Squeaky/Pain: Articulating the Felt Experience of Pain for Somaesthetic Interactions
Bibliographic record
Abstract
This pictorial illustrates the methodological tools for articulating the felt experience of chronic pain used for designing somaesthetic interactions. To do this, it presents the design process of a case study named Squeaky/Pain, a soma extension aiming to augment somaesthetic awareness of the pain involved in the appreciation of both pleasant and disturbing feelings and sensations. The soma extension is an interactive wearable that facilitates a sound-motion interaction to mimic the wearer’s pain experience, from agony to relief. The case study focuses on a less explored aspect of somaesthetic interactions which is the mediation of disturbing experiences for sensory awareness. Through the soma extension that mediates disturbing experiences, the study aims to improve people’s somatic knowledge and their lives as a result. The design process of Squeaky/Pain requires detailed accounts of lived bodily experiences to create somaesthetic interactions. To access a detailed articulation of felt experiences, various tools are employed to articulate the first- and second-person pain experience for design use. These are different types of body maps, video analysis, material and form explorations, journals, in-depth interviews and self-interviews. The ideation and the testing phases have proven that such tools complement one another to access the versatile aspects of felt experiences. In this pictorial, we demonstrate ways in which visual, verbal and written tools can be applied to reveal implicit bodily experiences to inform somaesthetic interaction design.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".