Digital music interventions for stress with bio-sensing: a survey
Bibliographic record
Abstract
Music therapy is used to treat stress and anxiety in patients for a broad range of reasons such as cancer treatment, substance abuse, addressing trauma, and just daily stress in life. However, access to treatment is limited by the need for trained music therapists and the difficulty of quantitatively measuring efficacy in treatment. We present a survey of digital music systems that utilize biosensing for the purpose of reducing stress and anxiety with therapeutic use of music. The survey analyzes biosensing instruments for brain activity, cardiovascular, electrodermal, and respiratory measurements for efficacy in reduction in stress and anxiety. The survey also emphasizes digital music systems where biosensing is utilized to adapt music playback to the subject, forming a biofeedback loop. We also discuss how these digital music systems can use biofeedback coupled with machine learning to provide improved efficacy. Lastly, we posit that such digital music systems can be realized using consumer-grade biosensing wearables coupled with smartphones. Such systems can provide benefit to music therapists as well as to anyone wanting to treat stress from daily living.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".