| --- |
| license: other |
| tags: |
| - eating-detection |
| - wearable |
| - ppg |
| - accelerometer |
| - time-series |
| --- |
| |
| # EatingDetection checkpoint |
|
|
| Ten leave-one-participant-out checkpoints for binary eating detection from the |
| smart earring — green PPG + tri-axial accelerometer, no gyroscope, no |
| microphone. |
|
|
| Trained on the 8-second windowed split of a 10-participant earring study. The |
| study data is not public yet, so these weights are released ahead of it: the |
| model, the evaluation protocol and the per-fold scores are all here, but the |
| numbers below cannot be re-derived from raw data until the dataset is released. |
|
|
| Code: https://github.com/FengYe476/Eating_Detection/tree/Feng/eating_detection |
|
|
| ## Result |
|
|
| | | macro-F1 | |
| |---|---:| |
| | **Pooled over all held-out decisions** | **0.9535** | |
| | Mean over the 10 participants | 0.9486 ± 0.0253 | |
|
|
| Same ten models, two reductions: pooling concatenates every held-out decision |
| and scores once (each window counts equally); the per-participant mean scores |
| each fold then averages (each person counts equally). |
|
|
| | P2 | P3 | P4 | P5 | P7 | P8 | P9 | P10 | P11 | P12 | |
| |---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| |
| | .944 | .947 | .947 | .947 | .960 | .996 | .913 | .901 | .964 | .966 | |
|
|
| `checkpoint_scores.json` holds the per-fold table, including the score stored |
| inside each checkpoint at training time — re-scoring reproduces all ten to four |
| decimals. |
|
|
| ## Files |
|
|
| `fold_P<user>.pt` — one per held-out participant. **`fold_P<u>.pt` never saw |
| participant `<u>`** during training, standardisation or checkpoint selection; |
| that is what makes its score subject-independent. For a participant outside this |
| cohort, any fold is equally valid. |
| |
| Each file is a dict: `state_dict`, `mu`, `sd` (per-channel standardisation fitted |
| on that fold's training subjects), `channels`, `window_samples`, `in_channels`, |
| `test_user`, `val_f1`, `test_macro_f1`. |
| |
| ## Model |
| |
| `CoincidenceTCN`, **9,433 parameters**. Input `(N, 4, 400)` — 8 s at 50 Hz, |
| channel order **`[ppg_green, acc_x, acc_y, acc_z]`**. Output: one logit per |
| window; eating if `logit > 0`. |
| |
| ```python |
| import torch |
| from eating_detection.models.coincidence_tcn import CoincidenceTCN |
| |
| d = torch.load("fold_P2.pt", map_location="cpu", weights_only=False) |
| m = CoincidenceTCN(d["in_channels"]); m.load_state_dict(d["state_dict"]); m.eval() |
| |
| X = ... # (N, 4, 400) float32, raw window values |
| X = (X - d["mu"]) / d["sd"] # the fold's own statistics — do not refit |
| eating = m(torch.from_numpy(X.astype("float32"))) > 0 |
| ``` |
| |
| ## Evaluation protocol |
| |
| 10-fold leave-one-participant-out over P2, P3, P4, P5, P7, P8, P9, P10, P11, |
| P12; both earrings merged as independent windows; 8 s windows on a 1 s stride. |
| Negatives follow "Strategy-2": every eating window, every idle window within two |
| minutes of eating, and far background idle subsampled to 1:1 with eating — so |
| majority-class accuracy would be 0.62, not 0.9. |
| |
| Reported as macro-F1 with the decision fixed at `logit > 0`; no per-participant |
| threshold selection and no per-participant calibration. |
| |
| ## Reading these numbers honestly |
| |
| - Negatives are not uniformly hard: false-positive rate is 0.138 on pauses |
| *inside* a food block against 0.039 on rest *between* blocks. Hard-negative |
| balanced accuracy is **0.90** — quote that for a deployment claim. |
| - P9 and P10 fail in opposite directions (under-detection and over-alarming) and |
| do so reproducibly; the decision rule has no threshold selection. |
| - Run-to-run spread for this configuration is about 0.8 pp, and CUDA vs MPS |
| alone moves it 0.3–0.4 pp. A single run does not separate two configurations |
| that differ by ~1 pp. |
|
|
