Designing with Biosignals: Challenges, Opportunities, and Future Directions for Integrating Physiological Signals in Human-Computer Interaction
Bibliographic record
Abstract
Biosensing technologies are a rapidly increasing presence in our daily lives. These sensor-based technologies measure physiological processes including heart rate, breathing, skin conductance, brain activity and more. Researchers are exploring biosensing from perspectives including: engineering, human-computer interaction, medicine, mental health, consumer products, and interactive art. These technologies can enhance our interactions allowing connection to our bodies and others around us across diverse application areas. However, designing with biosignals in Human-Computer Interaction presents new challenges pertaining to User Experience, Input/Output, interpretation of signals, representation, and ethics. There is an urgent need to build a scholarly community that includes the diverse perspectives of researchers, designers, industry practitioners and policymakers. The goal of this workshop is to leverage the knowledge of this community aiming to map out the research landscape of emerging challenges and opportunities, and to build a research agenda for future directions.
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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 | 0.030 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".