Automatically characterizing driving activities onboard smart wheelchairs from accelerometer data
Bibliographic record
Abstract
Wheelchairs play an important role for people living with locomotor impairments. However, power wheelchair users frequently report both minor and major accidents. The goal of this paper is to advocate for the use of robotic technology, in particular sensor-based detection and automatic classification of activities, to track and characterize activities onboard smart wheelchairs. Experiments were conducted in a clinical setting, in which experienced wheelchair users were asked to conduct a set of typical wheelchair activities. This paper presents an end-to-end pipeline for accurately classifying these activities from accelerometer data using signal processing and machine learning methods. Our classifier achieved an overall accuracy of around 50% in a more than 25 classes classification problem, compared to less than 4% with a random classifier. We also explored the possibility of discovering hidden patterns of activities using unsupervised topic modeling methods. We demonstrated the power of the inferred patterns with two use cases, namely story telling and hazard discovery. Altogether, this work provides new tools for characterizing the usage of smart wheelchairs with typical users.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".