Measurement and Evaluation of Human Sitting and Standing Movement Biomechanics
Bibliographic record
Abstract
Although regenerative actuators can extend the operating durations of robotic lower-limb exoskeletons and prostheses, these energy-efficient devices have been exclusively designed and evaluated for level-ground walking. Building on previous research, this work analyzed the lower-limb joint biomechanical power during stand-to-sit movements using inverse dynamics modelling and simulation to estimate the biomechanical energy available for electrical regeneration. Nine subjects performed 20 sitting and standing movements while lower-limb kinematics and ground reaction forces were measured. Subject-specific body segment parameters were estimated using dynamic parameter identification, whereby differences in ground reaction forces and moments between the experimental measurements and inverse dynamic simulations were minimized. Joint biomechanical power was calculated from net joint torques and rotational velocities and numerically integrated over time to determine joint biomechanical energy. The hip generated the largest maximum negative biomechanical power (1.8 ± 0.5 W/kg), followed by the knee joint (0.8 ± 0.3 W/kg) and ankle joint (0.2 ± 0.1 W/kg). Negative biomechanical energy from the hip, knee, and ankle joints per stand-to-sit movement were 0.36 ± 0.06 J/kg, 0.16 ± 0.08 J/kg, and 0.03 ± 0.01 J/kg, respectively. Future research should focus on biomechanical modelling and movement tracking of seniors and rehabilitation patients to better estimate the joint biomechanical energy available for electrical regeneration with robotic lower-limb exoskeletons and prostheses.Reference:Laschowski B, Razavian RS, and McPhee J. (2020). Simulation of Stand-to-Sit Biomechanics for Designing Lower-Limb Exoskeletons and Prostheses with Energy Regeneration. Under Review.
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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 | 0.001 |
| 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) | 0.002 | 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 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".