Development of Functional Electrical Stimulation Rowing: The Rowstim Series
Bibliographic record
Abstract
Potentially, functional electrical stimulation (FES)-assisted exercise may have an important therapeutic role in reducing comorbidities associated with spinal cord injury (SCI). Here, we present an overview of these secondary life-threatening conditions, discuss the rationale behind the development of a hybrid exercise called FES rowing, and describe our experience in developing FES rowing technology. FES rowing and sculling are unique forms of adaptive rowing for those with SCI. The paralyzed leg musculature is activated by multiple channels of electrical pulses delivered via self-adhesive electrodes attached to the skin. The stimulated muscle contractions are synchronized with voluntary rowing movements of the upper limbs. A range of steady-state FES rowing exercise intensities have been demonstrated from 15.2 ± 1.8 mL/kg/min in tetraplegia to 22.9 ±7.1 mL/kg/min in paraplegia. We expect that such high levels may help some to achieve significant reductions in the risks to their health, particularly where a dose-response relationship exists as is the case for cardiovascular disease and Type II diabetes. Furthermore, preliminary results suggest that cyclical forces more than 1.5 times body weight are imposed on the leg long bones which may help to reduce the risk of fragility fractures. We have demonstrated the feasibility of FES rowing on land and water using adapted rowing technology that includes; a fixed stretcher indoor ergometer (adapted Concept 2, Model E), a floating stretcher indoor ergometer (adapted Concept 2 Dynamic), a turbine powered water rowing tank, a custom hydraulic sculling simulator and a single scull (adapted Alden 16). This has involved volunteers with paraplegia and tetraplegia with SCI ranging from C4 to T12 AIS A using at least 4-channels of surface electrical stimulation. FES rowers, with SCI, have competed alongside non-SCI rowers over the Olympic distance of 2000 m at the British Indoor Rowing Championships in 2004, 2005, and 2006 and the World Indoor Rowing Championships in 2006 (CRASH-B's) in Boston, MA, USA. The best 2000 m FES rowing performance to date has been achieved by a 23-year-old male, Tom Aggar T12 AIS A, in 10 min 28 s. Moreover, two of our FES rowers with complete paraplegia have gone on to successfully compete in the Adaptive Rowing arms-only category (AM1x) at the World Rowing Championships and Paralympic Games.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".