Sensors Fusion for Cognitive Load Analysis using Gait Data

ait is the manner of walking in people and one of the basic functions for humans to move purposefully to reach a desired destination. The quality of life can be affected by gait abnormality and result in morbidity and mortality. Substantially, our aim in this research is to develop new methods and algorithms that make the most of the existing sensors for gait analysis. A detailed review [1] reveals the existing achievements and gaps in the current knowledge in gait analysis. The modalities in literature to capture gait data are grouped according to the sensing technology: video sequences, wearable sensors, and floor sensors. Following from the review, sensors under the foot are identified as a suitable method to study gait deterioration due to cognitive load in this research. Therefore, Deep learning models are implemented to fuse sensors under the foot and deliver automatic feature extraction of gait patterns and perform classification for the following. (a) Gait under cognitive load difference in males and females [2], where both genders identified by 95% yet they share the same cognitive load by 93%. (b) Healthy subjects’ natural limits due to cognitive load capacity investigated using their gait. Layer-Wise Relevance Propagation technique is used to link key known events in the gait cycle to identify the influence of cognitive demanding tasks on gait [3]. (c) Parkinson’s disease staging based on postural imbalance caused by gait deterioration. The models classified patients’ gait by 96% using ground truth markers [4]. These findings present valuable insight for gait spatiotemporal signals analysis, with other potential spin-offs are in the areas of biometrics and security

Towards Semi-Autonomous Robotic Arm Manipulation Operator Intention Detection from Forces Feedback

In harsh environments such as those found in nuclear facilities, the use of robotic systems is crucial for performing tasks that would otherwise require human intervention. This is done to minimize the risk of human exposure to dangerous levels of radiation, which can have severe consequences for health and even be fatal. However, the telemanipulation systems employed in these environments are becoming increasingly intricate, relying heavily on sophisticated control methods and local master devices. Consequently, the cognitive burden on operators during labor-intensive tasks is growing. To tackle this challenge, operator intention detection based on task learning can greatly enhance the performance of robotic tasks while reducing the reliance on human effort in teleoperation, particularly in a glovebox environment. By accurately predicting the operator's intentions, the robot can carry out tasks more efficiently and effectively, with minimal input from the operator. In this regard, we propose the utilization of Convolutional Neural Networks, a machine learning approach, to learn and forecast the operator's intentions using raw force feedback spatiotemporal data. Through our experimental study on glovebox tasks for nuclear applications, such as radiation survey and object grasping, we have achieved promising outcomes. Our approach holds the potential to enhance the safety and efficiency of robotic systems in harsh environments, thus diminishing the risk of human exposure to radiation while simultaneously improving the precision and speed of robotic operations

Task Learning for Intention Detection using Deep Neural Networks and Robotic Arm Data in Glovebox

Tele-manipulation systems are becoming more reliant on complex local (master) devices with sophisticated control methods; hence, the cognitive load on the operator during labour intensive tasks is increasing. The operator intention detection based on task learning can lead to better robot task performance with less human effort in teleoperation for a glovebox environment (see Fig. 1). Deep Convolutional Neural Networks are proposed to learn and predict the operator intention using robotic arm and its controller spatiotemporal data. Our preliminary experimental study on glovebox tasks for nuclear applications, particularly radiation survey and object grasping, provided promising results and encouraged us for a deeper research.UK Atomic Energy Authority, Remote Applications in Challenging Environments, Robotics and AI in Nuclear (RAIN Hub

A Privacy-Preservation Framework based on Biometrics Blockchain (BBC) to Prevent Attacks in VANET

In the near future, intelligent vehicles will be part of the Internet of Things (IoT) and will offer valuable services and opportunities that could revolutionise human life in smart cities. The Vehicular Ad-hoc Network (VANET) is the core structure of intelligent vehicles. It ensures the accuracy and security of communication in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) modes to enhance road safety and decrease traffic congestion. However, VANET is subject to security vulnerabilities such as denial-of-service (DoS), replay attacks and Sybil attacks that may undermine the security and privacy of the network. Such issues may lead to the transmission of incorrect information from a malicious node to other nodes in the network. In this paper, we present a biometrics blockchain (BBC) framework to secure data sharing among vehicles in VANET and to retain statuary data in a conventional and trusted system. In the proposed framework, we take advantage of biometric information to keep a record of the genuine identity of the message sender, thus preserving privacy. Therefore, the proposed BBC scheme establishes security and trust between vehicles in VANET alongside the capacity to trace identities whenever required. Simulations in OMNeT++, veins and SUMO were carried out to demonstrate the viability of the proposed framework using the urban mobility model. The performance of the framework is evaluated in terms of packet delivery rate, packet loss rate and computational cost. The results show that our novel model is superior to existing approaches

Understanding Saudi’s preferences of emergency physicians attire a cross-sectional study

Background: Patient centered care (PCC) is defined as the practice of attending to patients that includes listening, informing, and involving patients in their health care as per picker’s eight principles. A part of the patients’ rights is the presentation of the caring physician which includes attitude and attire. In Saudi Arabia, it has been known that doctors present with white coat. Several studies showed that physician dress-code improve patients’ satisfaction. Our aim in this study is to identify Saudi population preference on the attire of emergency physicians. Methods: This was cross-sectional study in Riyadh, Saudi Arabia. Questions were asked to Saudi population in malls and hospital’s waiting areas regarding physician attire by online survey. Participants were asked demographic questions then they were asked in detail about their preferences. Results: Total 486 participated in the study, where 52.15% strongly care about emergency physician’s look. 82% would like to see their male physician wearing scrubs/medical coat, 43% agreed on a face cover for their female physician but still prefer scrubs/medical coat with 45%.  Lastly participants had equal thoughts when it came to experience and reliability as 38% agree that looks can affect these two qualities. When it came to the relation of looks to knowledge as percentage were very close with 31% neutral and 32% agreed. Conclusions: Both Saudi sexes equally consider emergency physician external look as a representation on their respectfulness, reliability, and experience, but not that significant to the knowledge he has

A Computational Model for Reputation and Ensemble-Based Learning Model for Prediction of Trustworthiness in Vehicular Ad Hoc Network

Vehicular ad hoc networks (VANETs) are a special kind of wireless communication network that facilitates vehicle-to-vehicle(V2V) and vehicle-to-infrastructure(V2I) communication. This technology exhibits the potential to enhance the safety of roads, efficiency of traffic, and comfort of passengers. However, this can lead to potential safety hazards and security risks, especially in autonomous vehicles that rely heavily on communication with other vehicles and infrastructure. Trust, the precision of data, and the reliability of data transmitted through the communication channel are the major problems in VANET. Cryptography-based solutions have been successful in ensuring the security of data transmission. However, there is still a need for further research to address the issue of fraudulent messages being sent from a legitimate sender. As a result, in this study, we have proposed a methodology for computing vehicles reputation and subsequently predicting the trustworthiness of vehicles in networks. The blockchain records the most recent assessment of the vehicle’s credibility. This will allow for greater transparency and trust in the vehicle’s history, as well as reduce the risk of fraud or tampering with the information. The trustworthiness of a vehicle is confirmed not just by the credibility, but also by its network behavior as observed during data transfer. To classify the trust, an ensemble learning model is used. In depth tests are run on the dataset to assess the effectiveness of the proposed ensemble learning with feature selection technique. The findings show that the proposed ensemble learning technique achieves a 99.98% accuracy rate, which is notably superior to the accuracy rates of the baseline models

Prevalence and practice of oestrogen use among the male gym participants

BackgroundOestrogen is the primary female sex hormone and has important functions in both female and male physiology. Recently oestrogen abuse among male gym participants had raised as it is considered to enhance gym performance and breast size. AimsThis study assesses the prevalence of oestrogen use among male gym patrons in Saudi Arabia, their practice related to oestrogen abuse, and the profiles of users. Methods A cross-sectional study was conducted from February 2017 to May 2017 and included 4,860 male gym patrons. The participants were given a questionnaire with a total of 19 questions regarding socioeconomic information, knowledge and practices related to oestrogen, and lifestyle habits.Results The participants had a mean age of 28.6+6.2 years, 6.1 per cent of them abused oestrogen, and the most common forms used were ethinylestradiol (0.03mg) and drospirenone (3mg). Furthermore, 80.7 per cent of the users used it before exercise only. Breast enlargement was the main reason for oestrogen use, and local drug stores were the main source. Compared to non-users, oestrogen users were older (P=0.322), reported lower incomes (P=0.395), were more likely to be active smokers (P=0.597), and had a longer duration of gym participation (P < 0.001).ConclusionThe results indicate that 6.1 per cent of the surveyed male participants abused a combination of oestrogen and progesterone for breast enlargement, which was significantly more likely among males who had longer durations of gym participation

The Benign Prostatic Hyperplasia and Its Aetiologies

This study aimed at investigating the Benign Prostatic Hyperplasia and Its Aetiologies, therefore th prostatic hyperplasia predominantly involves the stromal compartment of the gland and affects more than 70% of men of 70 years or older with or without obstructive symptoms of benign prostatic hyperplasia. A consensus view is emerging concerning the factors and control systems that modulate cell proliferation and connective tissue biology in the prostate. The purpose of this review is to discuss some of the recent work contributing to the latter in the context of the aetiology of benign prostatic hyperplasia. The current study also reviews the most important findings regarding the key mechanisms involved in the pathophysiology of BPH. The study concluded that although the pathogenesis of BPH is not yet fully understood, several mechanisms seem to be involved in the development and progression of the disease. These mainly include systemic and local hormonal and vascular alterations as well as prostatic inflammation that would stimulate cellular proliferation

Role of Interventional Radiology in Management of Gastrointestinal Bleeding

Gastrointestinal bleeding is a common and potentially life-threatening condition that requires prompt and effective management. Interventional radiology has emerged as a valuable tool in the management of gastrointestinal bleeding, offering minimally invasive techniques that can rapidly control bleeding and improve patient outcomes. This review aims to provide an overview of the role of interventional radiology in the management of gastrointestinal bleeding, including its various techniques and their efficacy. The review discusses the different interventional radiology procedures that can be used to diagnose and treat gastrointestinal bleeding. It also highlights the advantages of techniques used in evaluation and management, including their ability to localize and control bleeding, as well as their low complication rates and shorter recovery times compared to traditional surgical approaches. Furthermore, the review addresses the specific indications for interventional radiology in the management of gastrointestinal bleeding, as well as the role of interventional radiology in the setting of underlying conditions. Overall, this review provides a comprehensive overview of the role of interventional radiology in the management of gastrointestinal bleeding, highlighting its effectiveness and potential benefits for patients. It also emphasizes the need for further research and collaboration between interventional radiologists and gastroenterologists to optimize the use of these techniques in clinical practice