Next Steps in Epidermal Computing: Opportunities and Challenges for Soft On-Skin Devices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it