The Effect of Sensor Placement in a Cooking Activity Recognition System
Bibliographic record
Abstract
Although device-free human activity recognition (HAR) has been among the commonly investigated HAR methods in the past few years, a neglected topic in this field is the sensor placement in real environments. To address this, we investigate the effect of sensor placement on the recognition performance of fine-grained human activities in a real cooking environment. We used a WiFi-based system that recognizes cooking activities from the Channel State Information (CSI) and the Received Signal Strength Indicator (RSSI) reflected by the user. We experimented with three sensor placement strategies, each having the user and the performing area in line-of-sight (LOS) and non-line-of-sight (NLOS), leading to 6 different placements. The results show a statistically significant difference between the distribution of CSI and RSSI features for different fine-grained activities when the user and the performing area are in the line of sight.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".