Employing Deep Reinforcement Learning to Maximize Lower Limb Blood Flow Using Intermittent Pneumatic Compression
Bibliographic record
Abstract
Intermittent pneumatic compression (IPC) systems apply external pressure to the lower limbs and enhance peripheral blood flow. We previously introduced a cardiac-gated compression system that enhanced arterial blood velocity (BV) in the lower limb compared to fixed compression timing (CT) for seated and standing subjects. However, these pilot studies found that the CT that maximized BV was not constant across individuals and could change over time. Current CT modelling methods for IPC are limited to predictions for a single day and one heartbeat ahead. However, IPC therapy for may span weeks or longer, the BV response to compression can vary with physiological state, and the best CT for eliciting the desired physiological outcome may change, even for the same individual. We propose that a deep reinforcement learning (DRL) algorithm can learn and adaptively modify CT to achieve a selected outcome using IPC. Herein, we target maximizing lower limb arterial BV as the desired outcome and build participant-specific simulated lower limb environments for 6 participants. We show that DRL can adaptively learn the CT for IPC that maximized arterial BV. Compared to previous work, the DRL agent achieves 98% ± 2 of the resultant blood flow and is faster at maximizing BV; the DRL agent can learn an "optimal" policy in 15 minutes ± 2 on average and can adapt on the fly. Given a desired objective, we posit that the proposed DRL agent can be implemented in IPC systems to rapidly learn the (potentially time-varying) "optimal" CT with a human-in-the-loop.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".