Loose Garments Effects on Wearable Sensors in Human Activity Tracking Applications
Bibliographic record
Abstract
With the advent of miniature sensor technology, it is now possible to collect data on various aspects of human movement under free-living conditions.This technology has the potential to be used in activity monitoring systems in several areas, including health, military, sports applications, and human monitoring.The majority of research in wearables technology is focused on skin-mounted sensors or embedded in tight clothes.However, most of our daily clothes are loose or contain wide parts.This paper is interested in analyzing measurements of an accelerometer embedded in loose clothes.Experiments are conducted using a wearable node embedded in an oblong piece of cloth to emulate loose clothes.The piece is attached to a participant's arm while performing daily activities.Measurements are collected and presented in both time and frequency domains.Finally, activity measurements are classified using SVM and KNN algorithms.Results indicate that the differences in measurements between loose and tight clothes are noticeable in both domains, but the degradation in classification accuracy is unneglectable.When the sensor was embedded in 10 cm long piece of cloth the classification accuracies are over 80% and 90% for SVM and KNN, respectively, which is approximate, 5% less than the tight clothes accuracies.
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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".