Augmented Reality-Based Rehabilitation Program after Total Knee Arthroplasty Feasibility Study
Bibliographic record
Abstract
Introduction Knee osteoarthritis (OA) is one of the most common degenerative joint disorders [1], with a pooled global of 22.9% after the age of 40 [2]. Approximately 654.1 million individuals (over 40 years old) were diagnosed with knee OA in 2020 worldwide [2]. Total knee arthroplasty (TKA) is a recommended surgical procedure for treating advanced knee osteoarthritis [3]. During 2020-2021, 52,223 cases of TKA were performed in Canada [4]. In the United States, this is estimated to be about 800,000 cases annually, and it is expected to increase to 3.48 million by 2030 [5]. While TKA effectively reduces pain and improves functional capacity, it requires postoperative rehabilitation for faster recovery and returns to daily activities [6]. Traveling to rehabilitation appointments can be a barrier for some patients [7] to follow a rehabilitation program [7]. This situation increases the tendency for home-based rehabilitation to reduce the cost and time [8]. Several studies [13]– [16] show no significant difference between inpatient and home-based rehabilitation in functional outcomes after TKA. Over the past decade, the evolution of immersive technology led to the integration of virtual and augmented reality into rehabilitation science [9], [10]. This progress has led to the possibility of remotely delivering rehabilitation programs, fostering user engagement [9], [10]. Several studies [11]– [18] that implemented virtual reality (VR) for post-TKA rehabilitation have found no statistically significant difference between VR-based interventions and in-person rehabilitation regarding improving functional outcomes. Despite the potential benefits, VR-based rehabilitation encounters challenges such as the precision of lower limb tracking systems, technological prerequisites, equipment costs, and potential user-associated risks, limiting its widespread adoption [15]. The evolution of augmented reality (AR) technology presents a more user-friendly alternative, characterized by its compatibility with widely available devices such as smartphones and tablets [19]. AR for training purposes is anticipated to streamline usability and mitigate potential adverse effects observed in VR scenarios [19]. Given these considerations, we intend to create an Augmented Reality-Based Rehabilitation Program (ARRP) designed to administer recommended rehabilitation to TKA patients. This program holds the potential to offer an alternative rehabilitation strategy for individuals who are unable to participate in in-person rehabilitation sessions or who face difficulties in using VR headsets.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".