Manual wheelchairs can promote independence among users. However, the user's level of disability, strength, stamina, and the environmental conditions within which the wheelchair is used may limit manual wheelchair functionality. The use of power assist add-ons may mitigate these limitations and help individuals to age in place. This scoping review analyzes scientific and gray literature to examine the use of power assist add-ons among adults across the life course who use manual wheelchairs, as well as their advantages and limitations in promoting independence and active aging. This review was guided by the PRISMA checklist for scoping reviews, and the Arksey and O'Malley review methodology. The literature search involved a keyword and MeSH search of electronic databases, proceedings, Google, Google Scholar and symposia. Articles were selected based on pre-defined inclusion criteria. Of the 945 unique titles returned, 17 articles were included. PADs such as rear-mounted power assist devices, powered main wheels, and front-end attachments were identified. Power-assist add-ons for manual wheelchairs show promise in improving mobility and reducing exertion for users. However, concerns regarding safety, indoor maneuverability, and user preferences highlight the need for specialized training and retrofitting power assist add-ons, especially among older users.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1080/10400435.2025.2451933 | DOI Listing |
J Comput Assist Tomogr
January 2025
Department of Radiology, College of Medicine, University of Florida, Gainesville, FL.
Purpose: The purpose of this work was to evaluate the image quality of a commercial CT scanner equipped with a novel detector and filtration technology called PureVision Optics (PVO).
Methods: CT number, noise, contrast-to-noise ratio (CNR), modulation transfer function (MTF), and noise power spectrum (NPS) were assessed using the ACR CT Accreditation phantom scanned with various acquisitions at 80 kV, 100 kV, 120 kV, and 135 kV, each with multiple CTDIvol values of 20 mGy, 40 mGy, and 65 mGy. Artifacts were evaluated in an anthropomorphic head phantom, a cadaver head, and in patient studies.
Broadband minimalist wireless base stations without energy-consuming electrical power amplifiers are the rosy scenario of the next-generation wireless communication systems. High-power radio-over-fiber (RoF) links, which are featured by large operation bandwidths, are regarded as the supporting technology for realizing such a vision. Nevertheless, the severe signal-to-noise ratio (SNR) deterioration induced by the second Brillouin scattering in high-power and long-distance RoF links must be first solved.
View Article and Find Full Text PDFThe self-homodyne detection (SHD) is a promising solution to achieve low-cost and low-power-consumption fiber-optic communications. In this work, we propose and demonstrate a high-capacity spatial-division multiplexing (SDM) system with SHD technology by employing single-mode multi-core fibers (SM-MCFs), where the fan-in/fan-out (FIFO) 3D photonic devices are designed and fabricated based on the femtosecond laser direct writing technique, enabling high-efficiency coupling between single-mode fibers (SMFs) and SM-MCFs. The FIFO 3D photonic devices, serving as the SDM (de)multiplexer, facilitate superior performance of low insertion loss and low inter-channel crosstalk.
View Article and Find Full Text PDFSci Rep
January 2025
Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Diffusion MRI is a leading method to non-invasively characterise brain tissue microstructure across multiple domains and scales. Diffusion-weighted steady-state free precession (DW-SSFP) is an established imaging sequence for post-mortem MRI, addressing the challenging imaging environment of fixed tissue with short T and low diffusivities. However, a current limitation of DW-SSFP is signal interpretation: it is not clear what diffusion 'regime' the sequence probes and therefore its potential to characterise tissue microstructure.
View Article and Find Full Text PDFSci Rep
January 2025
Amal Jyothi College of Engineering (Autonomous), Kanjirappally, Kerala, India.
In agriculture, promptly and accurately identifying leaf diseases is crucial for sustainable crop production. To address this requirement, this research introduces a hybrid deep learning model that combines the visual geometric group version 19 (VGG19) architecture features with the transformer encoder blocks. This fusion enables the accurate and précised real-time classification of leaf diseases affecting grape, bell pepper, and tomato plants.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!