Adaptive-Prompt-Driven Few-Shot Class-Incremental Learning for Human Activity Recognition With Radar Modality
Bibliographic record
Abstract
Human activity recognition (HAR) has attracted growing interest due to its wide ranging applications in healthcare, security, and smart surveillance. In particular, radar-based HAR offers a robust, non-contact alternative to conventional sensor based methods. Despite its promise, existing approaches often rely on substantial labeled data and exhibit limited adaptability in small-shot incremental learning scenarios. To address these challenges, we propose a novel few shot class incremental learning (FSCIL) framework for radar-based HAR. Our framework exploits vision transformers (ViTs) augmented with adaptive prompt mechanisms, hybrid attention blocks (HAB), and dynamic feature refinement strategies, which can substantially improve incremental learning performance. We validate the proposed approach on a fused radar dataset containing four sets of diverse radar datasets collected under varying environmental conditions. Our validation shows a harmonic accuracy (HAcc) up to 69.54 % and a Top-1 average accuracy (Top-1 Avg.) up to 91.20 %, outperforming state-of-the-art methods, with a session performance degradation as low as 10.80%. These results underscore the robustness of our approach in recognizing novel activities while preserving previously acquired knowledge.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".