A two-phase hybrid clustering framework exploring transitional activities in HAR
Bibliographic record
Abstract
Human Activity Recognition (HAR) using data streams from wearable sensors is challenging due to high data dimensionality, noise, and the lack of labeled data in unsupervised settings. Our prior work proved that traditional clustering models, which achieve state-of-the-art performance on simulated datasets, perform poorly on time-series numeric sensor data. This paper explores different autoencoder (AE) architectures to extract latent features with reduced dimensionality from streaming HAR datasets, which is then clustered using a clustering model to identify different activity patterns. Since the vanilla AE has shortcomings in learning distinguishing data patterns from spatio temporal time-series sensor data, we leverage the vanilla AE with convolutional, long-short term memory (LSTM), and a combination of convolutional and LSTM layers in multiple design phases. We apply supervised learning to train a superior spatio-temporal feature extraction AE model. Using the data features extracted by the trained AE, we train a clustering model with unsupervised learning approach. Our end-to-end integrated hybrid convolutional AE+LSTM feature extractor and K-Means clustering model achieves state-of-the-art clustering accuracy of up to 0.99 in terms of Normalized Mutual Information (NMI) and Adjusted Rand Index (ARI) scores for MobiAct and UCI HAR datasets, improving clustering performance by over 50% compared to previous methods. Further improvements are achieved through rigorous experimentation and advanced data preprocessing methods. We also present a visualization of the clusters, which explains the transitional activity patterns in the overlapping parts of the clusters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".