Bibliographic record
Abstract
Human activity recognition (HAR) is crucial for ubiquitous computing systems. While HAR systems are able to recognize a predefined set of activities established during the development process, they often fail to handle users' unique ways of completing these activities and changes in their behavior over time, as well as different activities. Knowledge-based HAR models have been proposed to help individuals create new activity definitions based on common-sense rules, but little research has been done to understand how users approach this task. To investigate this process, we developed and studied how people interact with an explainable knowledge-based HAR development tool called exHAR. Our tool empowers users to define their activities as a set of factual propositions. Users can debug these definitions by soliciting explanations for model predictions (why and why-not) and candidate corrections for faulty predictions (what-if and how-to). After conducting a study to evaluate the effectiveness of exHAR in helping users design accurate HAR systems, we conducted a think-aloud study to better understand people's approach to debugging and personalizing HAR systems and the challenges they may encounter. Our findings revealed why some participants had inaccurate mental models of knowledge-based HAR systems and inefficient approaches to the debugging process.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.023 |
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".