PhantomAR: gamified mixed reality system for alleviating phantom limb pain in upper limb amputees—design, implementation, and clinical usability evaluation
Bibliographic record
Abstract
BACKGROUND: Phantom limb pain (PLP) is a restrictive condition in which patients perceive pain in a limb that is no longer present, greatly reducing their quality of life. Mirror Therapy, wherein patients observe a mirror reflection of their intact limb, has demonstrated efficacy in alleviating PLP. However, its unilateral and seated nature presents limitations. To address these constraints while still reducing PLP, and evaluating the impact of different virtual limb representations (anthropomorphic vs. non-anthropomorphic) on the user's sense of ownership, agency, and embodiment, PhantomAR was developed. Leveraging wearable first-person augmented reality (AR) technology, PhantomAR extends traditional Mirror Therapy by enabling users to move freely and engage in bimanual tasks. METHODS: The assistive mixed reality game application PhantomAR was deployed on the Microsoft HoloLens 2 and augmented the user's residual limb by superimposing a virtual arm or tentacle that was controlled via residual muscles on their stump using an EMG electrode array. This setup allowed patients to engage in a first-person perspective and manipulate virtual objects with both the healthy and augmented limbs, free from the confines of a seated position. The study enrolled 10 able-bodied individuals and 8 individuals with unilateral, transradial amputation. All amputees experienced PLP. The usability of the PhantomAR application was evaluated using the System Usability Scale (SUS) and a user-centric survey. Additionally, the Game Experience was assessed on a 5-point Likert questionnaire (GEQ). Participants rated their phantom sensations using the Numerical Rating Scale and McGill Pain Questionnaire before, during, and after interaction with PhantomAR. The embodiment and agency of the virtual superimposed arm were evaluated with an altered Prosthesis Embodiment Scale. The study protocol included two sessions of 30 min each, during which participants experienced PhantomAR. RESULTS: Participants (n = 18) rated PhantomAR highly usable (SUS m = 90.8%, SD = 6.88). Feedback on the Game Experience Questionnaire was overwhelmingly positive, showing high immersion (m = 4.46, SD = 0.08) and positive affect (m = 4.97, SD = 0.05). PLP (n = 8) significantly decreased post-intervention (NRS and McGill Pain Questionnaire, p < .001). Skin temperature in the residual limb increased significantly post-intervention (p < .01) but did not correlate with PLP (r = - 0.08, p = 0.83). Tentacle overlay yielded mixed ownership but high agency ratings. CONCLUSION: PhantomAR leverages mixed reality to significantly reduce Phantom Limb Pain, enhance user engagement, and alter perceptions of ownership and agency of their augmented limb through bi-manual, dynamic, full-body interactions. Trial registration DRKS00033208 (Jan. 5th 2024).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".