Designing Responsive Music Making Devices: Creating Positive Exercise Experiences for Seniors
Bibliographic record
Abstract
As the population grows older, it is increasingly important to address the challenges associated with aging, such as health decline and loss of independence.Exercise can help seniors remain physically fit, but seniors often are unable to exercise regularly because of barriers and lack of motivation.Current research suggests that music making may provide health benefits and motivate individuals to exercise.This interdisciplinary study combines the fields of music making and design in relation to aging in order to address the question of how design can be used to create devices for seniors to make music, creating a more positive exercise experience.The topic was explored using qualitative design research methods.Music making devices were used as technology probes in a seniors' fitness class, an expert interview was conducted with a fitness instructor, and a co-design workshop was held with seniors.The findings suggest that music making can influence the participants' behaviour in a fitness class and could be used to motivate seniors to exercise.Based on the research, several design recommendations were made concerning the sensory aspects of the object, such as the audio and visual feedback and the tactile experience, and the usability and features of the product.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".