Automated Sperm Immobilization With Compensation for Sperm Intrinsic and Fluid Flow-Induced Movements
Bibliographic record
Abstract
In automated sperm immobilization, motorized stages are conventional means for transporting sperm, while they are not typical devices in clinical setups. This study designs a new robotic sperm immobilization method by leveraging a micromanipulator to position a tooltip above sperm for immobilizing it without a motorized stage. The challenge arises when tooltip motion agitates the medium, leading to the passive displacement of sperm, and the sperm's intrinsic movement also complicates tooltip positioning. To address these concerns, first, a predictor is introduced to predict the sperm's intrinsic movement. Second, a model is established to describe the fluid flow and characterize the passive displacement of sperm as a function of tooltip velocity and medium parameters. Finally, an adaptive control algorithm that accounts for uncalibrated medium parameters is designed to compensate for the sperm's intrinsic movement and tooltip motion-induced sperm displacement. The stability of the control algorithm is analyzed, and the boundedness of the positioning error is proven. Experiments verified the effectiveness of the proposed method in both predicting and feedforwarding sperm's intrinsic movement and in compensating for tooltip motion-induced sperm displacement. The performance of the proposed method is comparable to existing robotic sperm immobilization methods that rely on motorized stages, while being easier to implement in clinics. With the proposed method, the system achieved a success rate of 90.9% in sperm immobilization, representing an improvement of 28.2% over the method not factoring in the sperm's intrinsic movement and 16.3% over the method not accounting for the tooltip motion-induced sperm displacement, respectively.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".