On the Effectiveness of Training Objectives of Pretrained Models for Inertial Sensor Data*
Bibliographic record
Abstract
A recent significant advancement in artificial intelligence, particularly in natural language processing and computer vision, is self-supervised-based pretraining, which has been serving as a foundational building block for downstream tasks and applications. In addition, such a framework has also shown a great potential to set a foundation for applications on a wider range of data types. In this paper, we conduct an empirical study aiming to provide more evidence for further understanding a basic question in applying pretrained models to sensor data. Specifically, we aim to further understand how the most widely used training objectives—masked language modeling and contrastive objectives—behave in the inertial sensor domain. We perform our study on human activity data inspired by their wide range of applications. We use encoder architectures and leverage linear probing to test the quality of the learned encoder on different tasks. Our experiments show that masked language modeling is consistently better than contrastive learning. We provide detailed analysis and visualization to demonstrate the effectiveness of masked language modeling on three representative tasks: human activity recognition, inertial odometry, and human inertial posing. While we have focused on these specific tasks, we hope the study will help inspire more research to investigate and explore the effectiveness of pretrained architectures in the sensor domain.
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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".