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Current Motor Imagery Brain-Computer Interfaces (MI-BCI) require a lengthy and monotonous training procedure to train both the system and the user. Considering many users struggle with effective control of MI-BCI systems, a more user-centered approach to training might help motivate users and facilitate learning, alleviating inefficiency of the BCI system. With the increase of BCI-controlled games, researchers have suggested using game principles for BCI training, as games are naturally centered on the player. This review identifies and evaluates the application of game design elements to MI-BCI training, a process known as gamification. Through a systematic literature search, we examined how MI-BCI training protocols have been gamified and how specific game elements impacted the training outcomes. We identified 86 studies that employed gamified MI-BCI protocols in the past decade. The prevalence and reported effects of individual game elements on user experience and performance were extracted and synthesized. Results reveal that MI-BCI training protocols are most often gamified by having users move an avatar in a virtual environment that provides visual feedback. Furthermore, in these virtual environments, users were provided with goals that guided their actions. Using gamification, the reviewed protocols allowed users to reach effective MI-BCI control, with studies reporting positive effects of four individual elements on user performance and experience, namely: feedback, avatars, assistance, and social interaction. Based on these elements, this review makes current and future recommendations for effective gamification, such as the use of virtual reality and adaptation of game difficulty to user skill level.

Objectives: Clinical trials testing new devices require prior training on dummies to minimize the "learning curve" for patients. Dentists were trained using a novel water jet device for mechanical cleaning of dental implants and with a novel cold plasma device for surface functionalisation during a simulated open flap peri-implantitis therapy. The hypothesis was that there would be a learning curve for both devices. Materials and methods: 11 dentists instrumented 44 implants in a dummy-fixed jaw model. The effect of the water jet treatment was assessed as stain removal and the effect of cold plasma treatment as surface wettability. Both results were analysed using photographs. To improve treatment skills, each dentist treated four implants and checked the results immediately after the treatment as feedback. Results: Water jet treatment significantly improved from the first to the second implant from 62.7% to 75.3% stain removal, with no further improvement up to the fourth implant. The wettability with cold plasma application reached immediately a high level at the first implant and was unchanged to the 4th implant (mean scores 2.7 out of 3). Conclusion: A moderate learning curve was found for handling of the water jet but none for handling of the cold plasma. Clinical relevance: Scientific rational for study: Two new devices were developed for peri-implantitis treatment (Dental water jet, cold plasma). Dentists were trained in the use of these devices prior to the trial to minimize learning effects. Principal findings: Experienced dentists learn the handling of the water jet very rapidly and for cold plasma they do not need much training. Practical implications: A clinical study is in process. When the planned clinical study will be finished, we will find out, if this dummy head exercise really minimised the learning curve for these devices. [ABSTRACT FROM AUTHOR]

Emissions from wood-burning stoves contribute to local air pollution. However, it is difficult to determine the real emissions from such stoves, especially due to unknown user behaviour, which can have a large impact on emissions. In this study, the low-cost emission reduction measure “user training” was evaluated to determine its emission reduction potential on firewood stoves. Two sets of tests were carried out. First, a field measurement campaign was conducted in Styria (Austria) with four wood stoves, where gaseous and particulate emissions were measured before and after a user training on optimised heating behaviour (e.g. ignition mode, fuel properties and placement in the combustion chamber, air supply). Gaseous emissions (carbon monoxide – CO, organic gaseous compounds – OGC) were measured continuously, while particulates were measured in batches, in undiluted and hot as well as in diluted and cooled flue gas in parallel with a specific field measurement setup. In addition, particle filters were analysed to quantify the concentration of the carcinogenic compound benzo(a)pyrene (BaP). Second, user training workshops were conducted. These tests had a simple measurement setup in order to increase the number of tests. Thus, only CO emissions were evaluated.The results show that real life emissions in the field are high and have a high variability compared to laboratory tests and official type test results. However, user training showed a significant reduction of CO, OGC, TSP and BaP emissions of 42%, 57%, 45% and 76% (median), respectively. In addition, TSPsum (sum of hot and cooled particle emission samples) emissions decreased by 39% (median) after user training. The relative reduction rates of all batches show that the highest emission reduction potential was identified for BaP, with a reduction rate of up to 97%. The results of the workshop tests confirmed the high variability in user behavior and the range for the emission reduction potentials, with a median CO reduction of 41%.The emission reduction potential of the user training measure is comparable to state-of-the-art technological measures such as electrostatic precipitators and catalysts. However, these measures are costly and require a high level of technical sophistication. User training, on the other hand, is relatively cheap, easy to implement and suitable for all users. Of course, there is some risk that trained end-users will revert to their old habits, leading to higher emissions again. Therefore, regular training may be necessary to maintain the higher level of performance. As we did not assess this aspect in our work, further research would be needed to prove this theory.

Human-robot cooperation (HRC) is becoming increasingly relevant with the surge in collaborative robots (cobots) for industrial applications. Examples of humans and robots cooperating actively on the same workpiece can be found in research labs around the world, but industrial applications are still mostly limited to robots and humans taking turns. In this paper, we use a cooperative lifting task (co-lift) as a case study to explore how well this task can be learned within a limited time, and how background factors of users may impact learning. The experimental study included 32 healthy adults from 20 to 54 years who performed a co-lift with a collaborative robot. The physical setup is designed as a gamified user training system as research has validated that gamification is an effective methodology for user training. Human motions and gestures were measured using Inertial Measurement Unit (IMU) sensors and used to interact with the robot across three role distributions: human as the leader, robot as the leader, and shared leadership. We find that regardless of age, gender, job category, gaming background, and familiarity with robots, the learning curve of all users showed a satisfactory progression and that all users could achieve successful cooperation with the robot on the co-lift task after seven or fewer trials. The data indicates that some of the background factors of the users such as occupation, past gaming habits, etc., may affect learning outcomes, which will be explored further in future experiments. Overall, the results indicate that the potential of the adoption of HRC in the industry is promising for a diverse set of users after a relatively short training process. [ABSTRACT FROM AUTHOR]

Human-robot cooperation (HRC) is becoming increasingly relevant with the surge in collaborative robots (cobots) for industrial applications. Examples of humans and robots cooperating actively on the same workpiece can be found in research labs around the world, but industrial applications are still mostly limited to robots and humans taking turns. In this paper, we use a cooperative lifting task (co-lift) as a case study to explore how well this task can be learned within a limited time, and how background factors of users may impact learning. The experimental study included 32 healthy adults from 20 to 54 years who performed a co-lift with a collaborative robot. The physical setup is designed as a gamified user training system as research has validated that gamification is an effective methodology for user training. Human motions and gestures were measured using Inertial Measurement Unit (IMU) sensors and used to interact with the robot across three role distributions: human as the leader, robot as the leader, and shared leadership. We find that regardless of age, gender, job category, gaming background, and familiarity with robots, the learning curve of all users showed a satisfactory progression and that all users could achieve successful cooperation with the robot on the co-lift task after seven or fewer trials. The data indicates that some of the background factors of the users such as occupation, past gaming habits, etc., may affect learning outcomes, which will be explored further in future experiments. Overall, the results indicate that the potential of the adoption of HRC in the industry is promising for a diverse set of users after a relatively short training process.

Despite growing interest and research into brain-computer interfaces (BCI), their usage remains limited outside of research laboratories. One reason for this is BCI inefficiency, the phenomenon where a significant number of potential users are unable to produce machine-discernible brain signal patterns to control the devices. To reduce the prevalence of BCI inefficiency, some have advocated for novel user-training protocols that enable users to more effecctively modulate their neural activity. Important considerations for the design of these protocols are the assessment measures that are used for evaluating user performance and for providing feedback that guides skill acquisition. Herein, we present three trial-wise adaptations (running, sliding window and weighted average) of Riemannian geometry-based user-performance metrics (classDistinct reflecting the degree of class separability and classStability reflecting the level of within-class consistency) to enable feedback to the user following each individual trial. We evaluated these metrics, along with conventional classifier feedback, using simulated and previously recorded sensorimotor rhythm-BCI data to assess their correlation with and discrimination of broader trends in user performance. Analysis revealed that the sliding window and weighted average variants of our proposed trial-wise Riemannian geometry-based metrics more accurately reflected performance changes during BCI sessions compared to conventional classifier output. The results indicate the metrics are a viable method for evaluating and tracking user performance changes during BCI-user training and, therefore, further investigation into how these metrics may be presented to users during training is warranted. [ABSTRACT FROM AUTHOR]

The training of professionals specialized in archival or document management in Cuba has been a constant need. There is no national training program that allows to establish a benchmark to carry out improvement actions for professionals who work in the institutional file system. Based on the above, the objective was to design a reference program for the continuous training of specialists in the archival field. The study classified as descriptive. A training plan was designed for the specialization of professionals who work within the institutional file system in Cuba. It is concluded that user training constitutes a mandatory operational tool to promote continuous improvement of organizations based on document management. A user training program was designed as a reference framework to adapt to the needs of stakeholders and organizations. [ABSTRACT FROM AUTHOR]

Training programs developed by university libraries have gained prominence over time, overtaking other services and tasks traditionally performed by their employees. Greater user autonomy in relation to information (the search, evaluation, management, and use of) means a greater variety of sources, support, channels and information-producing agents, as well as new forms of teaching and learning, making it necessary to adapt traditional user training to the new needs of users and to the current technological context. In this sense, it is important to incorporate new content into training plans and take advantage of all teaching modalities at our disposal to provide this training in the most appropriate way and to reach the largest number of users. [ABSTRACT FROM AUTHOR]

Studies have shown that closed-loop myoelectric control schemes can lead to changes in user performance and behavior compared to open-loop systems. When users are placed within the control loop, such as during real-time use, they must correct for errors made by the controller and learn what behavior is necessary to produce desired outcomes. Augmented feedback, consequently, has been used to incorporate the user throughout the training process and to facilitate learning. This work explores the effect of visual feedback presented during user training on both the performance and predictability of a myoelectric classification-based control system. Our results suggest that properly designed feedback mechanisms and training tasks can influence the quality of the training data and the ability to predict usability using linear combinations of metrics derived from feature space. Furthermore, our results confirm that the most common in-lab training protocol, screen guided training, may yield training data that are less representative of online use than training protocols that incorporate the user in the loop. These results suggest that training protocols should be designed that better parallel the testing environment to more effectively prepare both the algorithms and users for real-time control.

Despite growing interest and research into brain-computer interfaces (BCI), their usage remains limited outside of research laboratories. One reason for this is BCI inefficiency, the phenomenon where a significant number of potential users are unable to produce machine-discernible brain signal patterns to control the devices. To reduce the prevalence of BCI inefficiency, some have advocated for novel user-training protocols that enable users to more effectively modulate their neural activity. Important considerations for the design of these protocols are the assessment measures that are used for evaluating user performance and for providing feedback that guides skill acquisition. Herein, we present three trial-wise adaptations (running, sliding window and weighted average) of Riemannian geometry-based user-performance metrics (classDistinct reflecting the degree of class separability and classStability reflecting the level of within-class consistency) to enable feedback to the user following each individual trial. We evaluated these metrics, along with conventional classifier feedback, using simulated and previously recorded sensorimotor rhythm-BCI data to assess their correlation with and discrimination of broader trends in user performance. Analysis revealed that the sliding window and weighted average variants of our proposed trial-wise Riemannian geometry-based metrics more accurately reflected performance changes during BCI sessions compared to conventional classifier output. The results indicate the metrics are a viable method for evaluating and tracking user performance changes during BCI-user training and, therefore, further investigation into how these metrics may be presented to users during training is warranted.

Studies have shown that closed-loop myoelectric control schemes can lead to changes in user performance and behavior compared to open-loop systems. When users are placed within the control loop, such as during real-time use, they must correct for errors made by the controller and learn what behavior is necessary to produce desired outcomes. Augmented feedback, consequently, has been used to incorporate the user throughout the training process and to facilitate learning. This work explores the effect of visual feedback presented during user training on both the performance and predictability of a myoelectric classification-based control system. Our results suggest that properly designed feedback mechanisms and training tasks can influence the quality of the training data and the ability to predict usability using linear combinations of metrics derived from feature space. Furthermore, our results confirm that the most common in-lab training protocol, screen guided training, may yield training data that are less representative of online use than training protocols that incorporate the user in the loop. These results suggest that training protocols should be designed that better parallel the testing environment to more effectively prepare both the algorithms and users for real-time control. [ABSTRACT FROM AUTHOR]

Abstract Background Point-of-care glucose-6-phosphate dehydrogenase (G6PD) testing has the potential to make the use of radical treatment for vivax malaria safer and more effective. Widespread use of G6PD tests as part of malaria case management has been limited, in part due to due concerns regarding product usability, user training, and supervision. This study seeks to assess how well end users can understand the Standard™ G6PD Test (SD Biosensor, Suwon, South Korea) workflow, result output, and label after training. This will ultimately help inform test registration and introduction. Methods Potential G6PD test users who provide malaria case management at three sites in Brazil, Ethiopia, and India were trained on the use of the SD Biosensor Standard G6PD Test and assessed based on their ability to understand the test workflow and interpret results. The assessment was done through a questionnaire, designed to assess product usability against key technical product specifications and fulfill regulatory evidence requirements. Any participant who obtained 85% or above correct responses to the questionnaire was considered to adequately comprehend how to use and interpret the test. Results Forty-five participants, including malaria microscopists, laboratory staff, nurses, and community health workers took part in the study. Seventy-eight percent of all participants in the study (35/45) obtained passing scores on the assessment with minimal training. Responses to the multiple-choice questions indicate that most participants understood well the test intended use, safety claims, and warnings. The greatest source of error regarding the test was around the correct operating temperature. Most test results were also read and interpreted correctly, with the haemoglobin measurement being a more problematic output to interpret than the G6PD measurement. Conclusions These data results show how a standardized tool can be used to assess a user’s ability to run a point-of-care diagnostic and interpret results. When applied to the SD Biosensor Standard G6PD Test, this tool demonstrates that a range of users across multiple contexts can use the test and suggests improvements to the test instructions and training that can improve product usability, increase user comprehension, and ultimately contribute to more widespread effective use of point-of-care G6PD tests. Trial registration: NCT04033640

This paper provides an overview of the discussion and presentations from the Workshop on the Management of Large CryoEM Facilities held at the New York Structural Biology Center, New York, NY on February 6-7, 2017. A major objective of the workshop was to discuss best practices for managing cryoEM facilities. The discussions were largely focused on supporting single-particle methods for cryoEM and topics included: user access, assessing projects, workflow, sample handling, microscopy, data management and processing, and user training.

The Harvard Cryo-Electron Microscopy Center for Structural Biology, which was formed as a consortium between Harvard Medical School, Boston Children’s Hospital, Dana-Farber Cancer Institute, and Massachusetts General Hospital, serves both academic and commercial users in the greater Harvard community. The facility strives to optimize research productivity while training users to become expert electron microscopists. These two tasks may be at odds and require careful balance to keep research projects moving forward while still allowing trainees to develop independence and expertise. This article presents the model developed at Harvard Medical School for running a research-oriented cryo-EM facility. Being a research-oriented facility begins with training in cryo-sample preparation on a trainee’s own sample, ideally producing grids that can be screened and optimized on the Talos Arctica via multiple established pipelines. The first option, staff assisted screening, requires no user experience and a staff member provides instant feedback about the suitability of the sample for cryo-EM investigation and discusses potential strategies for sample optimization. Another option, rapid access, allows users short sessions to screen samples and introductory training for basic microscope operation. Once a sample reaches the stage where data collection is warranted, new users are trained on setting up data collection for themselves on either the Talos Arctica or Titan Krios microscope until independence is established. By providing incremental training and screening pipelines, the bottleneck of sample preparation can be overcome in parallel with developing skills as an electron microscopist. This approach allows for the development of expertise without hindering breakthroughs in key research areas.

Online coadaptive training has been successfully employed to enable people to control motor imagery (MI)-based brain-computer interfaces (BCIs), allowing to completely skip the lengthy and demotivating open-loop calibration stage traditionally applied before closed-loop control. However, practical reasons may often dictate to eventually switch off decoder adaptation and proceed with BCI control under a fixed BCI model, a situation that remains rather unexplored. This work studies the existence and magnitude of potential post-adaptation effects on system performance, subject learning and brain signal modulation stability in a state-of-the-art, coadaptive training regime inspired by a game-like design. The results extracted in a cohort of 20 able-bodied individuals reveal that ceasing classifier adaptation after three runs (approx. 30 min) of a single-session training protocol had no significant impact on any of the examined BCI control and learning aspects in the remaining two runs (about 20 min) with a fixed classifier. Fifteen individuals achieved accuracies that are better than chance level and allowed them to successfully execute the given task. These findings alleviate a major concern regarding the applicability of coadaptive MI BCI training, thus helping to further establish this training approach and allow full exploitation of its benefits.

Background: Point-of-care glucose-6-phosphate dehydrogenase (G6PD) testing has the potential to make the use of radical treatment for vivax malaria safer and more effective. Widespread use of G6PD tests as part of malaria case management has been limited, in part due to due concerns regarding product usability, user training, and supervision. This study seeks to assess how well end users can understand the Standard™ G6PD Test (SD Biosensor, Suwon, South Korea) workflow, result output, and label after training. This will ultimately help inform test registration and introduction. Methods: Potential G6PD test users who provide malaria case management at three sites in Brazil, Ethiopia, and India were trained on the use of the SD Biosensor Standard G6PD Test and assessed based on their ability to understand the test workflow and interpret results. The assessment was done through a questionnaire, designed to assess product usability against key technical product specifications and fulfill regulatory evidence requirements. Any participant who obtained 85% or above correct responses to the questionnaire was considered to adequately comprehend how to use and interpret the test. Results: Forty-five participants, including malaria microscopists, laboratory staff, nurses, and community health workers took part in the study. Seventy-eight percent of all participants in the study (35/45) obtained passing scores on the assessment with minimal training. Responses to the multiple-choice questions indicate that most participants understood well the test intended use, safety claims, and warnings. The greatest source of error regarding the test was around the correct operating temperature. Most test results were also read and interpreted correctly, with the haemoglobin measurement being a more problematic output to interpret than the G6PD measurement. Conclusions: These data results show how a standardized tool can be used to assess a user's ability to run a point-of-care diagnostic and interpret results. When applied to the SD Biosensor Standard G6PD Test, this tool demonstrates that a range of users across multiple contexts can use the test and suggests improvements to the test instructions and training that can improve product usability, increase user comprehension, and ultimately contribute to more widespread effective use of point-of-care G6PD tests. Trial registration: NCT04033640 [ABSTRACT FROM AUTHOR]

A partir de experiencias recogidas en actividades de formación de usuarios, se propone una mirada sobre algunas relaciones entre el concepto de información vinculado con la comunicación en la sociedad y con la acción de las personas sobre sí mismas, por un lado, y por otro el desarrollo de competencias informacionales adaptadas a las posibilidades y a los desafíos del mundo actual, como requisito para una mayor libertad. = Based on experiences gathered in user training activities, some relationships between the concept of information linked to communication in society and the action of people on themselves are analized along with the development of informational skills adapted to the possibilities and the challenges of the current world, as a requirement for greater freedom.

While often presented as promising assistive technologies for motor-impaired users, electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) remain barely used outside laboratories due to low reliability in real-life conditions. There is thus a need to design long-term reliable BCIs that can be used outside-of-the-lab by end-users, e.g., severely motor-impaired ones. Therefore, we propose and evaluate the design of a multi-class Mental Task (MT)-based BCI for longitudinal training (20 sessions over 3 months) of a tetraplegic user for the CYBATHLON BCI series 2019. In this BCI championship, tetraplegic pilots are mentally driving a virtual car in a racing video game. We aimed at combining a progressive user MT-BCI training with a newly designed machine learning pipeline based on adaptive Riemannian classifiers shown to be promising for real-life applications. We followed a two step training process: the first 11 sessions served to train the user to control a 2-class MT-BCI by performing either two cognitive tasks (REST and MENTAL SUBTRACTION) or two motor-imagery tasks (LEFT-HAND and RIGHT-HAND). The second training step (9 remaining sessions) applied an adaptive, session-independent Riemannian classifier that combined all 4 MT classes used before. Moreover, as our Riemannian classifier was incrementally updated in an unsupervised way it would capture both within and between-session non-stationarity. Experimental evidences confirm the effectiveness of this approach. Namely, the classification accuracy improved by about 30% at the end of the training compared to initial sessions. We also studied the neural correlates of this performance improvement. Using a newly proposed BCI user learning metric, we could show our user learned to improve his BCI control by producing EEG signals matching increasingly more the BCI classifier training data distribution, rather than by improving his EEG class discrimination. However, the resulting improvement was effective only on synchronous (cue-based) BCI and it did not translate into improved CYBATHLON BCI game performances. For the sake of overcoming this in the future, we unveil possible reasons for these limited gaming performances and identify a number of promising future research directions. Importantly, we also report on the evolution of the user's neurophysiological patterns and user experience throughout the BCI training and competition.

This study aims to explore the effects of the segmentation of video tutorials on the implicit and explicit experience of the users in the hands-on training tasks. A laboratory experiment was conducted in the context of user training. Participants (n ​= ​20) were randomly assigned to one of two experimental conditions: with segmented or non-segmented versions of the same video tutorials. The participants had to watch an instructional video and recreate afterward the steps presented. Self-reported and psychophysiological measures of user experience were collected. The results suggest that while the implicit experience appears to be similar across condition, the explicit experience is significantly different: segmented tutorials lead to a better self-perceived experience and a better perceived usability of the software. This study makes several contributions to the user training and multimedia learning literature, such as the effect that the segmentation of tutorials generates on the user experience, as well as reinforcing the need to use concomitantly complementary methods to assess the experience of the users, such as implicit and explicit measures.

While often presented as promising assistive technologies for motor-impaired users, electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) remain barely used outside laboratories due to low reliability in real-life conditions. There is thus a need to design long-term reliable BCIs that can be used outside-of-the-lab by end-users, e.g., severely motor-impaired ones. Therefore, we propose and evaluate the design of a multi-class Mental Task (MT)-based BCI for longitudinal training (20 sessions over 3 months) of a tetraplegic user for the CYBATHLON BCI series 2019. In this BCI championship, tetraplegic pilots are mentally driving a virtual car in a racing video game. We aimed at combining a progressive user MT-BCI training with a newly designed machine learning pipeline based on adaptive Riemannian classifiers shown to be promising for real-life applications. We followed a two step training process: the first 11 sessions served to train the user to control a 2-class MT-BCI by performing either two cognitive tasks (REST and MENTAL SUBTRACTION) or two motor-imagery tasks (LEFT-HAND and RIGHT-HAND). The second training step (9 remaining sessions) applied an adaptive, session-independent Riemannian classifier that combined all 4 MT classes used before. Moreover, as our Riemannian classifier was incrementally updated in an unsupervised way it would capture both within and between-session non-stationarity. Experimental evidences confirm the effectiveness of this approach. Namely, the classification accuracy improved by about 30% at the end of the training compared to initial sessions. We also studied the neural correlates of this performance improvement. Using a newly proposed BCI user learning metric, we could show our user learned to improve his BCI control by producing EEG signals matching increasingly more the BCI classifier training data distribution, rather than by improving his EEG class discrimination. However, the resulting improvement was effective only on synchronous (cue-based) BCI and it did not translate into improved CYBATHLON BCI game performances. For the sake of overcoming this in the future, we unveil possible reasons for these limited gaming performances and identify a number of promising future research directions. Importantly, we also report on the evolution of the user's neurophysiological patterns and user experience throughout the BCI training and competition. [ABSTRACT FROM AUTHOR]

By performing motor-imagery tasks, for example, imagining hand movements, Motor-Imagery based Brain-Computer Interfaces (MI-BCIs) users can control digital technologies, for example, neuroprosthesis, using their brain activity only. MI-BCI users need to train, usually using a unimodal visual feedback, to produce brain activity patterns that are recognizable by the system. The literature indicates that multimodal vibrotactile and visual feedback is more effective than unimodal visual feedback, at least for short term training. However, the multi-session influence of such multimodal feedback on MI-BCI user training remained unknown, so did the influence of the order of presentation of the feedback modalities. In our experiment, 16 participants trained to control a MI-BCI during five sessions with a realistic visual feedback and five others with both a realistic visual feedback and a vibrotactile one. training benefits from a multimodal feedback, in terms of performances and self-reported mindfulness. There is also a significant influence of the order presentation of the modality. Participants who started training with a visual feedback had higher performances than those who started training with a multimodal feedback. We recommend taking into account the order of presentation for future experiments assessing the influence of several modalities of feedback. [ABSTRACT FROM AUTHOR]

Next generation prosthetics will rely massively on myoelectric ”Pattern Recognition” (PR) based control approaches, to improve their users’ dexterity. One major identified factor of successful functioning of these approaches lies in the training of amputees and in their understanding of how those prosthetics works. We thus propose here an intuitive pattern similarity biofeedback which can be easily used to train amputees and allow them to optimize their muscular contractions to improve their control performance. Experiments were conducted on twenty able-bodied participants and one transradial amputee. Their performance in controlling an interface through a myoelectric PR algorithm was evaluated; before and after a short automatic user training session consisting in using the proposed visual biofeedback for ten participants, and using a generic PR algorithm output feedback for the others ten. Participants who were trained with the proposed biofeedback increased their classification score for the retrained gesture (by 39.4%), without affecting the overall classification performance (which progressed by 10.2%) through over-training and increase of False Positive rate as observed in the control group. Additional analysis indicates a clear change in contraction strategy only in the group who used the proposed biofeedback. These preliminary results highlight the potential of this method which does not focus so much on over-optimizing the pattern recognition algorithm or on physically training the users, but on providing them simple and intuitive information to adapt or change their motor strategies to solve some misclassification issues. [ABSTRACT FROM AUTHOR]

This column describes a project funded by the Big Data to Knowledge initiative to develop online learning modules for researchers, clinicians, and informationists/librarians on aspects of next-generation sequencing projects. The modules describe a framework for next-generation sequencing projects, with particular emphasis placed on experimental design, ethical considerations, data storage, and data sharing. The author outlines how the modules were developed and summarizes their contents. [ABSTRACT FROM AUTHOR]

This paper develops a conceptual framework to explain employees' technology usage within organizations. Much of the prior information systems literature has assumed an underlying relationship between "facilitating conditions" for information technology (IT) adoption (e.g., user training, technical support, resource availability) and employees' technology use. Although these facilitating conditions are important, they do not provide a complete explanation of employees' IT usage. The reality of working in organizational settings suggests a different model of IT adoption and usage. Drawing from research on social information processing theory, and acknowledging the role of other individuals within the work context that shapes employees' learning, values, and behavior, we propose a framework to explain employees' adoption of IT and their level of usage within organizations, featuring both individual-level factors and factors related to the social information processing influence of co-workers. Our results show that an employee's coworkers exert an important influence on IT usage, whereas individual-level factors exhibit more modest effects. [ABSTRACT FROM AUTHOR]

Based on experiences gathered in user training activities, some relationships between the concept of information linked to communication in society and the action of people on themselves are analized along with the development of informational skills adapted to the possibilities and the challenges of the current world, as a requirement for greater freedom. [ABSTRACT FROM AUTHOR]

Smartphone clicker apps are increasingly used in university classrooms to facilitate teacher–student interaction and collaborative learning. This study aimed to identify the factors that influence teachers' decisions to adopt smartphone clicker app technology to enhance teacher–student interactions in university classrooms in Saudi Arabia. A mixed-method study design was employed in this study. Thirty-three teachers from a Computer Science faculty completed a questionnaire and 14 of them participated in focus group interviews to provide their views. Two main findings emerged in this study: positive and significant relationships between teachers' perceptions of the smartphone clicker app's ease of use and its perceived usefulness; and a significant relationship between teachers' perceptions of the usefulness of the smartphone clicker app and their attitude towards its use in the classroom. This study also identified that training on how to implement the smartphone clicker app effectively in lesson activities is a significant influence on teachers' perceptions of the usefulness of, and their decision to use, the app. The main implication of these findings is that smartphone clicker app developers and user training coordinators must consider teachers' perceptions of the suitability of the technology and their desire to design learning tasks to facilitate student participation and engagement. [ABSTRACT FROM AUTHOR]

By performing motor-imagery tasks, for example, imagining hand movements, Motor-Imagery based Brain-Computer Interfaces (MI-BCIs) users can control digital technologies, for example, neuroprosthesis, using their brain activity only. MI-BCI users need to train, usually using a unimodal visual feedback, to produce brain activity patterns that are recognizable by the system. The literature indicates that multimodal vibrotactile and visual feedback is more effective than unimodal visual feedback, at least for short term training. However, the multi-session influence of such multimodal feedback on MI-BCI user training remained unknown, so did the influence of the order of presentation of the feedback modalities. In our experiment, 16 participants trained to control a MI-BCI during five sessions with a realistic visual feedback and five others with both a realistic visual feedback and a vibrotactile one. training benefits from a multimodal feedback, in terms of performances and self-reported mindfulness. There is also a significant influence of the order presentation of the modality. Participants who started training with a visual feedback had higher performances than those who started training with a multimodal feedback. We recommend taking into account the order of presentation for future experiments assessing the influence of several modalities of feedback.

Development and establishment of information systems is a costly milestone for organizations, affecting the efficiency and performance of their core activities. Furthermore, education management systems as a workflow encompassed by various flows of information from different sources, have a pivotal role in day-to-day activities of universities.User satisfaction is a major criterion for an information system's success. It affects user's work efficiency and systems's practical usabilty for the user. Focusing on Golestan System, the education management system used at University of Tehran; we formulate a model for user satisfaction in education management systems; especially regarding the effect of user perception of ease of use, user perception of usefulness, user involvement in system's development, user training, senior management support and user experience. Our research population consists of all staff workers in educational services department and based on our findings, user perception of ease of use, user training and senior management support are the most influential parameters on user satisfaction and user perception of usefulness, user involvement in system's development and user experience are not significantly effective.

It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback-based user training, while conventional classifier-feedback methods, i.e., label feedback, hardly achieve any improvement. The result concludes that the use of proper classifier feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition-based prosthetic device manipulation. [ABSTRACT FROM AUTHOR]

Photovoltaic stand-alone systems are largely regarded as a viable option for decentralized rural electrification in developing countries. However, literature review reveals lack of documented experiences with installed PV systems such as Solar home systems as well as general problems with system maintenance and battery up keeping. This paper presents results from monitoring 31 stand-alone PV systems in remote health posts of North Gondar Zone in Ethiopia from installation until system failure; several systematic factors were found to have contributed to failure: lack of clear responsibility for the systems due to regular job rotation among health workers and lack of upfront, gender sensitive training, lack of equipment for maintenance work, very slow and unreliable chain of information in case of system failure and costly double tracking of energy supply. Nonfunctioning PV systems were found to threaten the technology'’s reputation by word of mouth. The results gained in this research provide important lessons for future programs of rural electrification by means of PV systems: they stress the importance of awareness building amongst funding agencies as well as the imperative of intense and sensitive training for users, especially women, and advocate for considering living conditions of users in system design. [ABSTRACT FROM AUTHOR]