Validity of an Interactive Functional Reach Test
Bibliographic record
Abstract
INTRODUCTION: Videogaming platforms such as the Microsoft (Redmond, WA) Kinect(®) are increasingly being used in rehabilitation to improve balance performance and mobility. These gaming platforms do not have built-in clinical measures that offer clinically meaningful data. We have now developed software that will enable the Kinect sensor to assess a patient's balance using an interactive functional reach test (I-FRT). The aim of the study was to test the concurrent validity of the I-FRT and to establish the feasibility of implementing the I-FRT in a clinical setting. SUBJECTS AND METHODS: The concurrent validity of the I-FRT was tested among 20 healthy adults (mean age, 25.8±3.4 years; 14 women). The Functional Reach Test (FRT) was measured simultaneously by both the Kinect sensor using the I-FRT software and the Optotrak Certus(®) 3D motion-capture system (Northern Digital Inc., Waterloo, ON, Canada). The feasibility of implementing the I-FRT in a clinical setting was assessed by performing the I-FRT in 10 participants with mild balance impairments recruited from the outpatient physical therapy clinic (mean age, 55.8±13.5 years; four women) and obtaining their feedback using a NASA Task Load Index (NASA-TLX) questionnaire. RESULTS: There was moderate to good agreement between FRT measures made by the two measurement systems. The greatest agreement between the two measurement system was found with the Kinect sensor placed at a distance of 2.5 m [intraclass correlation coefficient (2,k)=0.786; P<0.001] from the participant. Participants with mild balance impairments whose balance was assessed using the I-FRT software scored their experience favorably by assigning lower scores for the Frustration, Mental Demand, and Temporal Demand subscales on the NASA/TLX questionnaire. CONCLUSIONS: FRT measures made using the Kinect sensor I-FRT software provides a valid clinical measure that can be used with the gaming platforms.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".