Smart Wearable Technologies for Balance Rehabilitation in Older Adults at Risk of Falls: Scoping Review and Comparative Analysis
Bibliographic record
Abstract
Background: Falls among older adults are a significant public health concern, often leading to severe injuries, decreased quality of life, and substantial health care costs. Smart wearable technologies for balance rehabilitation present a promising avenue for addressing the falls epidemic, capable of providing detailed objective movement data, engaging visuals, and real-time feedback. With the recent and rapid evolution of innovative technologies, including artificial intelligence (AI), augmented reality (AR) or virtual reality (VR), and motion tracking, there is a need to evaluate the market to identify the most effective and accessible smart balance systems currently available. Objective: This study aims to evaluate the current landscape of smart wearable technology systems for balance rehabilitation in older adults at risk of falls. In addition, it aims to compare market-available systems to the telerehabilitation of balance clinical and economic decision support system (TeleRehab DSS), a recently developed smart balance system. Methods: A scoping review and strengths, weaknesses, opportunities, and threats (SWOT) analysis was completed, exploring the landscape of smart balance systems in older adults at risk of falls. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, electronic databases PubMed, MEDLINE, and Cochrane were systematically searched for articles in English from July 1, 2014, to July 1, 2024. Gray literature searches of relevant institutions and web pages were also conducted. The database search and commercial systems were then compared against the TeleRehab DSS in a SWOT analysis. Results: The scoping review yielded 17 systems that met the inclusion criteria; 10 investigational systems and 7 commercially available systems. Out of 10 studies, only 1 reported the use of intelligent learning or AI, 8 studies reported the use of motion tracking, and 9 studies used virtual reality. Of the studies incorporating motion tracking, 3 provided feedback as either visual or auditory. All but 2 studies reported the use of gamification, and 7 studies incorporated balance exercises. In total, 2 studies reported remote delivery, with 5 being clinician-supervised and 4 providing a clinician report. The SWOT analysis of TeleRehab DSS against the 7 market-available smart balance systems revealed several unique advantages, including personalized therapy with AI-DSS, AR for real-world interaction, enhanced clinician involvement, and comprehensive data analytics. Conclusions: The findings from this scoping review highlight the rapid evolution of smart balance systems, yet significant gaps remain in AI integration, remote accessibility, and clinician-driven data analytics. Despite limitations such as cost, accessibility, and user training requirements, TeleRehab DSS emerges as a significant innovation, addressing many of these gaps through AI-driven personalization, AR for real-world interaction, and real-time clinician monitoring. These features position it as a next-generation solution that aligns closely with the evolving needs of patients and clinicians. The results of this review provide valuable insights for future research, supporting the need for further validation studies and the development of more intelligent and accessible balance rehabilitation technologies.
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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 | 0.016 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".