Perceptual Thresholds for Vibration Transmitted to Road Cyclists
Bibliographic record
Abstract
OBJECTIVES: In this article, we seek to determine how sensitive road cyclists are to vertical vibration transmitted while riding a road bicycle and to propose metrics for the evaluation of dynamic comfort. BACKGROUND: Road cyclists are exposed to random-type excitation due to road roughness. Vibration transmitted affects dynamic comfort. But how sensitive are cyclists to vibration level? What are the best metrics to measure the amount of vibration transmitted to cyclists? Previous studies used sinusoidal excitation with participants on rigid seats and measured acceleration. METHODS: We use a psychophysical estimation of Just Noticeable Differences in Level (JNDL) for vertical vibration transmitted to cyclists on a road simulator. In Experiment 1, we estimate the JNDL for whole-body vibration using vertical excitation on both wheels simultaneously (20 male cyclists). In Experiment 2, we estimate the JNDL at two different points of contact by applying the same signal to only the hands or the buttocks (9 male cyclists). RESULTS: The JNDLs are expressed in terms of acceleration and power transmitted to the cyclist. We compare the JNDLs expressed with these 2 metrics and measured at different points of contact. CONCLUSION: Using these two metrics and at all points of contact, vibration magnitude needs to be reduced by at least 15%, for the change to be detectable by road cyclists. APPLICATION: A road bicycle needs to transmit at least 15% less vibration for male cyclists to detect an improvement in dynamic comfort. Dynamic bicycle comfort can be measured in terms of a new metric: power transmitted to the cyclist.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".