Biomusic: A Novel Technology for Revealing the Personhood of People with Profound Multiple Disabilities
Bibliographic record
Abstract
It is often difficult for family members and caregivers to interact with persons with profound multiple disabilities (PMD) because of the severely compromised communicative repertoire of this population. The resulting communication challenges may limit the ability of others to perceive personhood in individuals with PMD. This preliminary study investigated the effects of music generated in real time from physiological signals (biomusic) on caregiver perceptions of their interactions with persons with PMD. Caregivers (n = 10; parents and clinical staff) engaged in four, 10-min interactions with a person with PMD (n = 3; diagnoses = traumatic brain injury, pervasive developmental disorder, hypoxic brain injury), whose biomusic was projected throughout. Caregivers participated in two open-ended, semi-structured interviews to explore the effect of biomusic on these interactions. Most caregiver responses to biomusic were very positive, and many reported that biomusic caused an improvement in their interaction with and perceptions of the person with PMD. By providing audible evidence of the changing physiological state of persons with PMD, biomusic may enhance the perceived personhood of these individuals and enrich interactions with their family members and caregivers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".