Level of compliance in contact lens wearing medical doctors in Nepal

PURPOSES: To determine the level of compliance and major non-compliant behaviors in contact lens (CL) wearing medical doctors (MDs) and to compare it with age matched CL wearing normal subjects with no medical background (NS). METHODS: Thirty-nine current CL wearing MDs, who were prescribed CLs in Nepal Eye Hospital, Kathmandu, Nepal, between 2007 and 2011, were interviewed on ten modifiable compliant behaviors regarding lens care and maintenance. The level of compliance and the rate of non-compliance for each behavior were determined and compared with NS. RESULTS: Level of compliance was good, average and poor in 35.9%, 48.7% and 15.4% of MDs, respectively. There was no significant difference in compliance between MDs and NS (p=0.209). Level of compliance was not associated with age, gender and duration of lens wear (p>0.05). Compliance rate varied according to different behaviors, achieving a good compliance level of 95% for hand hygiene, avoidance of water contact and not sleeping with lenses. There was poor compliance for topping up solution (53.8%) and lens case replacement (15.4%). CONCLUSION: About one third of MDs had a good level of compliance. Level of compliance and compliance rate of different behaviors were similar in MDs and NS. Periodic lens case replacement was the most neglected behavior in CL wearers for this region.

Vision-based hand Wheel-chair control

Several studies have shown that people with disabilities benefit substantially from access to a means of independent mobility and assistive technology. Researchers are using technology originally developed for mobile robots to create easier to use wheelchairs. With this kind of technology people with disabilities can gain a degree of independence in performing daily life activities. In this work a computer vision system is presented, able to drive a wheelchair with a minimum number of finger commands. The user hand is detected and segmented with the use of a kinect camera, and fingertips are extracted from depth information, and used as wheelchair commands.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

Enrollment time as a requirement for biometric hand recognition systems

Biometric systems are increasingly being used as a means for authentication to provide system security in modern technologies. The performance of a biometric system depends on the accuracy, the processing speed, the template size, and the time necessary for enrollment. While much research has focused on the first three factors, enrollment time has not received as much attention. In this work, we present the findings of our research focused upon studying user’s behavior when enrolling in a biometric system. Specifically, we collected information about the user’s availability for enrollment in respect to the hand recognition systems (e.g., hand geometry, palm geometry or any other requiring positioning the hand on an optical scanner). A sample of 19 participants, chosen randomly apart their age, gender, profession and nationality, were used as test subjects in an experiment to study the patience of users enrolling in a biometric hand recognition system.

Ergonomic analysis in a food packaging workplace

Work-related musculoskeletal disorders (WMSD) became one of the biggest health problems in the workplace and one of the main concerns of ergonomics and despite all the technical improvements manual handling is still an important risk factor for WMSD. The current study was performed with the main objective of conducting an ergonomic analysis in a workplace that consists in packaging products in a pallet, in a food distribution industry, also called picking. In this perspective, the aim of the study is to identify if the tasks performed by operators present any risk of WMSD and, if so, to suggest proposals for minimizing the associated effort. The methodologies of ergonomic risk assessment that were initially applied were the Risk Reckoner and the Manual Handling Assessment Chart (MAC). Subsequently, in order to, on the one hand, complement the analysis performed using the two methods previously mentioned, and, on the other hand, allow an assessment of two important risk factors associated with this activity (work postures and loads handling), two additional methodologies were also selected: the Revised NIOSH Lifting Equation and the Rapid Entire Body Assessment (REBA). In all the performed approaches, the tasks of palletizing at lower levels were identified as the ones that most penalize workers in what regards the risk of development of WMSD. All methodologies identified levels of risk that require an immediate or short-term ergonomic intervention, aiming at ensuring the safety and health of workers performing such activity. The implementation of measures designed to eliminate or minimize the risk may involve the allocation of significant human and material resources that is increasingly necessary to manage efficiently. Taking into account the complexity and variability of the developed tasks, it is recommended that such a decision can be preceded by a new study using more accurate risk assessment methodologies, such as those that use monitoring tools.

Foreword [Occupational Safety and Hygiene III]

[Excerpt] This book is the third edition of this series of publications that are entitled “Occupational Safety and Hygiene”. The main contributions for this book were, once again, the works previously submitted and approved to be presented at the annual Symposium of the Portuguese Society of Occupational Safety and Hygiene (SPOSHO), which is celebrating its 11th edition in 2015. The SHO2015—International Symposium on Occupational Safety and Hygiene, similarly to the past seven years, was held in the School of Engineering at University of Minho in Guimarães, Portugal.

Occupational Safety and Hygiene SHO2015 - Proceedings book

Occupational Safety and Hygiene SHO2015 - Proceedings book

Nonlinear optimization for human-like synchronous movements of a dual arm-hand robotic system

In previous work we have presented a model capable of generating human-like movements for a dual arm-hand robot involved in human-robot cooperative tasks. However, the focus was on the generation of reach-to-grasp and reach-to-regrasp bimanual movements and no synchrony in timing was taken into account. In this paper we extend the previous model in order to accomplish bimanual manipulation tasks by synchronously moving both arms and hands of an anthropomorphic robotic system. Specifically, the new extended model has been designed for two different tasks with different degrees of difficulty. Numerical results were obtained by the implementation of the IPOPT solver embedded in our MATLAB simulator.

Hand gesture recognition system based in computer vision and machine learning: Applications on human-machine interaction

Tese de Doutoramento em Engenharia de Eletrónica e de Computadores