Use this when revising the main paper. PerCom papers are IEEE Xplore proceedings read by ubicomp reviewers, so they need a pervasive-computing contribution stated in the first page and evidence a reviewer trusts on people they did not train on. The failure this skill prevents is a technically fine paper that reads like an ML result with a wearable title glued on, or a systems demo with no human at its center.
| Section | Job it must do | Common failure |
|---|---|---|
| Intro | Problem, inadequacy, contribution, evidence preview, ubicomp payoff — first page | Leads with a technology trend, not a real-use problem |
| Background/Motivation | Why a user or deployment needs this now | Motivation by assertion, no grounding in practice |
| System / Study design | The technique or the study + sensing protocol, reproducibly | Method or sensor setup described too thinly to re-run |
| Evaluation | Each claim answered with cross-subject, proportional evidence | Within-subject or pooled-accuracy metrics that flatter the result |
| Limitations | The limits that bite, each bounded | Generic list untethered from this study's subjects/sensors |
| Related work | Delta-first positioning against the ubicomp literature | Catalog of citations with no contrast |
| Draft pattern | PerCom-safe rewrite |
|---|---|
| "Our system achieves 97% accuracy." | "leave-one-subject-out F1 of 0.xx (95% CI ...) on <N> participants" |
| "We evaluate on a large dataset." | "We evaluate on <N> participants over |
| "Results show our approach works well." | "cross-subject F1 improves by X over |
| "State-of-the-art performance." | Claim scoped to the subjects, sensors, and setting actually tested |
| "The model recognizes activities." | "the recognizer reaches F1 0.xx on held-out subjects for |
[Split] state within-subject vs. leave-one-subject-out (or session-out); PerCom default is cross-subject
[Balance] report class balance; on imbalanced activities use F1 (macro + per-class), not raw accuracy
[Event vs frame] say whether metrics are frame-level or event-level; they can differ sharply
[Realism] lab vs. free-living; scripted vs. spontaneous behavior -- name which you tested
-> for each: state the choice next to the number so a reviewer is not left guessing
A draft with three activity classes, nine figures, and a sprawling background: keep the cross-subject headline result, the two figures that carry it, a per-class F1 table, and a limitations subsection tied to subject diversity and ground truth; move per-subject breakdowns and extra ablations to the dataset with explicit forward references; cut background to what the argument needs. The test of a good cut: a reviewer should be able to answer "does it work on a new person, and what threatens that?" from the body alone.
[Writing diagnosis] clear / under-motivated / over-claimed / within-subject-only / over-scoped
[First-page fix] <new framing leading with the pervasive-computing contribution>
[Claim audit] <claim -> split (LOSO?) -> metric (F1?) -> where answered -> proportional? yes/no>
[Limitations fix] <limit that bites -> bounding to add, placed by the result>
[Anonymity edits] <system names / self-citations / dataset links to rewrite>