An Online Balance Training Application using Pose Estimation and Augmented Reality
Bibliographic record
Abstract
The evolution of digitally connected devices and artificial intelligence has opened the door for novel health and fitness applications that can be used by individuals at a time and in an environment convenient to them. The purpose of our research was to develop a platform that requires no additional hardware to provide an online balance training program. Balance exercises are often prescribed for healthy aging to keep the body active, improve balance and coordination, and prevent falls and injuries, as well as, for those doing rehabilitation after injuries or diseases such as stroke. We developed a simple web application (BaART: Balance Augmented Reality Trainer) that uses PoseNet to determine a user's location and pose to count the number of repetitions that were done successfully. Furthermore, we looked at how augmented reality, and specifically adding a virtual chair, might impact a user's sense of balance. In a study of 20 participants with and without balance disorders, we found that the developed system was easy to use and many would consider using such a system, particularly our older participants who spend more time at home. However, we also found that the virtual object (i.e. chair) was not used by most people. Furthermore, those with balance issues felt they required a real chair for balance and some even felt that the virtual object was distracting from the exercise. In the future, we plan to explore other uses of augmented reality, such as feedback on exercise quality, gaming features, and a virtual avatar trainer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".