Special collection in association with the 2023 International Conference on aging, innovation and rehabilitation
Bibliographic record
Abstract
We are happy to introduce this collection of research articles in association with the International Conference on aging, innovation and rehabilitation (ICAIR).The 2023 conference was an interdisciplinary event that brought together leading researchers, scientists, and entrepreneurs, dedicated to enhancing the quality of life for individuals who face challenges related to aging and disability (Fig. 1).The conference was jointly hosted by The KITE Research Institute | Toronto Rehabilitation Institute-University Health Network (Fig. 2) and the Rehabilitation Sciences Institute at the University of Toronto, with contributions and participation from other clinical and research institutions and hospitals worldwide.Abstracts submitted to the conference underwent peer review process and were selected for poster or podium presentations.A small subset of the abstracts, which received the highest review scores, were invited to submit a full-length manuscript for review and potential publication in this collection.These submissions underwent standard peer-review process at the journal and, after reviews, rebuttals, and revisions, twelve were accepted for publication, representing a wide range of techniques and applications related to health monitoring, assessment, and rehabilitation.While covering diverse topics, articles in this collection are linked through multiple connecting themes, such as functional electrical stimulation [1,2] or the application of signal processing and artificial intelligence in solving aging and rehabilitation problems [2-8].Specifically, a number of the papers in this collection [4-7] explore the application of computer vision techniques in various healthcare domains, particularly focusing on rehabilitation and mobility assistance.Lim et al. [2], for instance, investigate the feasibility of using depth cameras and pressure mats in a balance training system for individuals with spinal cord injuries.A previous pilot study had shown the potential of a visual-feedback balance training (VFBT), coupled with closed-loop functional electrical stimulation (FES), to improve the standing balance in individuals with incomplete spinal-cord injury/disease [9, 10].However, clinical implementation of such systems would be limited because of the required force plates, which are expensive and not easily accessible.Lim et al. [2] experimentally demonstrate that depth cameras and pressure mats can accurately track the body center of mass and center of pressure.As another example,
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.009 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.029 | 0.026 |
| Insufficient payload (model declined to judge) | 0.060 | 0.039 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".