Next Steps in Epidermal Computing: Opportunities and Challenges for Soft On-Skin Devices
Bibliographic record
Abstract
Skin is a promising interaction medium and has been widely explored for mobile, and expressive interaction. Recent research in HCI has seen the development of Epidermal Computing Devices: ultra-thin and non-invasive devices which reside on the user’s skin, offering intimate integration with the curved surfaces of the body, while having physical and mechanical properties that are akin to skin, expanding the horizon of on-body interaction. However, with rapid technological advancements in multiple disciplines, we see a need to synthesize the main open research questions and opportunities for the HCI community to advance future research in this area. By systematically analyzing Epidermal Devices contributed in the HCI community, physical sciences research and from our experiences in designing and building Epidermal Devices, we identify opportunities and challenges for advancing research across five themes. This multi-disciplinary synthesis enables multiple research communities to facilitate progression towards more coordinated endeavors for advancing Epidermal Computing.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".