Overground vs. treadmill-based robotic gait training to improve seated balance in people with motor-complete spinal cord injury: a case report
Bibliographic record
Abstract
BACKGROUND: Robotic overground gait training devices, such as the Ekso, require users to actively participate in triggering steps through weight-shifting movements. It remains unknown how much the trunk muscles are activated during these movements, and if it is possible to transfer training effects to seated balance control. This study was conducted to compare the activity of postural control muscles of the trunk during overground (Ekso) vs. treadmill-based (Lokomat) robotic gait training, and evaluate changes in seated balance control in people with high-thoracic motor-complete spinal cord injury (SCI). METHODS: Three individuals with motor-complete SCI from C7-T4, assumed to have no voluntary motor function below the chest, underwent robotic gait training. The participants were randomly assigned to Ekso-Lokomat-Ekso or Lokomat-Ekso-Lokomat for 10 sessions within each intervention phase for a total of 30 sessions. We evaluated static and dynamic balance control through analysis of center of pressure (COP) movements after each intervention phase. Surface electromyography was used to compare activity of the abdominal and erector spinae muscles during Ekso and Lokomat walking. RESULTS: We observed improved postural stability after training with Ekso compared to Lokomat during static balance tasks, indicated by reduced COP root mean square distance and ellipse area. In addition, Ekso training increased total distance of COP movements during a dynamic balance task. The trunk muscles showed increased activation during Ekso overground walking compared to Lokomat walking. CONCLUSIONS: Our findings suggest that the Ekso actively recruits trunk muscles through postural control mechanisms, which may lead to improved balance during sitting. Developing effective training strategies to reactivate the trunk muscles is important to facilitate independence during seated balance activity in people with SCI.
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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 | 0.002 |
| 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".