Fresnel Zone-Based Voting With Capsule Networks for Human Activity Recognition From Channel State Information
Why this work is in the frame
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Bibliographic record
Abstract
Wireless local-area network (WLAN) sensing offers advantages over other approaches to human activity recognition (HAR) for Internet of Things (IoT) applications, including privacy as well as adaptability to non-line-of-sight scenarios. This is why HAR plays an important role in the upcoming IEEE 802.11bf Wi-Fi standard, which aims to bring the adoption of WLAN sensing to a much larger scale. In this paper, we propose CapsHAR, a model based on capsule networks, which uses channel state information (CSI) from Wi-Fi signals to accurately perform human activity recognition. We evaluate the capability of the model on a variety of datasets, including large and small-scale gestures, as well as compare its performance to a variety of models and approaches. We then extend the CapsHAR model into a distributed architecture in order to eliminate the communication overhead of sending CSI data from multiple access points (AP) to a single server. We propose the use of edge computing to run CapsHAR at each AP separately, then combine the outputs of the models through a Fresnel zone-based voting scheme which makes more efficient use of spatial diversity. Overall, the CapsHAR architecture consistently achieves classification accuracy surpassing that of the state-of-the-art models, demonstrating the viability of capsule networks for reliable HAR in Wi-Fi-based IoT applications.
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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 | 0.001 | 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it