Adaptive cueing strategy for gait modification: A case study using auditory cues
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
People with Parkinson's (PwP) experience gait impairments that can be improved through cue training, where visual, auditory, or haptic cues are provided to guide the walker's cadence or step length. There are two types of cueing strategies: open and closed-loop. Closed-loop cueing may be more effective in addressing habituation and cue dependency, but has to date been rarely validated with PwP. In this study, we adapt a human-in-the-loop framework to conduct preliminary analysis with four PwP. The closed-loop framework learns an individualized model of the walker's responsiveness to cues and generates an optimized cue based on the model. In this feasibility study, we determine whether participants in early stages of Parkinson's can respond to the novel cueing framework, and compare the performance of the framework to two alternative cueing strategies (fixed/proportional approaches) in changing the participant's cadence to two target cadences (speed up/slow down). The preliminary results show that the selection of the target cadence has an impact on the participant's gait performance. With the appropriate target, the framework and the fixed approaches perform similarly in slowing the participants' cadence. However, the proposed framework demonstrates better efficiency, explainability, and robustness across participants. Participants also have the highest retention rate in the absence of cues with the proposed framework. Finally, there is no clear benefit of using the proportional approach.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it