Brain-controlled wheelchair (BCW) is one of the important applications of brain-computer interface (BCI) technology. The present research shows that simulation control training is of great significance for the application of BCW. In order to improve the BCW control ability of users and promote the application of BCW under the condition of safety, this paper builds an indoor simulation training system based on the steady-state visual evoked potentials for BCW. The system includes visual stimulus paradigm design and implementation, electroencephalogram acquisition and processing, indoor simulation environment modeling, path planning, and simulation wheelchair control, etc. To test the performance of the system, a training experiment involving three kinds of indoor path-control tasks is designed and 10 subjects were recruited for the 5-day training experiment. By comparing the results before and after the training experiment, it was found that the average number of commands in Task 1, Task 2, and Task 3 decreased by 29.5%, 21.4%, and 25.4%, respectively ( < 0.001). And the average number of commands used by the subjects to complete all tasks decreased by 25.4% ( < 0.001). The experimental results show that the training of subjects through the indoor simulation training system built in this paper can improve their proficiency and efficiency of BCW control to a certain extent, which verifies the practicability of the system and provides an effective assistant method to promote the indoor application of BCW.
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http://dx.doi.org/10.7507/1001-5515.201906025 | DOI Listing |
BMC Med Educ
January 2025
School of Nursing, Seirei Christopher University, Hamamatsu, Shizuoka, Japan.
Background: Point-of-care ultrasound (POCUS) can be used in a variety of clinical settings and is a safe and powerful tool for ultrasound-trained healthcare providers, such as physicians and nurses; however, the effectiveness of ultrasound education for nursing students remains unclear. This prospective cohort study aimed to examine the sustained educational impact of bladder ultrasound simulation among nursing students.
Methods: To determine whether bladder POCUS simulation exercises sustainably improve the clinical proficiency regarding ultrasound examinations among nursing students, evaluations were conducted before and after the exercise and were compared with those after the 1-month follow-up exercise.
Sci Rep
January 2025
School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, India.
Mechanical ventilation is the process through which breathing support is provided to patients who face inconvenience during respiration. During the pandemic, many people were suffering from lung disorders, which elevated the demand for mechanical ventilators. The handling of mechanical ventilators is to be done under the assistance of trained professionals and demands the selection of ideal parameters.
View Article and Find Full Text PDFFuture Cardiol
January 2025
Division of Cardiac Surgery, University of Toronto, Toronto, ON, Canada.
Reaching competency in congenital heart surgery (CHS) requires lengthy and rigorous training. Due to patient safety, time limitations, and procedural complexity, the intraoperative setting is not ideal for technical practice. Surgical simulation using synthetic, biological, or virtual models is an increasingly valuable educational tool for technical training and assessment.
View Article and Find Full Text PDFPaediatr Anaesth
January 2025
University of Washington School of Medicine, Seattle, Washington, USA.
Introduction: The Society for Pediatric Anesthesia Quality and Safety Committee developed the Pediatric Regional Anesthesia Time-Out Checklist, consisting of 14 safety items intended to be reviewed by an anesthesia team prior to a regional anesthetic. Primarily, we hypothesized that use of this Checklist would increase the number of safety items performed compared with no checklist, evaluating the usefulness of this tool. Secondarily, we hypothesized that, after checklist training, subjects would show better clinical judgment by electing to perform a regional anesthetic in scenarios in which no programmed error existed and electing to not perform a regional anesthetic in scenarios in which a programmed error did exist.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Center for Generic Aerospace Technology, Huanjiang Laboratory, Zhuji 311816, China.
This paper introduces Re-DQN, a deep reinforcement learning-based algorithm for comprehensive coverage path planning in lawn mowing robots. In the fields of smart homes and agricultural automation, lawn mowing robots are rapidly gaining popularity to reduce the demand for manual labor. The algorithm introduces a new exploration mechanism, combined with an intrinsic reward function based on state novelty and a dynamic input structure, effectively enhancing the robot's adaptability and path optimization capabilities in dynamic environments.
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