Predicted Threshold for Seated Stability: Estimation of Margin of Stability Using Wearable Inertial Sensors
Bibliographic record
Abstract
Individuals with spinal cord injury suffer from seated instability due to impaired trunk neuromuscular function. Monitoring seated stability toward the development of closed-loop controlled neuroprosthetic technologies could be beneficial for restoring trunk stability during sitting in affected individuals. However, there is a lack of (1) a biomechanical characterization to quantify the relationship between the trunk kinematics and sitting balance; and (2) a validated wearable biomedical device for assessing dynamic sitting posture and fall-risk in real-time. This study aims to: (a) determine the limit of dynamic seated stability as a function of the trunk center of mass (COM) position and velocity relative to the base of support; (b) experimentally validate the predicted limit of stability using traditional motion capture; (c) compare the predicted limit of stability with that predicted in the literature for standing and walking; and (d) validate a wearable device for assessing dynamic seated stability and risk of loss of balance. First, we used a six-segment model of the seated human body for simulation. To obtain the limit of stability, we applied forward dynamics and optimization to obtain the maximum feasible initial velocities of the trunk COM that would bring the trunk COM position to the front-end of the base-of-support for a set of initial COM positions. Second, experimental data were obtained from fifteen able-bodied individuals who maintained sitting balance while base-of-support perturbations were applied with three different amplitudes. A motion capture system and four inertial measurement units (IMUs) were used to estimate the trunk COM motion states (i.e., trunk COM position and velocity). The margin of stability was calculated as the shortest distance of the instantaneous COM motion states to those obtained as the limit of stability in the state-space plane. All experimentally obtained trunk COM motion states fell within the limit of stability. A high correlation and small root-mean-square difference were observed between the estimated trunk COM states obtained by the motion capture system and IMUs. IMU-based wearable technology, along with the predicted limit of dynamic seated stability, can estimate the margin of stability during perturbed sitting. Therefore, it has the potential to monitor the seated stability of wheelchair users affected by trunk instability.
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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 | 0.003 | 0.001 |
| 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 | 0.000 |
| 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".