Investigating the Effect of Orientation Variability in Deep Learning-based Human Activity Recognition
Bibliographic record
Abstract
Deep Learning (DL) has enabled considerable increases in the accuracy of classification tasks in several domains, including Human Activity Recognition (HAR). It is well-known that when data distribution changes between the training and test datasets, the accuracy can drop, sometimes significantly. However, some variability sources in HAR, such as sensor orientation, are only sometimes considered when evaluating these models. Therefore, we must understand how much such changes could impact current DL architectures. In this paper, under an orientation variability scenario, we evaluate three common DL architectures, DeepConvLSTM, TinyHAR, and Attend-and-Discriminate, to quantify the performance drop attributed to this shift. Our results show that all architectures show performance drops on average, as expected, but participants are affected differently from them, so they would fall short for some in classification accuracy in real-life settings where orientation can change across the wearing sessions of one participant or across participants. The performance change is related to the difference in distribution distance.
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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 | 0.002 | 0.009 |
| 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 | 0.001 | 0.001 |
| 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".