A Comparison Between Conventional and User-Intention-Based Adaptive Pushrim-Activated Power-Assisted Wheelchairs
Bibliographic record
Abstract
Pushrim-activated power-assisted wheel (PAPAW) users ideally require different levels of assistance depending on activity and preference. Therefore, it is important to design and develop adaptive PAPAW controllers to account for these differences. The main objective of this work was to integrate a user intention estimation framework into a PAPAW and develop personalized adaptive controllers. We performed experiments to gather kinetics of wheelchair propulsion for a variety of daily life wheelchair activities. The propulsion characteristics (i.e., pushrim forces) were used to train intention estimation models and characterize implicit user intentions when performing daily life wheelchair maneuvers. These intentions included moving straight forward, performing a right/left turn, and braking. The intention estimation framework, based on random forest classification algorithms and kinetic features, was implemented and tested in our laboratory-developed PAPAW. This computationally efficient framework was successfully implemented and tested for each participant in real-time. Our results revealed that the real-time user intention predictions were similar to the offline models. The power-assist ratio of each wheel was adjusted based on which user intention was identified. Data collected from four participants provided evidence regarding the effectiveness of using adaptive intention-based controllers. For instance, the propulsion effort was significantly reduced when using an adaptive PAPAW controller. Subjective views of participants regarding the workload of wheelchair propulsion (e.g., physical/cognitive effort) were also gathered. Our findings suggest that rankings of different controllers varied among different participants and across different wheelchair maneuvers, indicating the need for customized adaptive controllers to fit different users' activities and preferences.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".