Information acquisition using eye-gaze tracking for person-following with mobile robots

In the effort of developing natural means for human-robot interaction (HRI), signifcant amount of research has been focusing on Person-Following (PF) for mobile robots. PF, which generally consists of detecting, recognizing and following people, is believed to be one of the required functionalities for most future robots that share their environments with their human companions. Research in this field is mostly directed towards fully automating this functionality, which makes the challenge even more tedious. Focusing on this challenge leads research to divert from other challenges that coexist in any PF system. A natural PF functionality consists of a number of tasks that are required to be implemented in the system. However, in more realistic life scenarios, not all the tasks required for PF need to be automated. Instead, some of these tasks can be operated by human operators and therefore require natural means of interaction and information acquisition. In order to highlight all the tasks that are believed to exist in any PF system, this paper introduces a novel taxonomy for PF. Also, in order to provide a natural means for HRI, TeleGaze is used for information acquisition in the implementation of the taxonomy. TeleGaze was previously developed by the authors as a means of natural HRI for teleoperation through eye-gaze tracking. Using TeleGaze in the aid of developing PF systems is believed to show the feasibility of achieving a realistic information acquisition in a natural way

Mobile robot teleoperation through eye-gaze (telegaze)

In most teleoperation applications the human operator is required to monitor the status of the robot, as well as, issue controlling commands for the whole duration of the operation. Using a vision based feedback system, monitoring the robot requires the operator to look at a continuous stream of images displayed on an interaction screen. The eyes of the operator therefore, are fully engaged in monitoring and the hands in controlling. Since the eyes of the operator are engaged in monitoring anyway, inputs from their gaze can be used to aid in controlling. This frees the hands of the operator, either partially or fully, from controlling which can then be used to perform any other necessary tasks. However, the challenge here lies in distinguishing between the inputs that can be used for controlling and the inputs that can be used for monitoring. In mobile robot teleoperation, controlling is mainly composed of issuing locomotion commands to drive the robot. Monitoring on the other hand, is looking where the robot goes and looking for any obstacles in the route. Interestingly, there exist a strong correlation between human's gazing behaviours and their moving intentions. This correlation has been exploited in this thesis to investigate novel means for mobile robot teleoperation through eye-gaze, which has been named TeleGaze for short

Determination of veterinary pharmaceuticals residue in soil and biological materials: a review of current analytical methods

Veterinary pharmaceuticals have been extensively used in animal husbandry for control of disease and growth promoters. These compounds are excreted from animals via urine and faeces, end up in the environment through untreated animal waste disposal. Veterinary pharmaceuticals often exist in the complex solid environmental samples such as manure, slurry, and soil which require extensive extraction, clean-up and analysis method. This review highlights the current analytical methods for the analysis of veterinary pharmaceuticals in complex solid environmental matrices, including soil, animal manures and sediment. The aim of this review is to compare and summarize the performance of each method in terms of recovery, method detection limit (MDL) and method quantification limit (MQL)

Determination of polycyclic aromatic hydrocarbons in human blood samples using solid phase extraction and gas chromatography mass spectrometry- a review

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants with toxic effects and adverse health impacts on general population. Several methods of extraction had been applied to extract PAHs from human blood samples such as solid phase extraction (SPE). The SPE represents one of the most common techniques for extraction and clean-up procedures as it needs low quantity of solvents with less manual efforts. Similarly, various analytical instruments like gas chromatography coupled to mass spectrometry (GC-MS) was used to measure the PAHs levels. Gas chromatography is a simple, fast, and very efficient method for solvents and small organic molecules. This review provides an overview of the measured concentrations of PAHs in human blood samples through the application of SPE and GC-MS during the last ten years. While these studies used various solvents, their application of SPE method and GC-MS revealed rewarding results about the determination of PAHs levels in the human samples

A Multitier Deep Learning Model for Arrhythmia Detection

Electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVD) in the hospital, which often helps in the early detection of such ailments. ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient-doctor dialogues. It provides cardiologists with inferences regarding more serious cases. Notwithstanding its proven utility, deciphering large datasets to determine appropriate information remains a challenge in ECG-based CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification accuracy is improved via a robust feature extraction. This is followed using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. The MIT-BIH Arrhythmia was employed in the validation to identify five arrhythmia categories based on the association for the advancement of medical instrumentation (AAMI) standard. The performance of the proposed technique alongside state-of-the-art in the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected in the acquired ECG data in a smart healthcare framework

From Fair Decision Making to Social Equality

The study of fairness in intelligent decision systems has mostly ignored long-term influence on the underlying population. Yet fairness considerations (e.g. affirmative action) have often the implicit goal of achieving balance among groups within the population. The most basic notion of balance is eventual equality between the qualifications of the groups. How can we incorporate influence dynamics in decision making? How well do dynamics-oblivious fairness policies fare in terms of reaching equality? In this paper, we propose a simple yet revealing model that encompasses (1) a selection process where an institution chooses from multiple groups according to their qualifications so as to maximize an institutional utility and (2) dynamics that govern the evolution of the groups' qualifications according to the imposed policies. We focus on demographic parity as the formalism of affirmative action. We then give conditions under which an unconstrained policy reaches equality on its own. In this case, surprisingly, imposing demographic parity may break equality. When it doesn't, one would expect the additional constraint to reduce utility, however, we show that utility may in fact increase. In more realistic scenarios, unconstrained policies do not lead to equality. In such cases, we show that although imposing demographic parity may remedy it, there is a danger that groups settle at a worse set of qualifications. As a silver lining, we also identify when the constraint not only leads to equality, but also improves all groups. This gives quantifiable insight into both sides of the mismatch hypothesis. These cases and trade-offs are instrumental in determining when and how imposing demographic parity can be beneficial in selection processes, both for the institution and for society on the long run.Comment: Short version appears in the proceedings of ACM FAT* 201

Degradation of veterinary antibiotics and hormone during broiler manure composting

The fate of nine veterinary antibiotics and one hormone in broiler manure during 40 days of composting was investigated. Results showed that composting can significantly reduce the concentration of veterinary antibiotics and hormone in broiler manure, making application of the post-compost manure safer for soil application. More than 99% of the nine antibiotics and one hormone involved in this study were removed from the manure during 40 days of composting. The target antibiotics and hormone showed short half-life in broiler manure composting, ranging from 1.3 to 3.8 days. The relationship between the physico-chemical properties of soil, manure and manure compost and its veterinary antibiotic and hormone concentration was statistically evaluated by Pearson correlation matrix. The concentration of veterinary antibiotics and hormone in manure compost was suggested to be affected by physico-chemical properties such as pH, temperature, total organic carbon (TOC), total nitrogen (TN), total phosphorus (TP) and metal contents

A Multitier Deep Learning Model for Arrhythmia Detection

An electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVDs). ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient-doctor dialogs. Notwithstanding its proven utility, deciphering large data sets to determine appropriate information remains a challenge in ECG-based CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification accuracy is improved via a robust feature extraction protocol. This is followed by using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. Comparison of the performance recorded for the proposed technique alongside state-of-the-art methods reported the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The outcomes suggest that the proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected