Computers have been commonly used in daily life and at hospitals by medical staff. This study was carried out to search the microbial colonization of computer keyboards and mice used inside and outside hospital environments. Keyboards and mice samples from a total of 398 computers were included to the study, in which 38 were used by doctors and nurses in the hospital clinics (Group 1); 32 by the medical faculty students (Group 2), and 328 by university students (Group 3) in the computer laboratories of Selcuk University, Konya (located at middle Anatolia). Of the computers, 96.7% (n:385) have been found to be colonized by coagulase-negative staphylococci (CONS), 13.1% (n:52) by gram-positive spore-forming bacilli and 8.8% (n:35) by corynebacteria; followed by Candida spp. (4.2%), gram-negative bacilli (1.7%) [Acinetobacter spp. (n:4), Pseudomonas sp. (n:l), Klebsiella sp. (n:l), E. coli (n:1)], Staphylococcus aureus (1.5%), and molds (Penicillium, Aspergillus; 1.2%). The isolation rates of CoNS were similar between the groups (94,7%, 93.7%, and 97.2%, respectively). However it was noted that all of the gram-negative bacterial isolates (7/38; 18.4%) were from the samples collected from hospital computers (Group 1). Susceptibility rates of CoNS isolates to cefoxitin were detected as 26.2% in Group 1, 79.2% in Group 2, and 91.3% in Group 3. Five out of six S. aureus strains were found susceptible to cefoxitin, except one isolated from a sample of Group 1. Linezolid resistance in both CoNS and S. aureus isolates were not determined in any groups. As a result, according to the data obtained from this study as well as from the other foreign studies, the computer keyboards and mice which are widely used in the hospital settings, are being the source of potential cross contamination in the development of nosocomial infections. Therefore the computers should be cleaned properly frequently and hand washing procedures and disinfection rules should be obeyed after the use of computers before handling the patients.
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J Neural Eng
January 2025
Electrical and Computer Engineering Department, University of New Brunswick, 3 Bailey Dr., Fredericton, New Brunswick, E3B5A3, CANADA.
Objective: While myoelectric control has been commercialized in prosthetics for decades, its adoption for more general human-machine interaction has been slow. Although high accuracies can be achieved across many gestures, current control approaches are prone to false activations in real-world conditions. This is because the same electromyogram (EMG) signals generated during the elicitation of gestures are also naturally activated when performing activities of daily living (ADLs), such as when driving to work or while typing on a keyboard.
View Article and Find Full Text PDFNat Commun
November 2024
Google, Mountain View, CA, USA.
Accelerating text input in augmentative and alternative communication (AAC) is a long-standing area of research with bearings on the quality of life in individuals with profound motor impairments. Recent advances in large language models (LLMs) pose opportunities for re-thinking strategies for enhanced text entry in AAC. In this paper, we present SpeakFaster, consisting of an LLM-powered user interface for text entry in a highly-abbreviated form, saving 57% more motor actions than traditional predictive keyboards in offline simulation.
View Article and Find Full Text PDFJ Med Internet Res
October 2024
Department of Occupational Therapy, College of Medical Science, Soonchunhyang University, Asan, Republic of Korea.
J Med Internet Res
October 2024
Department of Psychiatry, University of Illinois Chicago, Chicago, IL, United States.
Background: Passive sensing through smartphone keyboard data can be used to identify and monitor symptoms of mood disorders with low participant burden. Behavioral phenotyping based on mobile keystroke data can aid in clinical decision-making and provide insights into the individual symptoms of mood disorders.
Objective: This study aims to derive digital phenotypes based on smartphone keyboard backspace use among 128 community adults across 2948 observations using a Bayesian mixture model.
PLoS One
October 2024
Department of Mathematics and Computer Science, University of Douala, Douala, Cameroon.
This study presents a novel multi-stage hierarchical approach to optimize key selection on virtual keyboards using eye gaze. Existing single-stage selection algorithms have difficulty with distant keys on large interfaces. The proposed technique divides the standard QWERTY keyboard into progressively smaller regions guided by eye movements, with boundary fixations first selecting halves and quarters to sequentially narrow the search area.
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