Technologies to Support Assessment of Movement During Video Consultations: Exploratory Study
Bibliographic record
Abstract
BACKGROUND: Understanding and assessing patients' body movements is essential for physical rehabilitation but is challenging in video consultations, as clinicians are frequently unable to see the whole patient or observe the patient as they perform specific movements. OBJECTIVE: The objective of this exploratory study was to assess the use of readily available technologies that would enable remote assessment of patient movement as part of a video consultation. METHODS: We reviewed the literature and available technologies and chose four technologies (Kubi and Pivo desktop robots, Facebook Portal TV, wide-angle webcam), in addition to help from a friend or a simple mobile phone holder, to assist video consultations. We used 5 standard assessments (sit-to-stand, timed "Up & Go," Berg Balance Test, ankle range of motion, shoulder range of motion) as the "challenge" for the technology. We developed an evaluation framework of 6 items: efficacy, cost, delivery, patient setup, clinician training and guidance, and safety. The coauthors, including 10 physiotherapists, then took the roles of clinician and patient to explore 7 combinations of 5 technologies. Subsequently, we applied our findings to hypothetical patients based on the researchers' family members and clinical experience. RESULTS: Kubi, which allowed the clinician to remotely control the patient's device, was useful for repositioning the tablet camera to gain a better view of the patient's body parts but not for tracking movement. Facebook Portal TV was useful, but only for upper body movement, as it functions based on face tracking. Both Pivo, with automated full body tracking using a mobile phone, and the wide-angle webcam for a laptop or desktop computer show promise. Simple solutions such as having a friend operate a mobile phone and use of a mobile phone holder also have potential. The setup of these technologies will require better instructions than are currently available from suppliers, and successful use will depend on the technology readiness of patients and, to some degree, of clinicians. CONCLUSIONS: Technologies that may enable clinicians to assess movement remotely as part of video consultations depend on the interplay of technology readiness, the patient's clinical conditions, and social support. The most promising off-the-shelf approaches seem to be use of wide-angle webcams, Pivo, help from a friend, or a simple mobile phone holder. Collaborative work between patients and clinicians is needed to develop and trial technological solutions to support video consultations assessing movement.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".