Hip, knee, and ankle kinematics of high range of motion activities of daily living
Bibliographic record
Abstract
Treatment of joint disease that results in limited flexion is often rejected by patients in non-Western cultures whose activities of daily living require a higher range of motion at the hip, knee, or ankle. However, limited information is available about the joint kinematics required for high range of motion activities, such as squatting, kneeling, and sitting cross-legged, making it difficult to design prosthetic implants that will meet the needs of these populations. Therefore, the objective of this work was to generate three-dimensional kinematics at the hip, knee, and ankle joints of Indian subjects while performing activities of daily living. Thirty healthy Indian subjects (average age: 48.2 +/- 7.6 years) were asked to perform six trials of the following activities: squatting, kneeling, and sitting cross-legged. Floating axis angles were calculated at the joints using the kinematic data collected by an electromagnetic motion tracking device with receivers located on the subject's foot, shank, thigh, and sacrum. A mean maximum flexion of 157 degrees +/- 6 degrees at the knee joint was required for squatting with heels up. Mean maximum hip flexion angles reached up to 95 degrees +/- 27 degrees for squatting with heels flat. The high standard deviation associated with this activity underscored the large range in maximum hip flexion angles required by different subjects. Mean ankle range of flexion reached 58 degrees +/- 14 degrees for the sitting cross-legged activity. The ranges of motion required to perform the activities studied are greater than that provided by most currently available joint prostheses, demonstrating the need for high range of motion implant design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".