Gesture recognition in smart home using passive RFID technology
Bibliographic record
Abstract
Gesture recognition is a well-establish topic of research that is widely adopted for a broad range of applications. For instance, it can be exploited for the command of a smart environment without any remote control unit or even for the recognition of human activities from a set of video cameras deployed in strategic position. Many researchers working on assistive smart home, such as our team, believe that the intrusiveness of that technology will prevent the future adoption and commercialization of smart homes. In this paper, we propose a novel gesture recognition algorithm that is solely based on passive RFID technology. This technology enables the localization of small tags that can be embedded in everyday life objects (a cup or a book, for instance) while remaining non intrusive. However, until now, this technology has been largely ignored by researchers on gesture recognition, mostly because it is easily disturbed by noise (metal, human, etc.) and offer limited precision. Despite these issues, the localization algorithms have improved over the years, and our recent efforts resulted in a real-time tracking algorithm with a precision approaching 14cm. With this, we developed a gesture recognition algorithm able to perform segmentation of gestures and prediction on a spatio-temporal data series. Our new model, exploiting works on qualitative spatial reasoning, achieves recognition of 91%. Our goal is to ultimately use that knowledge for both human activity recognition and errors detection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".