Telosb sensor network. Several performance metrics are carried out to demonstrate the pros and cons of these protocols. A telemedicine application is tested in top of the implemented Telosb sensor network at King Fahd University of Petroleum and Minerals Clinic in Saudi Arabia, utilizing Alive ECG sensors

Fault Reconnaissance Agent for Sensor Networks

One of the key prerequisite for a scalable, effective and efficient sensor network is the utilization of low-cost, low-overhead and high-resilient fault-inference techniques. To this end, we propose an intelligent agent system with a problem solving capability to address the issue of fault inference in sensor network environments. The intelligent agent system is designed and implemented at base-station side. The core of the agent system – problem solver – implements a fault-detection inference engine which harnesses Expectation Maximization (EM) algorithm to estimate fault probabilities of sensor nodes. To validate the correctness and effectiveness of the intelligent agent system, a set of experiments in a wireless sensor testbed are conducted. The experimental results show that our intelligent agent system is able to precisely estimate the fault probability of sensor nodes