Design and Implementation of Intelligent Agent Training Systems for Virtual Vehicles.
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ABSTRACT:
SUBMITTER: Urrea C
PROVIDER: S-EPMC7827925 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In order to solve the problems of poor physical fitness of college students and low efficiency of college sport venues' management, an intelligent sports management system based on deep learning technology is designed by using information technology and human-computer interaction under artificial intelligence. Based on the Browser/Server (B/S) structure, the intelligent sports management system is constructed. The basic framework of Spring Cloud is used to integrate the framework and components of each part, and a distributed microservice system is built. The artificial intelligence recommendation algorithm is used to analyze the user's age, body mass index (BMI), and physical health status, and recommend sports programs suitable for students, thus realizing the intelligent sports program recommendation function. At the same time, the recommendation algorithm is used to complete the course recommendation according to the students' preferences, teaching distance, opening time, course evaluation, and other indexes, and the course registration system is constructed; after the analysis of the entity and the relationship between the entities of the intelligent sports system, the database relational model of the system is designed with the entity relationship (E-R) diagram. The results of the functional test show that the system can run well. In conclusion, the sports training environment instructional system based on artificial intelligence and deep learning technology can meet the teaching needs of colleges, improve the sports' quality for college students, and promote psychological education.
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Project description:For autonomous vehicles (AV), the ability to share information about their surroundings is crucial. With Level 4 and 5 autonomy in sight, solving the challenge of organization and efficient storing of data, coming from these connected platforms, becomes paramount. Research done up to now has been mostly focused on communication and network layers of V2X (Vehicle-to-Everything) data sharing. However, there is a gap when it comes to the data layer. Limited attention has been paid to the ontology development in the automotive domain. More specifically, the way to integrate sensor data and geospatial data efficiently is missing. Therefore, we proposed to develop a new Connected Traffic Data Ontology (CTDO) on the foundations of Sensor, Observation, Sample, and Actuator (SOSA) ontology, to provide a more suitable ontology for large volumes of time-sensitive data coming from multi-sensory platforms, like connected vehicles, as the first step in closing the existing research gap. Additionally, as this research aims to further extend the CTDO in the future, a possible way to map to the CTDO with ontologies that represent road infrastructure has been presented. Finally, new CTDO ontology was benchmarked against SOSA, and better memory performance and query execution speeds have been confirmed.
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Project description:BackgroundHome-based renal replacement therapy (RRT) [peritoneal dialysis (PD) and home hemodialysis (HHD)] offers independent quality of life and clinical advantages compared to conventional in-center hemodialysis. However, follow-up may be less complete for home dialysis patients following a change in care settings such as post hospitalization. We aim to implement a Home Dialysis Virtual Ward (HDVW) strategy, which is targeted to minimize gaps of care.Methods/designThe HDVW Pilot Study will enroll consecutive PD and HHD patients who fulfilled any one of our inclusion criteria: 1. following discharge from hospital, 2. after interventional procedure(s), 3. prescription of anti-microbial agents, or 4. following completion of home dialysis training. Clinician-led telephone interviews are performed weekly for 2 weeks until VW discharge. Case-mix (modified Charlson Comorbidity Index), symptoms (the modified Edmonton Symptom Assessment Scale) and patient satisfaction are assessed serially. The number of VW interventions relating to eight pre-specified domains will be measured. Adverse events such as re-hospitalization and health-services utilization will be ascertained through telephone follow-up after discharge from the VW at 2, 4, 12 weeks. The VW re-hospitalization rate will be compared with a contemporary cohort (matched for age, gender, renal replacement therapy and co-morbidities). Our protocol has been approved by research ethics board (UHN: 12-5397-AE). Written informed consent for participation in the study will be obtained from participants.DiscussionThis report serves as a blueprint for the design and implementation of a novel health service delivery model for home dialysis patients. The major goal of the HDVW initiative is to provide appropriate and effective supports to medically complex patients in a targeted window of vulnerability.Trial registration(NCT01912001).
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Project description:Driving intelligence tests are critical to the development and deployment of autonomous vehicles. The prevailing approach tests autonomous vehicles in life-like simulations of the naturalistic driving environment. However, due to the high dimensionality of the environment and the rareness of safety-critical events, hundreds of millions of miles would be required to demonstrate the safety performance of autonomous vehicles, which is severely inefficient. We discover that sparse but adversarial adjustments to the naturalistic driving environment, resulting in the naturalistic and adversarial driving environment, can significantly reduce the required test miles without loss of evaluation unbiasedness. By training the background vehicles to learn when to execute what adversarial maneuver, the proposed environment becomes an intelligent environment for driving intelligence testing. We demonstrate the effectiveness of the proposed environment in a highway-driving simulation. Comparing with the naturalistic driving environment, the proposed environment can accelerate the evaluation process by multiple orders of magnitude.
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Project description:The COVID-19 pandemic drove a rapid transition to virtual care experiences for graduate medical trainees. Core training competencies have expanded to incorporate virtual contexts, however there is limited knowledge of the optimal design of virtual care training tools for learners. In this study, we describe the application of a Design Thinking approach to the identification and co-design of novel training tools to support residents and precepting attending physicians in virtual ambulatory care practice. We applied the model of “Empathize, Define, Ideate, Prototype, and Test” via a mixed methods approach to (1) explore the needs, preferences, and concerns of Internal Medicine residents and outpatient precepting attendings regarding virtual ambulatory care training environments, and (2) evaluate, prototype, and test potential training tools. Eleven residents and eight attending physicians participated. Identified learner needs and problem areas included: improving virtual visit technical skills; acquiring virtual communication skills; adapting to the loss of shared in-person learning space and optimizing virtual learning environments; remediating non-virtual procedural competencies; and educating on new documentation requirements. Key solution areas included: virtual precepting support tools; digital information and education dissemination tools; and strategies for management of technical issues. Several prototypes were proposed, with a single tool (a virtual preceptor tip sheet) deployed in clinical practice. Residents found the workshop program improved their understanding of Design Thinking and its relevance to healthcare. Ultimately, Design Thinking can be deployed to engage medical trainees and precepting attendings in the effective development of novel educational tools for the virtual care learning environment.
| S-EPMC9234169 | biostudies-literature
Project description:A self-powered triboelectric nanogenerator (SPTENG) based on triboelectric effect and an intelligent interactive system are fabricated for monitoring shooting training and virtual training. The SPTENG is composed of latex and PTFE and an intelligent system. Based on triboelectric effect, the SPTENG can be used to monitor the progress of trigger pressing without a power supply (this is supplied by trigger movements). Because of the flexible properties, it can be attached to a trigger conveniently to monitor the progress of trigger pressing, such as trigger time, trigger stability, etc. Meanwhile, as part of an intelligent shooting system, police can formulate a standard scheme according to signals to improve their skills. Furthermore, they can use it to train between reality and virtuality. Therefore, it has a wide development space in human-computer interaction and real-time information processing.
| S-EPMC9500841 | biostudies-literature
Project description:BackgroundStroke management in rural areas is more variable and there is less access to reperfusion therapies, when compared with metropolitan areas. Delays in treatment contribute to worse patient outcomes. To improve stroke management in rural areas, health districts are implementing telestroke networks. The New South Wales Telestroke Service provides neurologist-led telehealth to 23 rural spoke hospitals aiming to improve treatment delivery and patient outcomes. The training of clinical staff was identified as a critical aspect for the successful implementation of this service. Virtual reality (VR) training has not previously been used in this context.ObjectiveWe sought to develop an evidence-based VR training module specifically tailored for stroke telehealth. During implementation, we aimed to assess the feasibility of workplace deployment and collected feedback from spoke hospital staff involved in stroke management on training acceptability and usability as well as perceived training impact.MethodsThe TACTICS VR Stroke Telehealth application was developed with subject matter experts. During implementation, both quantitative and qualitative data were documented, including VR use and survey feedback. VR hardware was deployed to 23 rural hospitals, and use data were captured via automated Wi-Fi transfer. At 7 hospitals in a single local health district, staff using TACTICS VR were invited to complete surveys before and after training.ResultsTACTICS VR Stroke Telehealth was deployed to rural New South Wales hospitals starting on April 14, 2021. Through August 20, 2023, a total of 177 VR sessions were completed. Survey respondents (n=20) indicated a high level of acceptability, usability, and perceived training impact (eg, accuracy and knowledge transfer; mean scores 3.8-4.4; 5=strongly agree). Furthermore, respondents agreed that TACTICS VR increased confidence (13/18, 72%), improved understanding (16/18, 89%), and improved awareness (17/18, 94%) regarding stroke telehealth. A comparison of matched pre- and posttraining responses revealed that training improved the understanding of telehealth workflow practices (after training: mean 4.2, SD 0.6; before training: mean 3.2, SD 0.9; P<.001), knowledge on accessing stroke telehealth (mean 4.1, SD 0.6 vs mean 3.1, SD 1.0; P=.001), the awareness of stroke telehealth (mean 4.1, SD 0.6 vs mean 3.4, SD 0.9; P=.03), ability to optimally communicate with colleagues (mean 4.2, SD 0.6 vs mean 3.7, SD 0.9; P=.02), and ability to make improvements (mean 4.0, SD 0.6 vs mean 3.5, SD 0.9; P=.03). Remote training and deployment were feasible, and limited issues were identified, although uptake varied widely (0-66 sessions/site).ConclusionsTACTICS VR Stroke Telehealth is a new VR application specifically tailored for stroke telehealth workflow training at spoke hospitals. Training was considered acceptable, usable, and useful and had positive perceived training impacts in a real-world clinical implementation context. Additional work is required to optimize training uptake and integrate training into existing education pathways.
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Project description:Delays in acute stroke treatment contribute to severe and negative impacts for patients and significant healthcare costs. Variability in clinical care is a contributor to delayed treatment, particularly in rural, regional and remote (RRR) areas. Targeted approaches to improve stroke workflow processes improve outcomes, but numerous challenges exist particularly in RRR settings. Virtual reality (VR) applications can provide immersive and engaging training and overcome some existing training barriers. We recently initiated the TACTICS trial, which is assessing a "package intervention" to support advanced CT imaging and streamlined stroke workflow training. As part of the educational component of the intervention we developed TACTICS VR, a novel VR-based training application to upskill healthcare professionals in optimal stroke workflow processes. In the current manuscript, we describe development of the TACTICS VR platform which includes the VR-based training application, a user-facing website and an automated back-end data analytics portal. TACTICS VR was developed via an extensive and structured scoping and consultation process, to ensure content was evidence-based, represented best-practice and is tailored for the target audience. Further, we report on pilot implementation in 7 Australian hospitals to assess the feasibility of workplace-based VR training. A total of 104 healthcare professionals completed TACTICS VR training. Users indicated a high level of usability, acceptability and utility of TACTICS VR, including aspects of hardware, software design, educational content, training feedback and implementation strategy. Further, users self-reported increased confidence in their ability to make improvements in stroke management after TACTICS VR training (post-training mean ± SD = 4.1 ± 0.6; pre-training = 3.6 ± 0.9; 1 = strongly disagree, 5 = strongly agree). Very few technical issues were identified, supporting the feasibility of this training approach. Thus, we propose that TACTICS VR is a fit-for-purpose, evidence-based training application for stroke workflow optimisation that can be readily deployed on-site in a clinical setting.
| S-EPMC8631764 | biostudies-literature
Project description:Re-using faecal sludge (FS) to generate value has the potential to contribute towards solving the issue of long term sanitation solutions in growing urban areas across sub-Saharan Africa; however, hitherto, no design tools have been available that are capable of simulating a system involving economic factors, complex social issues and environmental circumstances. We hypothesized that Agent-Based Modelling (ABM), when deployed with appropriate rigour, can provide such a tool. Extensive field work was carried out in a Malawian city, investigating the adoption of Skyloo above-ground composting toilets by households, and the operation of the municipal FS site. 65 semi-structured interviews and 148 household interviews, together with observations, were carried out to characterize these processes, with the data acquired being used to construct two separate ABMs. The Skyloo ABM was run for various scenarios of start-up capital for business and payback of loans against the toilet cost to households. The municipal FS Site ABM was run for different patterns of dumping fee and enforcement structure. The field work demonstrated that there is potential for further expansion of FS reuse, with a market for agricultural application. The Skyloo ABM identified the significance of start-up capital for a business installing the toilet technology; the municipal FS Site ABM showed that existing fees, fines and regulatory structure were insufficient to reduce illegal dumping of FS to any useful degree, but that a monthly permit system would provide enhanced revenue to the city council compared with per-visit charging of disposal companies at the municipal FS site. Whilst each ABM ideally requires some additional data before full application, we have, for the first time, shown that ABM provides a basis for the simulation-based design of FS management systems, including complex social, economic and environmental factors.
| S-EPMC6479626 | biostudies-other