Multimodal Human Activity Recognition for Smart Healthcare Applications
Bibliographic record
Abstract
Human Activity Recognition (HAR) has emerged as a potential research topic for smart healthcare owing to the fast growth of wearable and smart devices in recent years. The significant applications of HAR in ambient assisted living environments include monitoring the daily activities of elderly and cognitively impaired individuals to assist them by observing their health status. In this research, we present a deep learning-based fusion approach for multimodal HAR that fuses the different modalities of data to obtain robust outcomes. Here, Convolutional Neural Networks (CNNs) retrieve the high-level attributes from the image data, and the Convolutional Long Short Term Memory (ConvLSTM) is utilized to capture significant patterns from the multi-sensory data. Finally, the extracted features from the modalities are fused through self-attention mechanisms that enhance the relevant activity data and inhibit the superfluous and possibly confusing information by measuring their compatibility. Lastly, extensive tests have been performed to measure the efficiency and robustness of the developed fusion approach using the UP-Fall detection dataset. It is evident from the experimental findings that the proposed fusion technique outperforms the existing state-of-the-art and achieves relatively better performance.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".