Leveraging Self-Supervised Learning for Human Activity Recognition with Ambient Sensors
Bibliographic record
Abstract
Human activity recognition (HAR) using ambient sensors has emerged as a promising approach to telemonitoring daily activities and enhancing the elderly quality of life. Deep learning models have demonstrated competitive performance in HAR on real-world datasets. However, acquiring large amounts of annotated sensor data for extracting robust features is costly and time-consuming. To overcome this limitation, we propose a novel model based on the self-supervised learning framework, SimCLR, for daily activity recognition using ambient sensor data. The core component of the model is the encoder module, which consists of two convolutional layers followed by a long short-term memory (LSTM) layer. This architecture allows the model to capture both spatial and temporal dependencies in the sensor data, enabling the extraction of informative features for downstream tasks. Through extensive experiments on three CASAS smart home datasets (Aruba-1, Aruba-2, and Milan), we showcase the superior performance of the model in semi-supervised learning and transfer learning scenarios, surpassing state-of-the-art approaches. The findings highlight the potential of self-supervised learning in extracting valuable information from unlabeled sensor data, reducing costly annotation efforts for real-world HAR applications.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".