LWiHS: A Lightweight Wi-Fi-Enabled Human Sensing Using Feature Fusion Strategy
Bibliographic record
Abstract
Human sensing based on Wi-Fi Channel State Information (CSI) has attracted attention due to its non-intrusiveness and wide applications. However, due to its susceptibility to external environmental noise and interference, high-complexity deep learning (DL) algorithms are usually adopted to improve the sensing accuracy, which can hardly be deployed in edge devices with limited computational abilities. Building a high-precision, low-complexity human sensing system remains challenging. To improve the accuracy of CSI time series (TS) classification, we proposed a TS to image conversion method based on feature fusion, which is developed to increase the spatial structure information of the original data and expand the feature vector space. To build an efficient and lightweight feature extraction network, we designed a framework LWiHS, which is a lightweight universal feature extraction network used to extract the perceptual information containing both deep and shallow features in the images. To reduce the number of model parameters, we adopted channel pruning based on layer adaptive amplitude pruning (LAMP) scoring. LWiHS is lightweight while maintaining good feature extraction capabilities. Our proposed LWiHS outperforms other advanced algorithms in both complexity and sensing performance on four open-source datasets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".