Impact of a national dementia research consortium: The Canadian Consortium on Neurodegeneration in Aging (CCNA).

The Canadian Consortium on Neurodegeneration in Aging (CCNA) was created by the Canadian federal government through its health research funding agency, the Canadian Institutes for Health Research (CIHR), in 2014, as a response to the G7 initiative to fight dementia. Two five-year funding cycles (2014-2019; 2019-2024) have occurred following peer review, and a third cycle (Phase 3) has just begun. A unique construct was mandated, consisting of 20 national teams in Phase I and 19 teams in Phase II (with research topics spanning from basic to clinical science to health resource systems) along with cross-cutting programs to support them.

Assistive Technology to Support Dementia Management: Protocol for a Scoping Review of Reviews.

Background: In Canada, more than 60% of persons living with dementia reside in their own homes, and over 25% rely heavily on their care partners (ie, family members or friends) for assistance with daily activities such as personal hygiene, eating, and walking. Assistive technology (AT) is a key dementia management strategy, helping to maintain health and social support in home and community settings. AT comprises assistive products and services required for safe and effective use.

Smartphone-Based Hand Function Assessment: Systematic Review.

Background: Hand function assessment heavily relies on specific task scenarios, making it challenging to ensure validity and reliability. In addition, the wide range of assessment tools, limited and expensive data recording, and analysis systems further aggravate the issue. However, smartphones provide a promising opportunity to address these challenges.

Quantifying rider posture variability in powered two- and three-wheelers for safety assessment.

Objective: Injury outcomes for powered two- and three-wheeler (PTW) riders are influenced by the rider posture. To enable analysis of PTW rider accidents and development of protection systems, detailed whole-body posture data is needed. Therefore, the aim of this study is to fill this gap by providing collections of average male whole-body postures, including subpopulation variability, for different PTW types.

StairNet: visual recognition of stairs for human-robot locomotion.

Human-robot walking with prosthetic legs and exoskeletons, especially over complex terrains, such as stairs, remains a significant challenge. Egocentric vision has the unique potential to detect the walking environment prior to physical interactions, which can improve transitions to and from stairs. This motivated us to develop the StairNet initiative to support the development of new deep learning models for visual perception of real-world stair environments.