Design and clinical feasibility of an immersive robotic intervention for phantom limb pain
Bibliographic record
Abstract
Phantom Limb Pain is a common after effect for many traumatic amputees, which in most cases results in chronic life-long pain. Despite the implications of this type of pain, the cause and treatment is not fully understood and ineffective. The use of robotics for the rehabilitation of Phantom Limb Pain capitalises on the ability to map physical movements of the affected limb to a virtual limb. Thus potentially enabling those who cannot be fitted with a prosthetic limb, due to either tissue healing or stump neuromas, to take part in rehabilitation. This in combination with force feedback, immersive virtual reality and muscle control of the affected limb in order to control a virtual avatar provides a platform to study the system’s feasibility in treating Phantom Limb Pain and other upper limb conditions with similar pathology. To address this, an immersive robotic system combining virtual reality with robotic movement was developed, facilitating realistic visual and physical interactions enhanced with force feedback to replicate real world physics. A feasibility clinical study was undertaken with 12 upper limb amputees clinically diagnosed with Phantom Limb Pain to evaluate the feasibility of the system as an intervention for Phantom Limb Pain. Participants were assigned to one of two groups. One group experienced immersive virtual reality and the other in addition, the physical properties of the environment. The only difference between the groups was the addition of force feedback, implemented to examine its effects on perceived levels of pain. Participants received nine hours of robotic intervention in total, conducted over a three week period (three hours per week) followed by two follow up questionnaire sessions to determine if any potential pain levels had changed. The primary outcome measure used to assess perceived levels was the clinically validated short McGill Pain questionnaire. Due to the limitations of the study design it was not possible to account for all potential placebo effects which might have influenced the results. However, it is suggested that the paradigm presented in this thesis (with or without force feedback) has the potential to lower perceived levels of pain. In addition the intervention was feasible in a hospital setting, participants were recruited and went through a lengthy protocol with a fully working system which was able to collect data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".