mHealth system for the early detection of infectious diseases using biomedical signals

Latin American Congress on Automation and Robotics LACAR 2019, 30/10/2019-01/11/2019, Cali, Colombia.Detection at an early stage of an infection is a major clinical challenge. An infection that is not diagnosed in time can not only seriously affect the health of the infected patient, but also spread and initiate a contagious approach towards other people. This paper deals with mHealth system for medical care and pre-diagnosis. The developed mHealth system use an Android App that collects physiological signals from the patients with a portable and easy-to-use sensors kit. The focus of the work is put on being able to build a low-cost system that using a very small amounts of data (one set record per patient and day). The processed data are uploaded to an online database to train a clinical decision support system to automatically diagnose infections. The mHealth system may be operated by the same personnel on site not requiring to be medical or computational skilled at all. The implementation takes five kinds of measures simultaneously (Electrodermal Activity, Body Temperature, Blood Pressure, Heart Beat Rate and Oxygen Saturation (SPO2)). A real implementation has been tested and results confirm that the sampling process can be done very fast and steadily Finally, the App usability was tested, showing a fast learning curve and no significant differences are observable in learning time by people with different skills or age. These usability factors are key for the mHealth system success

Analysis of Barriers to the Deployment of Health Information Systems: a Stakeholder Perspective

© 2018 The Author(s). This paper argues that the cross-analysis of barriers with stakeholders provides a richer picture than analyzing the barriers on their own, as most of the literature in this area does. To test this hypothesis, we used the data from 33 interviews across 19 different types of stakeholders that were involved in a telemedicine system for the Chronically-ill Patient. Our findings show encouraging results. For instance, it was found that the group of stakeholders who are directly related to the governance and policy-making identified most of the barriers. This finding may imply that this group is more aware of the challenges when implementing HIS, or it may suggest that this group poses more resistance due to the current economic and Organizational models in health care. It was also found that some barriers are cited by all stakeholders whereas others not, suggesting that some barriers may be more relevant than others

User-centric ambient information systems and applications

Quan Z. Sheng, Elhadi M. Shakshuki, Jiangang M

Peer to Peer Multi-agent System for Online Trading

This paper presents a peer-to-peer multi-agent system architecture for online trading. Such a system addresses some of the shortcomings that are present in contemporary online trading systems, such as single attribute-based negotiation, the requirement of an electronic marketplace and variations and status changes within the network. The proposed system architecture is a multi-agent, multi-tier architecture. These agents are autonomous, goal driven agents that work together cooperatively to assist different users to buy products online based on their interests and preferences. The system architecture is threetier and consists of three types of agents that are classified based on their functionality: interface, resource and retrieval agents. The interface agents are the front end of the system and interact with different users to fulfill their needs. The resource agents access and capture the content and the changes of the local information database. The retrieval agents travel and interact with other agents at remote host machines. A prototype of this system is being implemented using Aglet. 1

Advances in mobile web information systems

Jian Yu, Quan Z. Sheng, Muhammad Younas, Elhadi Shakshuk

Advances in context-aware mobile services

Jian Yu, Quan Z. Sheng, Muhammad Younas, Elhadi Shakshuk

Energy-aware reprogramming of sensor networks using incremental update and compression

Reprogramming is an important issue in wireless sensor networks. It enables users to extend or correct functionality of a sensor network after deployment at a low cost. In this paper, we investigate the problem of improving energy efficiency and delay of reprogramming by using data compression and incremental updates. We analyze different algorithms for both approaches, as well as their combination, when applied to resource-constrained devices. Our results show that the classic Lempel-Ziv-77 compression algorithm with Bsdiff for delta encoding has the best overall performance compared to other compression algorithms; on average reducing energy usage by 74% and enabling 71% faster updates

Adaptive ACK: A novel intrusion detection system to mitigate intended packet dropping in MANETs

Unlike infrastructure wireless networks, mobile ad-hoc network (MANET) is an infrastructureless network where self-configuring mobile nodes connected by wireless links. Because of its decentralized property, these nodes relay on each other to store and forward packets. Most of the proposed MANET protocols assume cooperative behavior among its nodes. However, some nodes may exhibit different behaviors such as selfish. Thus, misbehaving nodes can disrupt the network operation and affect the network availability by refusing to cooperate to route packets due to their selfish or malicious behavior. Towards this end, this paper proposes a novel intrusion detection system. An adaptive acknowledgment scheme (AACK) is proposed to detect and exclude such misbehaving nodes. This scheme is developed to overcome watchdog weaknesses due to collisions and limited transmission power. To demonstrate the performance of our proposed scheme, simulation experiments are performed. The simulation results show that AACK scheme provides better network performance with less overhead than some existing DSR-based systems such TWOACK and Watchdog

GPU-based Parallel Computing for Activity-based Travel Demand Models

Activity-based travel demand models (ABMs) are gaining popularity in the field of traffic modeling because of their high level of detail compared to traditional travel demand models. Due to this, however, ABMs have high computational requirements, making ABMs hard to use for analysis and optimization purposes. We address this problem by relying on the concept of parallel computing using a computer’s graphics processing unit (GPU). To illustrate the potential of GPU computing for ABM, we present a pilot study in which we compare the observed computation time of an ABM GPU implementation that we built using NVIDIA’s CUDA framework with similar, non-parallel implementations. We conclude that speed-ups up to a factor 50 can be realized, enabling the use of ABMs both for fast analysis of scenarios and for optimization purposes