Development of A Learning-Based Terrain Classification Framework for Pushrim-Activated Power-Assisted Wheelchairs
Bibliographic record
Abstract
Pushrim-activated power-assisted wheels (PAPAWs) are assistive technologies that provide on-demand torque assistance to wheelchair users. Although the available power can reduce the physical load of wheelchair propulsion, it may also cause maneuverability and controllability issues. Commercially-available PAPAW controllers are insensitive to environmental changes, leading to inefficient and/or unsafe wheelchair movements. In this regard, adaptive velocity/torque control strategies could be employed to improve safety and stability. To investigate this objective, we propose a context-aware sensory framework to recognize terrain conditions. In this paper, we present a learning-based terrain classification framework for PAPAWs. Study participants performed various maneuvers consisting of common daily-life wheelchair propulsion routines on different indoor and outdoor terrains. Relevant features from wheelchair frame-mounted gyroscope and accelerometer measurements were extracted and used to train and test the proposed classifiers. Our findings revealed that a one-stage multi-label classification framework has a higher accuracy performance compared to a two-stage classification pipeline with an indoor-outdoor classification in the first stage. We also found that, on average, outdoor terrains can be classified with higher accuracy (90%) compared to indoor terrains (65%). This framework can be used for real-time terrain classification applications and provide the required information for an adaptive velocity/torque controller design.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".