Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time

Burnout syndrome among multinational nurses working in Saudi Arabia

Background: Nursing Staff is reported to be under extreme state of stress, leading to burnout syndrome (BS). Most of the studies have been conducted among the nurses working in their home countries. This study was conducted to assess the prevalence of BS among a multinational nursing workforce in Saudi Arabia.Materials and Methods: King Fahd University Hospital, AlKhobar, Saudi Arabia, is a tertiary care hospital employing 510 nurses of multinational workforce. Two hundred and fifty Maslach Burnout Inventory (MBI) individual-based questionnaires were distributed after modification to include the age, sex, marital status, nationality, unit working and number of years on the job. The data were entered in the database and analyzed using Statistical Package for the Social Sciences (SPSS), version 14.0. A P value of <0.05 was considered statistically significant .Results: One hundred and ninety-eight nurses (77.2%) completed the questionnaire. Their average age was 34.46 } 5.36 years. Forty-five percent (89) had high emotional exhaustion (EE) and 28.9% (57) had moderate suffering with EE. Staffs who were on the job for longer duration had a lesser frequency of EE (P . 0.001). The frequency of depersonalization (DP) was 83 (42%) and was graded as high and 61 (30.8%) were moderately affected. Personal accomplishment (PA) was moderate to low in the majority of the nurses (71.5%). Married nurses were prone to EE (28.17 } 12.1 versus 22.3 } 9.6) than unmarried nurses (P = 0.003, CI 95% and OR 2.4). The nurses in the patientsfwards and clinics were more emotionally exhausted with higher DP compared to nurses in the high stress and high activity areas (P < 0.001, OR .11.1; and P < 0.001, CI 95% and OR 9.65). Non-Saudi nurses were significantly more prone to EE (27.3 } 12.1 versus 21.6 } 2.9) than Saudi nurses (P = 0.004; 95% CI: <9.64).Conclusion: We found that majority of the nursing staff at our hospital were in a state of burnout with high frequency of EE and DP. Only a quarter of the surveyed staff felt that they had some level of PA. Age and working away from their home countries were the important predictors in the development of BS in nurses. We believe that working conditions have to be improved to develop strategies to cope and alleviate stressful situations

The Impact of Replacing Complex Hand-Crafted Features with Standard Features for Melanoma Classification using Both Hand-Crafted and Deep Features

Melanoma is the deadliest form of skin cancer and it is the most rapidly spreading cancer in the world. An earlier detection of this kind of cancer is curable; hence, earlier detection of melanoma is pre-eminent. Because of this fact, a lot of research is being done in this area especially in automatic detection of melanoma. In this paper, we are proposing an automatic melanoma detection system which utilizes a combination of deep and hand-crafted features. We analyzed the impact of using a simpler and standard hand-crafted feature, in place of complex usual hand-crafted features e.g. shape, texture, diameter, or some custom features. We used a convolutional neural network (CNN) known as deep residual network (ResNet) to extract the deep features and utilized the scale invariant feature descriptor (SIFT) as the hand-crafted feature. The experiments revealed that combining SIFT did not improve the accuracy of the system however, we obtained higher accuracy than state-of-the-art methods with our deep only solution