Purpose: To review the literature on communication technologies in rehabilitation for people with a traumatic brain injury (TBI), and: (a) determine its application to cognitive-communicative rehabilitation, and b) develop a model to guide communication technology use with people after TBI.
Method: This integrative literature review of communication technology in TBI rehabilitation and cognitive-communication involved searching nine scientific databases and included 95 studies.
Results: Three major types of communication technologies (assistive technology, augmentative and alternative communication technology, and information communication technology) and multiple factors relating to use of technology by or with people after TBI were categorized according to: (i) individual needs, motivations and goals; (ii) individual impairments, activities, participation and environmental factors; and (iii) technologies. While there is substantial research relating to communication technologies and cognitive rehabilitation after TBI, little relates specifically to cognitive-communication rehabilitation.
Conclusions: Further investigation is needed into the experiences and views of people with TBI who use communication technologies, to provide the 'user' perspective and influence user-centred design. Research is necessary to investigate the training interventions that address factors fundamental for success, and any impact on communication. The proposed model provides an evidence-based framework for incorporating technology into speech pathology clinical practice and research.
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http://dx.doi.org/10.1080/02699052.2017.1292429 | DOI Listing |
PLoS One
January 2025
Rice Department, Bangkok, Thailand.
Bacterial Leaf Blight (BLB) usually attacks rice in the flowering stage and can cause yield losses of up to 50% in severely infected fields. The resulting yield losses severely impact farmers, necessitating compensation from the regulatory authorities. This study introduces a new pipeline specifically designed for detecting BLB in rice fields using unmanned aerial vehicle (UAV) imagery.
View Article and Find Full Text PDFPLoS One
January 2025
Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia.
We explore the efficacy of multimodal behavioral cues for explainable prediction of personality and interview-specific traits. We utilize elementary head-motion units named kinemes, atomic facial movements termed action units and speech features to estimate these human-centered traits. Empirical results confirm that kinemes and action units enable discovery of multiple trait-specific behaviors while also enabling explainability in support of the predictions.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Computer and Information Science, Konstanz University, Konstanz, Baden-Württemberg, Germany.
A major challenge of our time is reducing disparities in access to and effective use of digital technologies, with recent discussions highlighting the role of AI in exacerbating the digital divide. We examine user characteristics that predict usage of the AI-powered conversational agent ChatGPT. We combine behavioral and survey data in a web tracked sample of N = 1376 German citizens to investigate differences in ChatGPT activity (usage, visits, and adoption) during the first 11 months from the launch of the service (November 30, 2022).
View Article and Find Full Text PDFNetwork
January 2025
Computer Science and Engineering, SA Engineering College, Poonamallee, India.
The optimization on the cloud-based data structures is carried out using Adaptive Level and Skill Rate-based Child Drawing Development Optimization algorithm (ALSR-CDDO). Also, the overall cost required in computing and communicating is reduced by optimally selecting these data structures by the ALSR-CDDO algorithm. The storage of the data in the cloud platform is performed using the Divide and Conquer Table (D&CT).
View Article and Find Full Text PDFIndian J Thorac Cardiovasc Surg
February 2025
Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India.
Purpose: Despite significant improvements in the design and performance of continuous flow left ventricular assist devices (CFLVADs), one of the most important reasons hampering further penetration of this technology is the occurrence of adverse events, especially strokes. One of the well-known risk factors for strokes is hypertension which is particularly common in patients undergoing a CFLVAD implant. While the device is implanted in the heart, strokes happen due to pathology in the brain and we hypothesised that modelling the blood flow in the circle of Willis might shed light on the causation of strokes in this situation.
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