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Special collection in association with the 2023 International Conference on aging, innovation and rehabilitation
BioMedical Engineering OnLine volume 23, Article number: 49 (2024)
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 [2,3,4,5,6,7,8]. Specifically, a number of the papers in this collection [4,5,6,7] 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, Sabo et al. [7] demonstrate the responsiveness of a previously developed predictive vision-based machine learning model [11, 12] to measure changes in gait in response to medication and deep brain stimulation in individuals with Parkinson’s disease.
Following the success of ICAIR 2023, the conference will run as an annual event, each May in Toronto, Ontario, Canada. We plan to continue this collection in future, and each year invite a selection of some of the most exciting research projects presented at ICAIR.
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References
Marquez-Chin M, Saadatnia Z, Sun YC, et al. A dry polymer nanocomposite transcutaneous electrode for functional electrical stimulation. BioMed Eng OnLine. 2024;23:10.
Lim D, Pei W, Lee JW, et al. Feasibility of using a depth camera or pressure mat for visual feedback balance training with functional electrical stimulation. BioMed Eng OnLine. 2024;23:19.
Jawad T, Koh RGL, Zariffa J. Selective peripheral nerve recording using simulated human median nerve activity and convolutional neural networks. BioMed Eng OnLine. 2023;22:118.
Kurbis AG, Kuzmenko D, Ivanyuk-Skulskiy B, et al. StairNet: visual recognition of stairs for human–robot locomotion. BioMed Eng OnLine. 2024;23:20.
Basiri R, Manji K, LeLievre PM, et al. Protocol for metadata and image collection at diabetic foot ulcer clinics: enabling research in wound analytics and deep learning. BioMed Eng OnLine. 2024;23:12.
Barzegar Khanghah A, Fernie G, Roshan Fekr A. Joint angle estimation during shoulder abduction exercise using contactless technology. BioMed Eng OnLine. 2024;23:11.
Sabo A, Iaboni A, Taati B, et al. Evaluating the ability of a predictive vision-based machine learning model to measure changes in gait in response to medication and DBS within individuals with Parkinson’s disease. BioMed Eng OnLine. 2023;22:120.
Simmatis LER, Robin J, Spilka MJ, et al. Detecting bulbar amyotrophic lateral sclerosis (ALS) using automatic acoustic analysis. BioMed Eng OnLine. 2024;23:15.
Lee J, et al. A novel therapeutic tool for standing balance for individuals with incomplete spinal cord injury: a pilot study. Proceedings of the 22th Annual Conference of the International Functional Electrical Stimulation Society, Toronto. 2018.
Houston DJ, et al. Functional electrical stimulation plus visual feedback balance training for standing balance performance among individuals with incomplete spinal cord injury: a case series. Front Neurol. 2020;11:680.
Sabo A, et al. Assessment of Parkinsonian gait in older adults with dementia via human pose tracking in video data. J Neuroeng Rehabil. 2020;17:1–10.
Sabo A, et al. Estimating Parkinsonism severity in natural gait videos of older adults with dementia. IEEE J Biomed Health Inform. 2022;26(5):2288–98.
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Taati, B., Popovic, M.R. Special collection in association with the 2023 International Conference on aging, innovation and rehabilitation. BioMed Eng OnLine 23, 49 (2024). https://doi.org/10.1186/s12938-024-01243-x
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DOI: https://doi.org/10.1186/s12938-024-01243-x