A Deep Learning Human Activity Recognition Framework for Socially Assistive Robots to Support Reablement of Older Adults
Bibliographic record
Abstract
Many older adults prefer to stay in their own homes and age-in-place. However, physical and cognitive limitations in independently completing activities of daily living (ADLs) requires older adults to receive assistive support, often necessitating transitioning to care centers. In this paper, we present the development of a novel deep learning human activity recognition and classification architecture capable of autonomously identifying ADLs in home environments to enable long-term deployment of socially assistive robots to aid older adults. Our deep learning architecture is the first to use multimodal inputs to create an embedding vector approach for classifying and monitoring multiple ADLs. It uses spatial mid-fusion to combine geometric, motion and semantic features of users, environments, and objects to classify and track ADLs. We leverage transfer learning to extract generic features using the early layers of deep networks trained on large datasets to apply our architecture to various ADLs. The embedding vector enables identification of unseen ADLs and determines intra-class variance for monitoring user ADL performance. Our proposed unique architecture can be used by socially assistive robots to promote reablement in the home via autonomously supporting the assistance of varying ADLs. Extensive experiments show improved classification accuracy compared to unimodal/dual-modal models and the ADL embedding space also incorporates the ability to distinctly identify and track seen and unseen ADLs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".